memex: split AGENTS.md into monorepo overview and per-project workflow

Move the development workflow details (TDD, REPL, literate programming, branch policy) from top-level AGENTS.md into projects/AGENTS.md. Top-level AGENTS.md now describes only the monorepo structure and project list.
docs: consolidate neurosymbolic roadmap and design decisions, bump passepartout
2026-05-12 20:13:32 -04:00 · 2026-05-10 14:11:15 -04:00 · 2026-05-10 07:11:08 -04:00 · 2026-05-09 15:00:35 -04:00
8 changed files with 1420 additions and 1710 deletions
--- a/AGENTS.md
+++ b/AGENTS.md
@@ -1,77 +1,12 @@
 # AGENTS.md
-## Development Cycle (every change)
+This is the memex monorepo. It contains multiple Common Lisp projects, each
 in `projects/`. See `projects/AGENTS.md` for the general development workflow
 (ROADMAP-driven, TDD in REPL, literate programming, branch policy).
-1. **Think in org** — write your reasoning, goals, and approach in the .org file first
+## Project list
 2. **Write contract** — define a `** Contract` section listing each function's behavior:
   `(fn-name args)`: description. Returns/guarantees ...
 3. **TDD from contract** — each contract item becomes a `fiveam:test` in `* Test Suite`
   a. Write the test first → tangle → run → prove it FAILS (RED)
   b. Write the implementation → tangle → run → prove it PASSES (GREEN)
   c. Record both failure and success output
 4. **Reflect in org** — once tests pass, ensure the implementation is in the .org source
 5. **Update literate prose** — write/update the explanatory text around the code:
   what it does, why it exists, how it connects to the rest of the system
 6. **Commit** — only when asked. Ask first.
-## Commands
+| Project | Description | Runtime |
-
+|---------|-------------|---------|
-Tangle a single file:
+| passepartout | Neurosymbolic agent | `passepartout daemon` |
-  emacs --batch --eval "(progn (require 'org) (find-file \"org/FILE.org\") (org-babel-tangle) (kill-buffer))"
+| cl-tui | Reusable terminal UI framework | `sbcl` + `(ql:quickload :cl-tui)` |
 Validate structural integrity:
  emacs --batch -Q --eval '(progn (find-file "org/FILE.org") (check-parens) (kill-buffer))'
 Run tests:
  sbcl --noinform \
    --eval '(load (merge-pathnames "quicklisp/setup.lisp" (user-homedir-pathname)))' \
    --eval '(ql:quickload :passepartout :silent t)' \
    --eval '(load "lisp/FILE.lisp")' \
    --eval '(fiveam:run (intern "SUITE-NAME" :passepartout-TESTS))' --quit
 For error details: bind fiveam:*on-failure* to :debug
 ## REPL (port 9105) — preferred when available
 Start: `passepartout daemon`
 Send code:
  msg = '(:type :event :payload (:sensor :repl-eval :code "(+ 1 2)"))'
  s.sendall(f'{len(msg):06x}'.encode() + msg.encode())
 When REPL is up: TDD in-image first, then reflect to .org and tangle.
 When REPL is down: fall back to the SBCL cycle above.
 ## Rules
 - .org is source of truth; .lisp is generated — never edit .lisp directly
 - Every code change starts with a contract and a failing test
 - Prove RED before writing implementation
 - Validate before committing
 - If a tool fails, explain why and ask before trying alternatives
 - Before shipping a version, run the `** File Update Checklist` in `docs/ROADMAP.org`
 - **YOU MAY NOT** push a version tag (e.g., `v0.5.0`), create a GitHub release, or run `git push`
  that triggers CI/CD version workflows without explicit permission. Ask first.
 ## Core Boundary (HARD RULE)
 - **YOU MAY NOT add files to `passepartout.asd` `:components` without asking for permission.**
  ASDF `:components` is the core harness. Files there load on every daemon boot,
  cannot be hot-reloaded, and a bug there kills the agent's brainstem.
 - When you want to add a new module, **ask first**. Provide:
  1. Why it cannot be a skill (the self-repair criterion — can the agent fix it
     if corrupted without human help?) Demonstrate specifically how a broken
     version of this file prevents the agent from perceiving, reasoning,
     or acting — not just degrading performance or losing a feature.
  2. What it depends on and what depends on it
  3. Why it cannot use `fboundp` guards from core
 - **Default: everything is a skill.** Skills load via `skill-initialize-all`,
  are hot-reloadable, self-repairable, and a bug in a skill degrades the agent
  but doesn't kill it. The harness stays thin.
 - **The self-repair criterion**: a file belongs in core only if, when corrupted,
  the agent *cannot* fix it without human help. Corrupted core = dead brain,
  dead hands, or unreachable. Corrupted skill = degraded but self-repairable.
  This criterion is documented in `docs/ARCHITECTURE.org` and
  `docs/DESIGN_DECISIONS.org`.
--- a/notes/passepartout-SWOT.org
+++ b/notes/passepartout-SWOT.org
@@ -0,0 +1,868 @@
 #+TITLE: Passepartout Neurosymbolic + Agora Integration — SWOT Analysis
 #+AUTHOR: Agent
 #+FILETAGS: :notes:analysis:swot:passepartout:agora:neurosymbolic:
 #+CREATED: [2026-05-09 Sat]
 * Premise and Scope
 This analysis assumes the engineering is possible — Screamer can be wrapped,
 VivaceGraph can persist facts, ACL2 can verify structural properties, the
 archivist can extract triples from prose with Screamer verification, and the
 note-publishing bridge to Agora can be implemented. The question is not "can it
 be built?" but "does the architecture cohere? What does it enable? What does it
 miss?"
 * Will It Work Conceptually?
 The short answer: yes, within a specific domain. The long answer: the boundary of
 that domain is the most important thing to get right.
 ** The architecture's core insight is correct and load-bearing
 The central design decision — "the LLM proposes; the symbolic engine decides
 whether to accept" — is sound. It is the inverse of every existing agent
 architecture. Claude Code, OpenCode, Hermes — all of them put the LLM in the
 driver's seat and add safety as an afterthought (prompt-based guardrails that
 consume tokens and can be evaded). Passepartout inverts this: the LLM proposes
 actions and facts, but a deterministic layer of gates, constraint solvers, and
 formal verifiers decides what to admit and what to execute. This inversion is the
 correct response to the hallucination problem. You cannot eliminate hallucination
 by making the LLM better. You eliminate it by not asking the LLM to do things
 that require certainty.
 The bootstrap mechanism — extracting 50-70 entity classes mechanically from the
 existing Dispatcher gate stack with zero new code — is genuinely elegant. It
 proves the pattern at minimal cost: code becomes facts, facts enable reasoning.
 Every new gate pattern adds to the ontology organically. This is the right way to
 start a knowledge base: not by designing a schema upfront, but by formalizing what
 the system already knows implicitly.
 ** The "one memex, two indices" architecture survives contact with reality
 Option 4 (one memex with neural and symbolic indices over the same Org files) is
 the correct long-term architecture. The prose is the ground truth — always. The
 symbolic index is a derived view that can be thrown away and rebuilt. The neural
 index handles semantic search, associative leaps, and fuzzy matching. This
 division of labor is permanent, not transitional, because the domains they serve
 are fundamentally different kinds of knowledge.
 The practical path — starting with Option 5 (ephemeral facts, no persistence)
 through Phases 1-4, then graduating to Option 4 with VivaceGraph persistence in
 Phase 5 — is the right sequence. It punts the serialization format problem until
 the fact language has been battle-tested. It keeps the cost of mistakes low. It
 treats the ontology as something discovered through use rather than designed
 upfront.
 ** Wikipedia's ontology WOULD give it a running start — with caveats
 Wikidata contains approximately 100 million entities with a decade of human
 curation: type hierarchies, relations, dates, citations, disambiguation. For a
 personal memex that mentions Nabokov, /Pale Fire/, Kafka, postmodernism, and
 butterfly migration, the gate stack's 50-70 entity classes is starvation.
 Organic growth through prose extraction would take years to cover the entities in
 one person's engagement with a single novel.
 Loading Wikidata's entity graph into the symbolic index transforms the
 archivist's job from "discover that Nabokov wrote /Pale Fire/" to "connect your
 heading to Wikidata entity Q36591." The second task is reference resolution, not
 knowledge extraction — simpler, more reliable, and in many cases doable without
 an LLM at all (string match against loaded entities). The notes claim this
 collapses the LLM's role to three thin boundaries: input translation, prose-to-
 candidate-triple for personal content, and result-to-prose formatting.
 The caveats are real:
 - Entity resolution (matching prose mentions to Wikidata entities) is genuinely
  hard. "Nabokov" in a diary might refer to Vladimir Nabokov (Q36591), his son
  Dmitri (Q566744), or someone else entirely. Disambiguation requires context
  that the symbolic engine doesn't have without LLM assistance.
 - Wikidata is biased toward English Wikipedia's coverage. A memex in Arabic,
  Farsi, or Amharic will find far fewer resolved entities. The "universal" in
  Wikidata is aspirational, not actual.
 - Wikidata's property graph is not a ontology in the formal sense — it's a
  collaboratively edited dataset with contradictions, gaps, and editorial wars
  frozen in time. Loading it directly into a symbolic index that expects
  consistency (Screamer checks, cardinality policies) will surface thousands of
  contradictions on ingest, many of which are Wikidata artifacts, not meaningful
  tensions.
 - N-hop expansion is unbounded. One hop from Nabokov hits hundreds of entities
  (his works, his family, his influences, his translators). Two hops hits
  thousands. Three hops hits tens of thousands. The notes say "3-4 hops" for a
  literary memex but don't estimate the entity count this implies. The claim that
  5 million entities = ~400MB is the best-case hash-table figure; a graph with
  query indices will be larger, and Prolog-like queries over millions of nodes
  are not free.
 Still: even a partial Wikidata load with conservative hop limits would provide
 more ontology than the system could accumulate through years of organic growth.
 It is the right accelerator, and the architecture handles it correctly — Wikidata
 facts are admitted with =:provenance :wikidata= and =:policy :plural=, meaning
 they sit alongside personal facts without overriding them. Disagreements are
 surfaced, not resolved. The architecture treats Wikidata as evidence from an
 external source, not as ground truth. That's the correct posture.
 ** Cardinality policies are the right abstraction for contradiction
 The =:singular= / =:dual= / =:plural= cardinality model is one of the most
 important ideas in these notes. Classical logic requires consistency — a
 contradiction implies everything (ex contradictione quodlibet). A constraint
 solver like Screamer also requires consistency — a contradictory constraint set
 has no solutions. But a personal memex operates across domains where the meaning
 of contradiction is fundamentally different:
 - "rm -rf / is catastrophic" is =:singular= — there is one truth that evolves
  over time.
 - "I loved this person AND I resented them" is =:dual= — the tension IS the
  fact.
 - "Wikidata says Everest is 8848m; DBpedia says 8849m; my 2023 diary says
  8848m" is =:plural= — multiple sources disagree, and surfacing the disagreement
  with provenance is the product.
 This is a genuinely novel contribution to knowledge representation. Most
 knowledge graphs (Wikidata, Freebase, DBpedia) don't model contradiction at all —
 they pick one value and discard the rest. Most constraint solvers reject
 contradiction as error. Passepartout's cardinality model makes contradiction a
 first-class citizen: you can query the fact that "I used to believe X until
 Tuesday, then Y," or "these three sources disagree on height," or "I hold these
 two positions in tension." The symbolic engine's job is not to decide which is
 right. It is to surface the tension with provenance.
 This alone, if implemented correctly, would be a category-level advance over
 every existing personal knowledge management tool.
 ** Ontology versioning is the right approach to the migration problem
 Every knowledge base eventually faces schema migration — you split =:secret-file=
 into =:crypto-secret= and =:plaintext-secret=, and now every deduction that
 crossed the old category boundary is suspect. The standard approach is batch
 UPDATE operations that overwrite the past. Passepartout's approach — the category
 hierarchy itself is a Merkle tree, every fact stores the =:ontology-version= at
 assertion time, category changes trigger re-verification rather than remapping —
 preserves all worldviews. You can query "what did I believe about secrets before
 I refined my security model?" This is not querying a fact. It is querying the
 history of your own thinking.
 This is the kind of capability that no existing tool provides, and it flows
 directly from the architecture. If the Merkle DAG infrastructure exists (it does,
 from v0.2.0), ontology versioning is ~40 lines on top of it. The conceptual
 design is sound. The engineering appears tractable.
 * SWOT Analysis
 ** Strengths
 *** Architectural inversion — proposer vs decider
 The LLM proposes. The symbolic engine decides. This is the inverse of every
 existing agent architecture, and it solves the hallucination problem at the
 architectural level rather than the prompt-engineering level. No amount of
 prompt refinement can make a probabilistic system deterministic. But a
 deterministic admission gate can make a probabilistic proposer safe.
 *** Unified container format (Org files)
 Org files serve as the container for human prose, Lisp source code, symbolic
 facts, and Agora Notes. One format, one toolchain, one Merkle tree, one version
 control system. If Passepartout stops existing, the data survives in plain text.
 This is the hardest commitment in the design and the most undervalued. Most agent
 architectures store memory in JSONL transcripts, vector databases, or proprietary
 formats — opaque to the human and dependent on the tool. Passepartout's memory
 IS the human's memory, in the human's format.
 *** Provenance as product
 Every fact carries =:grounding= (the specific Org heading), =:provenance= (who
 or what produced it), =:timestamp=, =:referenced-by=, =:contradicted-by=,
 =:superseded-by=. The =/audit= command renders the full provenance chain. In the
 broader memex, the value is not the verified fact ("this command is safe"). It
 is the provenance itself: "this claim originated in that diary entry, has been
 referenced 7 times across 4 projects, was contradicted 6 months later, and was
 revised 3 weeks after that." This is a memory prosthesis that makes your own mind
 legible to you.
 *** Gate-to-fact bootstrap — ontology from existing code
 The existing Dispatcher gate stack encodes an implicit ontology (categories of
 secrets, destructive commands, trusted domains, core files). The bootstrap
 extracts this mechanically — zero LLM tokens, zero human authoring, ~30 lines of
 Lisp. This proves the pattern and provides the seed ontology without any new
 infrastructure. Every new gate pattern added by the human (HITL approvals that
 become rules) extends the ontology automatically.
 *** Self-preservation architecture
 The Third Law implementation — quarantine on skill failure, degraded-mode
 signaling, resource monitoring, external watchdog, refusal to self-terminate —
 is individually small (~20-50 lines each) and collectively transforms
 self-preservation from a passive architectural property into an active behavior.
 The key insight: the biggest gap is not that these mechanisms are hard. It is
 that degradation is currently silent. Making it visible is cheap and high-impact.
 *** Cardinality policies as a solution to contradiction
 The =:singular= / =:dual= / =:plural= model is novel in knowledge representation
 and directly addresses the hardest problem in a personal memex: that
 contradiction is the product, not the error. Bayesian knowledge bases, graph
 databases, and triple stores all struggle with contradiction. Passepartout's
 model makes it a feature.
 *** Organic ontology growth
 Categories emerge from the system's own operation: gate patterns → gate outcomes
 → Screamer generalizations → archivist proposals → cross-domain overlap
 detection. The ontology is a garden, not a building. This avoids the Principia
 Mathematica problem — the need to define everything upfront — by replacing
 axiomatic design with evolutionary growth. Categories that aren't used fade.
 Categories that are contradictory are pruned. Categories that emerge from
 overlapping domains are promoted. The system converges on useful granularity
 through use.
 *** Agora as provenance layer for networked knowledge
 A BFT-timestamped triple store is one approach, but the Merkle DAG + DID
 signatures provide a lighter-weight alternative: every fact's provenance is
 content-addressed, every author's identity is cryptographically verifiable, and
 the DAG structure enables partial replication without consensus. This is more
 tractable than full BFT and sufficient for a personal memex that needs to share
 facts across a network.
 *** Decoupling of compute cost from knowledge base size
 LLM tokens are minimized by design — deterministic gates cost 0 tokens, sparse-
 tree rendering keeps context at 2,000-4,000 tokens, Screamer deductions cost 0
 tokens. Adding 5 million Wikidata entities does not add a single token to any LLM
 call. The variables that actually degrade performance — context window size, LLM
 call frequency, Screamer deduction budget — are all bounded independently of
 knowledge base size. This is a structural property: the education is local, only
 the brain costs.
 ** Weaknesses
 *** The fact language is unproven and may be insufficient
 Triples — =(:entity :relation :value)= with provenance and grounding — is the
 current hypothesis. It is simple enough to be parseable, expressive enough to
 capture the gate stack's implicit claims, and extensible enough that Screamer can
 operate on it. But:
 - Triples cannot naturally express temporal relations. "Was X before Y?" requires
  reification (making the relation itself an entity), which makes queries
  exponentially more complex.
 - Triples cannot express modal claims. "Should not do X unless Y" has no natural
  triple representation. Neither does "could have done X but chose Y."
 - Triples cannot express counterfactuals. "If X had happened, Y would have
  followed." These are essential for the "what if" reasoning that a personal
  memex should support.
 - Triples struggle with n-ary relations. "Nabokov wrote Pale Fire in 1962 while
  living in Montreux" is a 4-ary relation (author, work, date, location), not a
  set of independent binary relations. Breaking it into triples loses the
  connection that binds them.
 - Triples cannot express negation cleanly. "Nabokov did NOT write Doctor Zhivago"
  requires a negative fact, which in a triple store with an open-world assumption
  means "not known" and "known not" are conflated.
 The notes acknowledge this limitation but defer it. The right granularity
 "depends on what queries the planner actually needs to make, and that cannot be
 known in advance." This is honest but unsatisfying. If triples prove insufficient,
 the entire fact store, the Screamer integration, the VivaceGraph persistence, and
 the archivist's extraction format must be redesigned. The architecture has no
 intermediate fallback between "triples" and "something more expressive."
 *** Screamer as admission gate is untested at this scale
 Screamer is a constraint solver with non-deterministic backtracking. Using it
 to check a candidate triple against an existing fact store is conceptually
 elegant: express the fact store as constraint variables, assert the candidate,
 check solvability. But:
 - Screamer was designed for constraint satisfaction problems with tens to
  hundreds of variables. A fact store with millions of triples (after Wikidata
  loading) is a constraint space orders of magnitude larger than Screamer's
  design envelope.
 - The consistency check is domain-scoped (only rules from the candidate's
  =:domain= apply), but cross-domain contradictions are the most valuable kind.
  "Nabokov was born in 1899" (literature domain) should be consistent with
  "Nabokov died in 1977" (history domain). If these are separate domains, the
  check misses contradictions; if they are unified, the constraint space
  explodes.
 - Screamer's non-deterministic backtracking is worst-case exponential. The notes
  bound this via deduction budget (=SCREAMER_DEDUCTION_BUDGET_MS=) but don't
  address the admission check itself, which runs on every assertion.
 There is a risk that Screamer works beautifully for the gate-bootstrapped seed
 (50-70 entity classes, ~200 facts) and becomes unusably slow after Wikidata
 loading (millions of facts). The transition from "works" to "doesn't" may be
 gradual and hard to detect — the system gets slower but doesn't crash,
 degrading user experience without a clear diagnostic.
 *** The "flip" from lossy to deterministic is underspecified
 The architecture's central narrative arc is the "flip": at some point, the non-
 lossy facts constitute a sufficient foundation that the symbolic engine can
 reverse the flow — instead of LLM extraction, the symbolic engine reads prose
 through its own lens and deduces facts directly. The sufficiency metric
 (non-lossy / total > 0.7) makes this "computable and visible to the user."
 But:
 - The threshold (0.7) is arbitrary. It is not derived from empirical measurement,
  information theory, or constraint satisfaction theory. It is a guess.
 - Sufficiency is domain-specific, not global. The gate stack may have 0.95
  coverage of security classifications but 0.05 coverage of literary analysis.
  A global threshold of 0.7 hides the domains where the symbolic engine is still
  effectively blind.
 - The "flip" operation itself is not defined. "Screamer reads prose through its
  own lens" — Screamer does not read prose. It operates on structured facts.
  Either the archivist still extracts triples (which is LLM work), or some new
  mechanism parses prose into triples deterministically (which is NLP at a level
  that does not exist in open-source Lisp).
 - Even after the flip, facts from the pre-flip period carry =:provenance
  :llm-proposed= and are therefore suspect. The pre-flip facts were admitted
  against fewer non-lossy facts, meaning Screamer's consistency checks were
  weaker. A fact admitted during the seed phase may be wrong but undetected
  because there were no contradicting facts at the time. Re-verifying all pre-
  flip facts against the current fact store is described as a heartbeat task but
  the cost (millions of Screamer checks) is not estimated.
 The flip is a beautiful narrative. It may also be a mirage — the system may
 achieve high sufficiency in narrow domains (security, filesystem, coding) and
 never approach it in the broader memex (literature, personal reflection, daily
 life). If the broader memex is the use case, the flip may never happen.
 *** The archivist's extraction cost is unaccounted
 The archivist calls the LLM to extract triples from prose, with "a minimal prompt
 (~200 tokens)." Over a personal memex with thousands of entries — a decade of
 diary entries, hundreds of literature notes, dozens of project logs — the
 extraction cost is substantial.
 Assume 5,000 headings, 200 tokens per heading prompt, and an LLM that returns
 ~100 tokens of structured triples per heading. That's 1.5 million tokens for the
 initial extraction, plus verification tokens (Screamer checks cost 0 LLM tokens,
 but incorrect proposals generate feedback that may trigger re-extraction). At
 current API prices (~$0.15 per million input tokens for GPT-4o-mini), the cost
 is modest (~$0.25). But at scale — re-extraction after ontology changes,
 continuous extraction as new content is added, extraction for all incoming Agora
 Notes — the cost accumulates.
 More importantly, the extraction latency is human-noticeable. 5,000 headings at
 1 second per LLM call is ~1.4 hours of extraction time. The system needs to
 either batch-extract on startup (making cold starts slow) or extract lazily on
 first query (making first queries slow). Neither is ideal.
 The notes trumpet the token savings from deterministic gates and Screamer
 deductions (valid — those cost 0 tokens) but the archivist's extraction cost is
 the system's single largest recurring LLM expense, and it is mentioned only in
 passing.
 *** The Agora integration is clean in theory, undefined in practice
 The "Passepartout IS the PDS" claim is elegant: the =memory-object= struct IS
 the Note format, the Merkle DAG IS the Key Event Log, the fact store IS the
 reputation system. But:
 - An Agora PDS needs to serve HTTP APIs for thin clients. The daemon speaks a
  framed TCP protocol over a local port. Extending it to serve HTTPS with
  DIDComm endpoints, subscription management, and Relay push/pull is a
  substantial engineering effort.
 - The PDS needs to manage encrypted storage — client-side encrypted content that
  the PDS itself cannot read. Passepartout's vault stores credentials with
  integrity hashes but does not currently manage per-Note encryption with
  audience-specific keys.
 - The Relay Network is described as an intelligent communication backbone with
  pub/sub routing. Passepartout has no Relay implementation, no Relay-facing API,
  and no subscription management beyond its own event orchestrator.
 - Agora's contract system (SCAL contracts, HODL invoices, arbitration tiers)
  requires state machines and Lightning Network integration that Passepartout
  has no primitives for.
 - The "Passepartout IS the PDS" vision conflates two things: the data model
  (Org files = Notes) and the infrastructure (a process that serves a network
  protocol). The data model unification is clean and right. The infrastructure
  unification implies Passepartout grows from a local agent to a network server
  — a significant architectural expansion that the notes treat as a ~40-line
  utility.
 *** No adversarial model
 The notes describe layered authentication (crypto, sensory, deterministic,
 probabilistic) and type-level gates as structural safety. They do not describe
 an adversarial model:
 - What stops a malicious Agora Note from containing 100,000 triples that flood
  the fact store?
 - What stops a DID from publishing Notes that deliberately inject contradictions
  to force Screamer into exponential backtracking?
 - What stops a compromised sensor key from signing valid sensor data that is
  adversarially crafted (e.g., video frames designed to trigger specific vision
  model false positives)?
 - What stops a spam DID from creating millions of Personas and flooding the
  user's incoming Notes directory?
 The resource monitor (Phase 1a) handles storage pressure generically. The
 quarantine system handles individual DIDs flagged for spam. But none of these
 are adversary-aware — they react to symptoms (disk full, error rate high) rather
 than anticipating attack patterns. An adversarial model would identify these
 vectors and design mitigations specifically. The notes describe a system that
 works in a cooperative environment, not an adversarial one.
 *** The self-repair criterion creates a two-tier architecture
 The AGENTS.md rule — "default: everything is a skill" — means the symbolic
 engine (Screamer, VivaceGraph, fact store, archivist, ACL2, planner) is all
 skills, not core. This is correct for the self-repair criterion: a corrupted
 skill degrades the agent but doesn't kill it. A corrupted core file kills the
 brainstem.
 But it creates a tension: the symbolic engine IS the reasoning layer that would
 diagnose and repair a corrupted skill. If the fact store itself is corrupted
 (impossible facts, inconsistent cardinality, broken Merkle chains), the engine
 that detects corruption is the engine that is corrupted. The system needs a
 "repair from below" path — a minimal core that can purge and rebuild the symbolic
 index without depending on the symbolic index. This path exists (the fact store
 is ephemeral in Phase 1-4 and rebuildable from prose in Phase 5+) but is not
 exercised automatically. A corruption in the symbolic engine requires human
 detection and manual rebuild — the exact problem the self-repair criterion was
 designed to avoid.
 ** Opportunities
 *** A memory prosthesis that makes your own mind legible
 The symbolic index, when populated and queried, answers questions that no
 existing tool can:
 - "What did I believe about monorepos in 2023, and how has that changed?"
 - "Which of my diary entries contradict each other?"
 - "What entities in my memex have no connection to any other entity?"
 - "Show me everything I've written about Nabokov, organized by when I wrote it,
  what I was reading at the time, and what I concluded."
 - "Which of my project plans reference security assumptions that I later changed?"
 - "What did I think about this topic, and why did I change my mind?"
 These are not information retrieval queries. They are self-knowledge queries.
 They require provenance chains, temporal versioning, contradiction surfacing, and
 cross-domain linkage — all of which the architecture provides as first-class
 capabilities. If this works, it transforms the memex from a searchable archive
 into a thinking partner that knows the history of your thoughts.
 *** Deterministic reasoning as a moat
 Every competitor agent system (Claude Code, OpenCode, OpenClaw, Hermes, Cognee,
 Mem0) uses neural-only reasoning. They are all vulnerable to the same failure
 mode: the LLM hallucinates a fact or an action, and there is no second system to
 catch it. Their safety is heuristic. Their memory is flat. Their reasoning is
 unprovable.
 Passepartout's architectural bet — a symbolic engine that verifies, deduces, and
 audits — creates a category difference, not a performance difference. If the bet
 pays off, Passepartout is not "a better AI agent." It is a different kind of
 system — one whose reasoning is provable, whose memory is content-addressed, and
 whose knowledge accumulates through deduction rather than re-prompting.
 This is a genuine moat. It cannot be replicated by adding a better system prompt
 or a larger context window. It requires building the ontology, the constraint
 solver, the fact store, and the provenance tracker — work that takes years and
 cannot be shortcut by spending more on inference.
 *** Agora as the first sovereign agent network
 If Passepartout serves as the PDS and an Agora Persona, then AI agents can:
 - Publish verified outputs as signed Notes with cryptographic provenance.
  Readers know the agent produced the output, not a human impersonating the
  agent.
 - Accept invocation Notes from other persona owners. "Please analyze this
  contract and publish your findings." The agent receives the request as an
  Agora Note, processes it, signs the response, and publishes it.
 - Build reputation through auditable chains of signed work products, not through
  self-reported claims.
 - Participate in the compute marketplace as both consumer and provider.
 - Maintain sovereign identity — the agent's DID is independent of any platform,
  any provider, any human account.
 This is not a chatbot on a messaging platform. It is an autonomous entity on a
 decentralized network, with cryptographic identity, verifiable provenance, and
 economic agency. If Agora reaches even Order 1 (the first 1,000 users),
 Passepartout agents become some of the most capable participants on the network.
 *** The 10-80-10 ratio for coding is genuinely achievable
 For a coding agent — the domain that Passepartout currently operates in — the
 10-80-10 ratio is plausible. The existing Dispatcher already verifies every
 action deterministically. Adding Screamer for consistency checking, VivaceGraph
 for dependency queries, and ACL2 for structural verification would shift the
 ratio from the current ~95-5-0 (neural-gate-symbolic) toward 50-40-10 in the
 near term and potentially 10-80-10 in the long term.
 The bootstrapped gate facts already cover file classifications, command safety,
 path protections, and tool permissions — the core categories for a coding agent.
 The archivist's extraction from project files would add dependency information,
 test coverage, and code structure facts. The planner could reason about
 refactoring order, dependency chains, and safety constraints deterministically.
 This is the domain where the symbolic engine provides the most immediate value,
 and it is the domain Passepartout already operates in.
 *** Wikidata as an entity backbone unlocks cross-domain reasoning
 Without Wikidata, the symbolic index for a general-knowledge memex is a sparse
 set of personal facts with no connecting structure. With Wikidata, the entity
 graph is pre-structured. The system can answer:
 - "What does my memex say about Nabokov that Wikidata doesn't?"
 - "Where does my memex disagree with Wikidata?"
 - "What entities in my memex have no Wikidata counterpart?" (These are the
  personal, novel, or subjective entities that are the most valuable.)
 - "Show me the intersection of my literary interests (from diary) with Wikidata's
  influence graph — which authors I read influenced each other in ways I haven't
  written about?"
 These are cross-domain queries that require both the personal memex (for what
 the user knows) and Wikidata (for what the world knows). Neither alone can
 answer them. Together, they enable a kind of knowledge synthesis that no existing
 tool provides.
 *** Ontology versioning enables "what-if" reasoning about one's own thinking
 The ability to query across worldviews — "what did I believe before I changed my
 security model?" — is a capability that has no analog in any existing tool. It
 transforms the memex from a static archive into a dynamic record of intellectual
 evolution. Combined with the temporal awareness system (Phase 0c), the system
 could surface correlations: "You changed your mind about monorepos two weeks
 after reading this article, which you bookmarked on this date, and one week
 before starting this project that uses a monorepo structure." The provenance
 chain IS the narrative of your thinking.
 *** Contract-level pre-arbitration reduces the cost of decentralized commerce
 Agora's Tier 0 Arbitrator — a local AI that provides evidence summaries before
 human arbitration — is a genuinely useful role for a neurosymbolic system.
 - "Contract CID X references arbitrator DID Y. DID Y is active. Verified."
 - "All parties have signed. The HODL invoice is locked. Verified."
 - "The buyer's claim of non-delivery is supported by 3 signed messages with
  timestamps after the delivery deadline."
 - "The seller's proof-of-delivery field is empty. No QR scan recorded."
 Each check is a Screamer query against the contract-lifecycle domain. The results
 are a plist, not a ruling. Both parties see the same evidence summary before
 escalating. This makes Level 1 arbitration faster (arbitrators receive
 pre-processed evidence bundles), cheaper (no human time spent on trivial
 verification), and more transparent (both parties see the same machine-generated
 summary).
 This is not AI judging. This is AI preparing the docket. The distinction is
 important and defensible.
 *** Self-auditing agents could transform AI safety discourse
 If Passepartout can answer =/audit= for any action or fact — showing the full
 provenance chain, every gate that approved it, every fact that supported it,
 every alternative that was considered — then AI safety moves from "trust us, we
 tested it" to "here is the audit trail, verify it yourself."
 This is the transparency that every AI safety framework calls for and none
 delivers. It is possible because the architecture records provenance as a
 first-class operation, not as an after-the-fact log. The provenance is the
 operating system, not a logging layer.
 *** The memex + Agora combination could be a new kind of social network
 Current social networks (Twitter, Facebook, Reddit) separate the person from
 their knowledge. You are a profile with posts. Your posts are isolated units
 without connection to your broader intellectual life.
 A Passepartout-powered Agora Persona would publish Notes that are grounded in
 the memex: "Here is my analysis of /Pale Fire/, drawn from diary entries across
 three years, annotated with Wikidata context, and verified against my existing
 literary framework." The Note is cryptographically signed, carrying provenance
 back to the specific Org headings that informed it. Readers see not just the
 conclusion but the intellectual scaffolding that produced it.
 This is not a "post." It is a publication — a knowledge artifact with verifiable
 provenance, auditable reasoning, and cryptographic identity. If this becomes the
 norm, it raises the standard for public discourse from "this is my opinion" to
 "this is my opinion, here is the evidence, here is how it evolved, here is who
 verified it."
 ** Threats
 *** The ontology problem may be harder than anticipated
 The notes are honest about this: "Whitehead's Principia Mathematica took over
 300 pages to define the logical foundations before it could prove that 1+1=2."
 Passepartout's domain is narrower (coding + personal knowledge) but the
 ontology problem is the same category of problem. Every entity class must be
 defined. Every relation must have clear semantics. Every inference rule must be
 justified.
 The gate-to-fact bootstrap provides 50-70 entity classes — enough for a coding
 agent. But the broader memex contains orders of magnitude more entity types:
 people, places, works, concepts, events, emotions, aesthetic judgments,
 professional skills, personal projects, temporal patterns. Defining these as
 triples with clear semantics is genuine intellectual work that no amount of
 engineering can shortcut.
 The risk is not that it's impossible. It's that it's slow — slow enough that
 the system never achieves the density of facts needed for the "flip" in the
 broader memex. The coding domain may reach sufficiency in months. The literary
 domain may take years. The daily-reflection domain may never cross the
 threshold because the facts involved (mood, insight, aesthetic experience) are
 not formalizable as triples.
 *** Screamer may not scale to the fact store size
 The constraint satisfaction approach to consistency checking is elegant for a
 seed fact set of hundreds of triples. It is unproven for millions of triples
 (after Wikidata loading + years of personal extraction). The domain-scoping
 strategy (Screamer only checks facts from the candidate's =:domain=) bounds the
 constraint space, but the most valuable consistency checks are cross-domain:
 - "You classified this file as public in your project notes but the gate stack
  classifies it as secret." (project domain vs security domain)
 - "You wrote that Nabokov influenced Kafka, but Wikidata says Kafka died before
  Nabokov published his first novel." (literature domain vs Wikidata domain)
 - "You planned to use this dependency, but the dependency's license changed in
  a way that conflicts with your project's license." (project domain vs legal
  domain)
 If cross-domain checks are disabled for performance, the most valuable
 contradictions are never detected. If they are enabled, the constraint space
 explodes. There is no obvious sweet spot.
 *** Wikidata quality may undermine trust in the symbolic index
 If Wikidata facts are admitted with =:policy :plural= and the user sees
 thousands of contradictions between Wikidata and their personal memex, the
 symbolic index may feel less trustworthy, not more. "Wikidata says Mount Everest
 is 8848m. DBpedia says 8849m. Your 2023 diary says 8848m. These three sources
 disagree on height." This is correct behavior — surfacing disagreement with
 provenance — but it may be overwhelming. The user wanted a knowledge base, not
 a disagreement engine.
 The trust problem is compounded by Wikidata's editorial biases. Wikidata
 reflects the biases of Wikipedia editors: English-language dominance, Western
 epistemological frameworks, systemic underrepresentation of non-Western
 knowledge. A memex in Arabic that references Islamic philosophy, Egyptian
 history, or African literature will find Wikidata's coverage thin, biased, or
 absent. The symbolic index would dutifully surface these gaps — "your memex
 mentions 47 entities with no Wikidata counterpart" — but it cannot fill them.
 *** LLM cost and latency may prevent the archivist from keeping up
 If the user writes a diary entry every day, the archivist must extract triples
 from each new heading. If the extraction takes 1-3 seconds per heading, it's
 background noise. But if the user imports 500 old diary entries, or the
 archivist needs to re-extract after an ontology change, or Agora Notes arrive in
 bulk from multiple follows, the extraction queue grows faster than it drains.
 The notes describe extraction as a background task triggered by heartbeat, but
 they don't specify the extraction rate limit. An unbounded queue with no rate
 limit would consume the LLM budget. A bounded queue would fall behind. A lazy
 extraction strategy (extract on first query) would make first queries slow.
 A batch extraction on startup would make cold starts slow.
 The archivist's throughput is gated by LLM API rate limits, token costs, and
 inference latency. These are external constraints that the architecture cannot
 eliminate. The symbolic engine can reduce LLM calls for reasoning; it cannot
 reduce LLM calls for extraction from prose.
 *** Agora may never reach network effects
 Agora faces the cold start problem that every decentralized social protocol
 faces: users won't join without content, creators won't post without users. The
 bootstrapping strategy (managed service → hybrid → full decentralization,
 targeting niche communities first) is well-articulated but its success depends
 on execution in a market where Mastodon, Bluesky, Nostr, and Farcaster are
 already competing for the same users.
 If Agora doesn't reach even Order 1 (1,000 users), the PDS integration is
 academic. Passepartout's DID identity, DIDComm gateway, Note signing, and
 contract verification are all infrastructure for a network that doesn't exist.
 The symbolic engine still works locally — provenance tracking, contradiction
 surfacing, and deduction are all valuable without Agora. But the network effects
 that make Agora a transformative platform — reputation, contracts, marketplaces,
 collective governance — require a living network.
 The risk is asymmetric: Passepartout invests significant engineering in Agora
 integration that provides zero value if Agora fails to launch.
 *** Complexity may prevent adoption
 Passepartout is already a complex system: a Lisp daemon, a terminal UI, a skill
 engine, a gate stack, multiple LLM backends, a Merkle memory system, and an
 event orchestrator. Adding a fact store, a constraint solver, a graph database,
 a theorem prover, an archivist, a planner, and an Agora PDS makes it more
 complex, not less.
 The target user — someone who wants a personal AI assistant that works offline —
 may not want or need any of this. They want the TUI to work, the LLM to be fast,
 and the files to stay safe. The neurosymbolic engine is infrastructure for a use
 case (lifelong personal knowledge management with verifiable provenance) that
 most users do not yet know they have.
 The risk is that Passepartout builds a cathedral for a congregation of one — a
 system that is architecturally brilliant and practically unused because the
 complexity-to-value ratio is too high for anyone except the author.
 *** The self-repair criterion may not hold under adversarial conditions
 The architecture assumes that skills can fail gracefully (fboundp guards, hash
 table fallbacks, degraded mode). It does not assume that a skill can be
 adversarially corrupted to behave correctly while producing wrong results. A
 compromised archivist that extracts plausible but false triples, a compromised
 Screamer that passes all consistency checks, a compromised VivaceGraph that
 returns query results from a parallel graph — these are "living" skills that
 would pass integrity checks and still poison the symbolic index.
 The type-level gates prevent the LLM from modifying gate code. They do not
 prevent a compromised skill (loaded by a trusted human, or corrupted on disk by
 a separate process) from operating normally while subtly wrong. The integrity
 monitoring (Phase 0) catches disk-level corruption through hash checks. It does
 not catch semantic corruption — a skill that is byte-for-byte identical to the
 known-good version but loaded with a malicious input that triggers a latent bug.
 This is not a vulnerability unique to Passepartout. It is a vulnerability in
 every system where components trust each other. But Passepartout's architecture
 amplifies the risk because the symbolic engine is supposed to be the trustworthy
 layer — the component that verifies the LLM's output. If the symbolic engine
 itself is compromised, the system has no higher court of appeal.
 *** The 10-80-10 ratio may create false confidence
 If the sufficiency metric shows "71% non-lossy, threshold 70%, mode: AUTO-
 EXTRACTION," the user may assume the system is trustworthy. But sufficiency is
 global — it aggregates across all domains. The system may have 95% sufficiency
 in the security domain and 5% sufficiency in the literary domain, averaging to
 71%. The auto-extraction switch would bypass the LLM for all categories with
 sufficient coverage, but the threshold is global, not per-domain. A literary
 query would hit the symbolic index that has "sufficient" coverage globally but
 insufficient coverage for literature.
 The notes describe domain-scoped Screamer checks but not domain-scoped
 sufficiency. A global sufficiency metric that triggers a global extraction mode
 change is the wrong granularity. Per-domain sufficiency, with per-domain
 extraction mode, would be more complex but more honest. The architecture as
 described has the simpler, more dangerous version.
 ** Summary Matrix
 |           | Positive                                                                              | Negative                                                                              |
 |-----------+---------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------|
 | INTERNAL  | S: Architectural inversion, unified Org format, provenance as product,                | W: Unproven fact language, Screamer scale unverified, extraction cost hidden,         |
 |           | cardinality model, gate-to-fact bootstrap, self-preservation, organic ontology,       | flip underspecified, adversarial model absent, self-repair tension,                   |
 |           | Wikidata as accelerator, decoupled compute cost                                       | Agora integration scope undefined, per-domain sufficiency missing                     |
 |-----------+---------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------|
 | EXTERNAL  | O: Memory prosthesis, deterministic moat, sovereign agent network,                    | T: Ontology may be harder than expected, Screamer may not scale,                      |
 |           | 10-80-10 for coding achievable, Wikidata cross-domain queries,                        | Wikidata quality/trust, LLM extraction bottleneck, Agora network effects,             |
 |           | ontology versioning, contract pre-arbitration, self-auditing safety,                  | complexity-to-adoption ratio, adversarial semantic corruption,                        |
 |           | knowledge-based social network                                                        | false confidence from global sufficiency metric                                       |
 * What This Unlocks
 ** Technologically
 The neurosymbolic engine, if built, would be the first AI system where:
 1. *Reasoning is auditable.* Every conclusion carries a provenance chain back to
   its premises. The =/audit= command renders the full inference tree — every
   fact, every deduction, every gate outcome — in human-readable form.
 2. *Knowledge accumulates deterministically.* Screamer deductions and gate
   outcomes generate new facts without any LLM involvement. The knowledge base
   grows from the system's own operation, not from re-prompting the LLM.
 3. *Memory is content-addressed.* Every fact is a Merkle node. Every version
   chain is tamper-proof. Rollback is atomic. The storage format is proven
   correct before it is committed to disk.
 4. *Safety is provable, not empirical.* Type-level gates make self-modification
   structurally impossible. ACL2 proves that the rule set has no contradictions.
   The dispatcher doesn't "try" to be safe — it is safe by construction.
 5. *The human and the machine share the same format.* Org files for both. No
   hidden database. No import/export step. The agent's memory IS the human's
   memory.
 These five properties, together, define a new category of AI system: the
 *sovereign reasoning agent*. Not sovereign in the blockchain sense (decentralized
 by consensus), but sovereign in the personal sense: the agent runs on your
 hardware, reasons with your knowledge, and proves its reasoning to you.
 ** Socially
 If the technical vision succeeds and Agora reaches network effects, the
 combination unlocks:
 1. *Verifiable public discourse.* Every published claim carries provenance back
   to source material. "I read this, I thought this, I changed my mind on this
   date, here is the evidence." Public discourse shifts from "competing opinions"
   to "competing evidence chains." The quality floor rises because claims without
   provenance are visibly weaker than claims with provenance.
 2. *Sovereign AI agents with legal and economic personhood.* A Passepartout
   agent with an Agora Persona can own assets, enter contracts, earn reputation,
   and face consequences for failure. This is not a chatbot. It is an autonomous
   entity with cryptographic identity, verified provenance, and economic agency
   — more like a corporation than a tool.
 3. *Self-auditing AI safety.* Every action the agent takes is traceable. Every
   gate decision is recorded. Every fact that informed a decision is queryable.
   AI safety moves from "trust us" to "here is the audit trail." This is the
   transparency that every AI ethics framework calls for.
 4. *A personal knowledge economy.* If your memex can publish Notes as Agora
   content, your intellectual work — your analyses, your syntheses, your
   discoveries — becomes a publishable, attributable, monetizable asset. Not
   through advertising or subscriptions, but through direct value exchange:
   Lightning payments for content access, contract work for your verified
   expertise, reputation that follows your Persona across platforms.
 5. *Collective intelligence without centralized control.* If multiple
   Passepartout agents share facts through Agora Notes, the collective symbolic
   index represents the verified, provenanced knowledge of a community — not the
   averaged opinion of a crowd, but the auditable intersection of independently
   verified claims. This is Wikipedia without the editorial board, science
   without the journal gatekeepers, journalism without the corporate owners.
 6. *A memory prosthesis that outlives the individual.* A memex with a decade of
   diary entries, linked to Wikidata's entity graph, with Screamer deductions
   surfacing patterns and contradictions, with ontology versioning preserving
   intellectual evolution — this is not a knowledge management tool. It is an
   externalized, queryable, auditable record of a life's thinking. It is what
   Vannevar Bush imagined in 1945: "an enlarged intimate supplement to one's
   memory."
 * Conclusion
 The architecture described in these notes is genuinely novel. Not incrementally
 novel — most agent architectures are variations on "LLM + tools + prompt-based
 safety." Passepartout's neurosymbolic vision is categorically different: an
 inversion where the deterministic layer judges the probabilistic layer, where
 facts carry provenance chains, where contradiction is a feature rather than an
 error, and where the user's Org files are the single source of truth for both
 human and machine.
 The largest risk is not that the architecture is wrong. It is that the ontology
 problem — the genuine difficulty of defining what a "fact" is, what relations
 are, what categories are useful, and how they evolve — is harder than the notes
 anticipate, and that the system spends years in a partially-working state where
 the symbolic index is too sparse to be useful but too entangled to be discarded.
 The second-largest risk is that Agora never reaches the network effects needed
 to make the PDS integration valuable beyond a local experiment, and that the
 engineering investment in DIDComm gateways, Note signing, contract verification,
 and Relay integration produces infrastructure for a network that doesn't exist.
 The opportunity is equally large: a system that makes your own mind legible to
 you, that proves its reasoning rather than asserting it, that accumulates
 knowledge across sessions through deduction rather than re-prompting, and that
 publishes verified, provenanced knowledge to a decentralized network. If this
 works — even partially, even slowly — it is a category-level advance over every
 existing agent architecture and every existing personal knowledge management
 tool.
 The notes are a map of territory that no one has walked. The territory is real.
 The map is detailed enough to navigate by. Whether the journey completes depends
 on whether the ontology problem yields to engineering, and whether the user —
 the one human whose memex this serves — finds value in the partial system well
 before the full vision materializes.
--- a/notes/passepartout-agora.org
+++ b/notes/passepartout-agora.org
@@ -0,0 +1,314 @@
 #+TITLE: Passepartout-Agora Integration — Unified Container Format
 #+AUTHOR: Agent
 #+FILETAGS: :notes:integration:agora:passepartout:design:
 #+CREATED: [2026-05-08 Fri]
 * Summary
 Org files and Agora Notes are the same container. Both are text with headers,
 tags, properties, and prose body. Both contain zero or more symbolic facts
 extractable by Passepartout's archivist. The only difference is that an Agora
 Note carries a DID signature and a CID for cryptographic provenance on the
 network. An Org file without a signature is a local Note. A signed Org file
 pushed to the PDS is an Agora Note.
 Passepartout's =memory-object= struct serves as the storage format for both.
 The archivist extracts facts from one unified store. Authorship is distinguished
 by provenance, not location.
 * The Unification
 ** Org files and Notes are the same container
 | Property         | Org file (local)             | Agora Note (network)               |
 |------------------+------------------------------+-------------------------------------|
 | Format           | Org-mode text                | Org-mode text                       |
 | Identity         | Merkle hash (=memory-object=) | CIDv1 (same hash)                   |
 | Contains facts   | Yes (archivist extracts)     | Yes (archivist extracts)            |
 | Author identity  | Implicit (file in =~/memex/=) | Explicit (DID signature in =proof=) |
 | Access control   | Filesystem permissions       | =access_control= flags              |
 | Routing          | N/A (local disk)             | =notify= + =references= + Relay     |
 | Ephemeral        | No                           | =ephemeral_duration=                |
 | Behavioral flag  | Implicit (convention)        | =is_feed= field                     |
 The structure converges in a single plist:
 #+begin_src lisp
 (:cid <merkle-hash>             ;; Identity across local and network
 :title <string>                ;; Org headline title
 :content <org-text>            ;; Full Org body (headings, prose, source blocks)
 :owner <did-or-nil>            ;; For Agora Notes: the signing Persona DID. nil for local
 :proof <plist-or-nil>          ;; ( :editor <did> :signature <bytes> )
 ;; Agora behavioral flags (nil for local files)
 :is-feed <boolean-or-nil>
 :access-control <did-list-or-nil>
 :notify <did-list-or-nil>
 :references <cid-list-or-nil>
 :reply-to <cid-or-nil>
 :thread-root <cid-or-nil>
 :ephemeral-duration <integer-or-nil>
 ;; Passepartout metadata
 :created-at <timestamp>
 :tags <string-list>            ;; Org tags
 :properties <plist>            ;; Org property drawer
 :extracted-facts <fact-list>)  ;; Populated by archivist after extraction
 #+end_src
 ** Facts are extracted from both, identically
 An Org file in =~/memex/literature/pale-fire.org= and an Agora Note from
 =did:agora:heather= with =:references <post-CID>= both contain prose. The
 archivist scans both, proposes triples via the LLM, verifies via Screamer,
 and admits facts to the symbolic index. The facts carry different provenance:
 #+begin_src lisp
 ;; Extracted from local Org file
 (:entity :pale-fire :relation :theme :value :unreliable-narration
 :provenance :local-prose :grounding "heading-42")
 ;; Extracted from Agora Note
 (:entity :kafka :relation :influence :value :nabokov
 :provenance :agora-note :grounding <incoming-note-cid> :author "did:agora:heather")
 #+end_src
 No new extraction path. The archivist already walks containers and extracts
 facts. The container type determines the provenance tag and the grounding
 identifier (local heading ID vs. Note CID).
 ** The memex distinguishes provenance by location, not format
 Incoming Agora Notes arrive at =~/memex/social/notes/<did>/<cid>.org=.
 The directory structure encodes authorship:
 | Path                                              | Meaning                            |
 |---------------------------------------------------+------------------------------------|
 | ~/memex/daily/                                    | Local diary entries                |
 | ~/memex/projects/                                 | Local project files                |
 | ~/memex/literature/                               | Local reading notes                |
 | ~/memex/notes/                                    | Local design and thinking notes    |
 | ~/memex/social/notes/<did>/<cid>.org              | Incoming Notes from other DIDs     |
 | ~/memex/social/outbox/<cid>.org                   | Outgoing Notes signed by the user  |
 The archivist scans all directories. Local files produce facts with
 =:provenance :local-prose=. Agora files produce facts with =:provenance
 :agora-note= + =:author <did>=. The symbolic index maps the provenance
 to the cardinality policy: local prose is =:plural= (the human's own notes —
 multiple interpretations coexist). Agora Notes are =:plural= by default (the
 author's claim, not authoritative over local facts). Agora Notes can be promoted
 to =:singular= or =:dual= if they carry cryptographic proofs of specific claims.
 ** Publishing Org content as Agora Notes
 When the user wants to publish a diary entry, project log, or literary note as
 an Agora Note, the operation is:
 1. Select the Org heading or file.
 2. Compute the Merkle hash (=memory-object= hash → CIDv1).
 3. Sign with the user's Persona DID key (Phase 0b key registry).
 4. Set Agora flags: =:is-feed= t/nil, =:access-control= [], =:references= [previous-note-cid].
 5. Push to the PDS. The Note is an Org plist with a DID signature.
 6. The PDS stores and relays it. The Note remains in =~/memex/social/outbox/= with its CID.
 All of this is a single function: =(note-publish heading-id &key is-feed access-control references)=.
 ~40 lines, extending the vault (key signing), the fact store (CID generation),
 and the memex (output directory).
 * Implications for Passepartout's Architecture
 ** The symbolic index now has a second ingestion path
 Facts enter through three gates:
 1. Gate outcomes (bootstrap + runtime, =:provenance :gate-outcome=)
 2. Screamer deductions (=:provenance :deduced=)
 3. Archivist extraction (=:provenance :local-prose= or =:provenance :agora-note=)
 The third path now covers both local Org files and incoming Agora Notes. No new
 path needed. The archivist gains no new code — only a new directory to walk
 (=~/memex/social/notes/=) and a new provenance tag to assign.
 ** Authentication Layer 1 now has Agora-native verification
 Phase 0b's cryptographic gate (vector 0) verifies DID signatures. An incoming
 Agora Note carries =:owner <did>= and =:proof.signature <bytes>=. Gate vector 0
 verifies the signature against the DID's public key (from the key registry, which
 is now also an Agora DID registry). Verification is identical for local signals
 and Agora signals — the same gate, the same key lookup.
 ** Self-preservation gains an Agora dimension
 The resource monitor (Phase 1a) tracks =~/memex/social/= as a source of storage
 growth. Incoming Notes from network sources are lower preservation priority than
 local prose — if disk pressure hits, incoming Agora Notes are evicted first
 (their source is the remote PDS; they can be re-fetched). Quarantine (Phase 1a)
 extends to Agora channels: if a DID is sending spam or malformed Notes, their
 incoming directory is quarantined and the DID is flagged for human review.
 ** Sufficiency tracks Agora as a provenance source
 The sufficiency score (Phase 4) gains a new provenance category:
 #+begin_example
 Symbolic Index
  Facts:    3,847
    Gate outcomes:       847  (22%)
    Deduced:             921  (24%)
    Human-authored:       72   (2%)
    Local prose:        1,247  (32%)
    Agora Notes:          760  (20%)
  ─────────────────────────
  Non-lossy:            1,840  (48%)
  LLM-proposed:         2,007  (52%)
 #+end_example
 Agora Notes are a provenance source, not a lossiness category. Facts from Agora
 Notes carry =:provenance :agora-note= — they are LLM-extracted (the archivist
 proposes them) but the source is cryptographically signed by a known DID. They
 are neither =:gate-outcome= (mechanical) nor =:llm-proposed= from local prose
 (uncertain source). They occupy a middle ground: verified source, uncertain
 extraction.
 * Implications for Agora
 ** Passepartout IS the PDS
 The TODO.org in =projects/agora/= already captures this: "Passepartout IS the
 PDS — the agent runs a personal data store in-process." With Org files as the
 Note format, this is literal. The PDS stores Org files. The agent reads them.
 The network accesses them via the PDS API. There is no separate PDS process.
 ** Level 0 pre-arbitration via Screamer
 Section 07 of the Agora requirements describes a "Tier 0 Arbitrator" — a local
 AI that provides a sanity check before human arbitration. Passepartout's
 Screamer + fact store provides this at zero LLM tokens when working from
 existing facts:
 - "Contract CID X references arbitrator DID Y. DID Y is active. Verified."
 - "All parties have signed. The HODL invoice is locked. Verified."
 - "The buyer's claim of non-delivery is supported by 3 signed messages with
  timestamps after the delivery deadline."
 - "The seller's proof-of-delivery field is empty. No QR scan recorded."
 Each check is a Screamer query against the contract-lifecycle domain. Results
 are a plist, not a ruling. Both parties see the same evidence summary before
 escalating to Level 1.
 ** Reputation as deduced facts
 Screamer deduces reputation from signed contract chains, not asserted claims:
 #+begin_src lisp
 (:entity "did:agora:heather" :relation :contract-reputation
 :value (:completed 47 :defaulted 0 :disputes 3 :won 3 :escalated 0)
 :provenance :deduced :derived-from (<list of 47 contract CIDs>))
 #+end_src
 This is the strong version of Agora's Trust Score. It's a fact deduced from
 cryptographic evidence, not a claim by the persona (self-reporting could be
 false) and not a claim by a centralized reputation service (could be bought).
 The deduction is auditable — `/audit did:agora:heather` shows every contract,
 every outcome, every ruling.
 ** Agent Behavioral Contracts — formal enforcement for the ABC of Agora
 Bhardwaj (2026) introduces a formal framework that brings Design-by-Contract
 principles to autonomous AI agents. An ABC contract =C = (P, I, G, R)=
 specifies /Preconditions/, /Invariants/ (hard and soft), /Governance/ policies
 (hard and soft), and /Recovery/ mechanisms as first-class runtime-enforceable
 components.
 This maps directly onto Agora's contract lifecycle:
 | ABC component          | Agora mapping                                                |
 |------------------------+--------------------------------------------------------------|
 | =P= (Preconditions)    | Contract Note validity checks: all signers' DIDs active,     |
 |                        | contract CID correctly referenced, HODL invoice locked        |
 | =I= (Invariants)       | Hard: payment amount unchanged, arbitrator DID unchanged.     |
 |                        | Soft: delivery within estimated window                        |
 | =G= (Governance)       | Hard: no party modifies contract terms unilaterally.          |
 |                        | Soft: parties communicate through designated channels         |
 | =R= (Recovery)         | Arbitration escalation, HODL invoice release, reputation      |
 |                        | deduction                                                    |
 The framework's key mathematical results have direct implications for Agora:
 - /Drift Bounds Theorem/: contracts with recovery rate γ > α (natural drift rate
  from LLM non-determinism in agent behavior) bound behavioral drift to D* = α/γ.
  For Agora, this means contract enforcement can be /predictive/ — detecting drift
  before violation — rather than just /corrective/ after breach.
 - /Compositionality Theorem/: sufficient conditions (interface compatibility,
  assumption discharge, governance consistency, recovery independence) under
  which individual contract guarantees compose end-to-end for multi-agent chains.
  This is essential for Agora's multi-party contracts, where a buyer, seller,
  arbitrator, and escrow agent form a chain of interdependent behavioral
  expectations.
 - /(p, δ, k)-satisfaction/: probabilistic compliance accounting for LLM
  non-determinism — contracts hold with probability p, deviations stay within
  tolerance δ, recovery within k steps. This formalizes what Screamer's
  contract-lifecycle domain queries verify: whether the current state of a
  contract satisfies its agreed-upon conditions, given the inherent uncertainty
  in any agent's behavior.
 The empirical results are significant: across 1,980 sessions on 7 models,
 contracted agents (with ABC enforcement) detected 5.2-6.8 soft violations per
 session that uncontracted agents missed entirely, with <10ms per-action overhead.
 Overhead is critical for Passepartout as the PDS — contract enforcement must not
 add latency to Note processing.
 ABC does not replace Screamer. ABC specifies /what/ must hold; Screamer verifies
 /whether/ it holds against the fact store. The contract-lifecycle domain already
 planned for Phase 0b (signal chain) can be implemented as an ABC-like structure:
 a tuple of preconditions, invariants, governance rules, and recovery mechanisms,
 each expressed as Screamer-verifiable facts with Merkle provenance.
 See also:
 - Bhardwaj, V.P. (2026). Agent Behavioral Contracts: Formal Specification and
  Runtime Enforcement for Reliable Autonomous AI Agents. arXiv:2602.22302.
 ** The merkle DAG IS the Key Event Log
 Agora's KEL specification (Section 02) describes an append-only log of key
 events — inception, rotation, revocation, follow events. Passepartout's Merkle
 DAG (Phase 5, built on v0.2.0 memory-object infrastructure) is this log. Each
 key event is a fact in the =:key-lifecycle= domain. Each event has a
 =:parent-id= chaining to the previous event. The DAG is content-addressed —
 every event is a CID. The full KEL is queryable: `/audit did:agora:heather`
 renders every key event, every follow event, every contract signature, with
 provenance chains.
 * Relation to the Neurosymbolic Roadmap
 The Agora integration is not a new phase. It is a consequence of decisions
 already made:
 | Roadmap item            | Agora consequence                                              |
 |-------------------------+----------------------------------------------------------------|
 | Phase 0b (key registry) | Key registry uses Agora DIDs. DID store is =:key-lifecycle= domain |
 | Phase 1 (fact store)    | Fact store is also Note store. Same API, same hash table       |
 | Phase 1a (self-pres.)   | Incoming Notes tracked. Spam DIDs quarantined. Disk eviction    |
 | Phase 3 (archivist)     | Archivist walks =~/memex/social/notes/= alongside local dirs    |
 | Phase 4 (sufficiency)   | Agora Notes are a provenance category in the sufficiency score  |
 | Phase 5 (Merkle DAG)    | DAG = KEL. DAG = contract audit trail                          |
 | Phase 0b (signal chain) | Signal chain = contract lifecycle chain. Same Merkle linking    |
 No new lines in the roadmap. The Note publishing function (~40 lines) is a
 utility, not a phase.
 * What Is NOT Built
 1. *A separate Note parser.* Agora Notes ARE Org files. The existing Org parser
   reads both.
 2. *A separate Note store.* The =memory-object= struct stores both. The
   =*memory-store*= hash table holds both.
 3. *A separate extraction path for Agora content.* The archivist extracts facts
   from prose regardless of origin. The provenance tag distinguishes source.
 4. *A new authentication mechanism for Agora signals.* Gate vector 0 verifies
   DID signatures. The key registry is the DID registry.
 See also:
 - =projects/agora/docs/= — Agora requirements (overview, identity, primitive, social, contracts, governance)
 - =projects/agora/TODO.org= — Passepartout integration track
 - =passepartout-neurosymbolic-design-decisions-and-options.org= — the full design rationale
 - =passepartout-neurosymbolic-roadmap.org= — the phased implementation plan
--- a/notes/passepartout-neurosymbolic-design-decisions-and-options.org
+++ b/notes/passepartout-neurosymbolic-design-decisions-and-options.org
@@ -1,719 +1,24 @@
-#+TITLE: Passepartout Neurosymbolic Engine — Design Decisions and Architecture Options
+#+TITLE: Passepartout Neurosymbolic Engine — Design Decisions — SUPERSEDED
 #+AUTHOR: Agent
-#+FILETAGS: :notes:design-decisions:neurosymbolic:architecture:v3.0.0:
+#+FILETAGS: :notes:design-decisions:neurosymbolic:superseded:
 #+CREATED: [2026-05-08 Fri]
-
+#+SUPERSEDED: [2026-05-10 Sun]
-* The Hallucination Problem — Why Neurosymbolic
+
-
+This document has been consolidated into ~passepartout/docs/DESIGN_DECISIONS.org~. The unified document interleaves the neurosymbolic design rationale into nine thematic parts with a single narrative arc:
-An LLM is a statistical engine trained on token sequences. It generates the most
+
-probable continuation of a prompt. Given sufficient context, that continuation is
+| Part | Topic | Key New Sections |
-correct. Given novel context, it is often wrong in confident-sounding ways.
+|------|-------|-----------------|
-
+| I   | Foundation | Historical Lineage (McCarthy) |
-This is not a training deficiency. Hallucination is a fundamental property of
+| II  | The Two Brains | Hallucination Problem, 10-80-10, Brain/Education metaphor |
-probabilistic inference. You can reduce it with better models, longer contexts,
+| III | Safety & Self-Preservation | Active Third Law, Layered Signal Authentication |
-and clever prompting, but you cannot eliminate it by making the LLM better. You
+| IV  | The Symbolic Engine | Five Options, Chosen Path, Gate-to-Fact Bootstrap, LLM as Proposer, Cardinality Policies, Organic Ontology, Ontology Versioning, Sufficiency Criterion, Merkle DAG, Abstract Fact Store Interface |
-eliminate it by not asking the LLM to do things that require certainty.
+| V   | Knowledge Sources | Semantic Wikipedia, MOMo Empirical Validation |
-
+| VI  | Implementation Properties | Performance Scaling, Provenance as Product |
-This is the architectural bet at the heart of Passepartout's neurosymbolic design.
+| VII | Engineering Infrastructure | REPL, Cybernetic Loop, Observability, Literate Programming, Eval Harness, MCP, Local-First, Token Economics, Time Awareness (carried over from existing) |
-The LLM should not be the reasoning engine. It should be the *creative* engine —
+| VIII | Validation | Marcus, CREST, KiL philosophical validation; Competitive Argument |
-proposing possibilities, surfacing connections, translating between natural
+| IX  | Open Questions | Fact language, human role, Wikidata scope, natural language interface, graph query performance |
-language and formal representation. The *reasoning* engine should be symbolic:
+
-deterministic, verification-grounded, provenance-tracked, and incapable of
+Cross-references are preserved in:
-hallucination by construction.
+- ~notes/passepartout-symbolic-engine-exploration.org~
-
+- ~notes/passepartout-whitehead.org~
-This is not a rejection of neural methods. It is a division of labor. The neuro
+- ~notes/competitive-landscape.org~
 is the brain — generative, associative, creative, comfortable with ambiguity. It
 produces hypotheses. The symbolic engine is the education — accumulated, verified,
 provenance-tracked knowledge that the brain draws on and is disciplined by. It
 doesn't think. It remembers, checks, and constrains.
 The brain is always smarter than the education, but the education prevents the
 brain from being confidently wrong.
 ** See also:
 - =passepartout/docs/DESIGN_DECISIONS.org=: "The Probabilistic-Deterministic Split"
  for the gate-level version of this argument.
 - =notes/passepartout-whitehead.org=: Whitehead's ramified theory of types as
  the structural guarantee against self-referential contradictions.
 - =notes/passepartout-symbolic-engine-exploration.org=: the full design space and
  the lossiness problem at the neural-symbolic boundary.
 * The Five Architecture Options
 The symbolic engine must relate to the human memex. The relationship is not
 obvious because knowledge lives in two incompatible forms: natural language
 prose (what the human reads and writes) and formal facts (what the symbolic
 engine reasons about). The translation between them is lossy by nature. The
 architecture is defined by how it handles that lossiness.
 =notes/passepartout-symbolic-engine-exploration.org= explores five options. They are
 summarized here to make subsequent decisions legible.
 ** Option 1: The Auto-Formalizer
 A separate knowledge graph stores symbolic facts. The LLM populates it by
 extracting triples from unstructured data — documentation, manuals, logs,
 session histories. The KG becomes co-authoritative with the human prose.
 This is the simplest to implement but inherits the dual-representation problem
 in its most acute form. The KG and the prose can disagree, and the architecture
 provides no mechanism for resolving disagreements. It also stores knowledge
 twice — once in the user's Org files, once in the KG — with no guarantee that
 they stay synchronized.
 ** Option 2: Two Intentionally Separate Memexes
 The human memex contains prose: thoughts, diaries, decisions, documentation.
 The symbolic memex contains formal facts: constraints, rules, relationships,
 deductions. The archivist bridges between them but does not try to keep them
 synchronized. They are allowed to diverge because they serve different purposes.
 The prose captures what the human intended. The symbolic memex captures what
 the symbolic engine has proven.
 This is philosophically honest — it admits that no lossless translation between
 natural language and formal logic is possible. But it forces the user to reason
 about two separate knowledge stores and understand when to trust each.
 ** Option 3: Tangled Fact Blocks in Org Files
 The tangle mechanism already handles the dual-representation problem for code.
 Lisp code lives in literate blocks within Org files (=#+begin_src lisp=). The
 tangle mechanism extracts these blocks and generates =.lisp= files. A new block
 type — =#+begin_src knowledge= — would contain symbolic facts in a formal
 language. The tangle mechanism would load these facts into the symbolic engine's
 in-memory store, just as it loads Lisp code into the SBCL image.
 This is aesthetically appealing because it unifies the format. One toolchain,
 one version control system, one Merkle tree. But the block language itself IS
 the knowledge representation language, and that language is the ontology we
 have not yet defined. The format is unified but the content is unspecified.
 ** Option 4: One Memex, Two Indices
 The prose remains in human language in Org files. The prose is always the ground
 truth. Two indices sit on top of the prose as derived views:
 - The *neural index* uses vector embeddings to enable semantic search. The LLM
  navigates the prose through embedding space, retrieving relevant headings.
 - The *symbolic index* stores formal assertions about what the prose says —
  predicates, relations, constraints — each grounded to a specific heading or
  block in the Org file.
 Each index serves its own side of the machine. They do not need to understand
 each other's representations. They only need to agree on which heading or block
 they are referring to. Because the prose is always the ground truth, the symbolic
 index can be thrown away and rebuilt from scratch if it becomes corrupted or
 stale. No information is lost — only the extracted assertions.
 ** Option 5: Ephemeral Symbolic Facts
 No persistence, no serialization format, no knowledge graph stored on disk.
 VivaceGraph exists in memory during the session. Screamer derives facts from the
 prose as needed. When the session ends, the facts are discarded and re-derived
 from the prose on the next start.
 This punts the ontological design problem entirely. You never have to decide on
 a serialization format because you never serialize. The cost is compute
 (re-derivation on every restart) and the inability to accumulate facts across
 sessions. But it is the correct first step — a way to learn what kinds of facts
 are actually useful before committing to a storage format.
 * The Chosen Path: Option 4, Starting with Option 5
 The one-memex-two-indices architecture (Option 4) is the correct long-term
 architecture. The prose is the ground truth. The symbolic index is a derived
 view that can be rebuilt. The neural index handles what the symbolic index
 cannot — semantic search, fuzzy matching, associative leaps.
 But committing to a persistence format before knowing what facts are useful
 is premature. The practical path starts with Option 5 (ephemeral facts) as the
 Phase 1-4 implementation, then graduates to Option 4 with VivaceGraph
 persistence in Phase 5 when the fact language has been battle-tested (=see
 =passepartout-neurosymbolic-roadmap.org=).
 ** Why the dual index is permanent, not transitional
 In the coding domain, there is an aspiration that the symbolic index could
 eventually capture enough of the prose's propositional content to become a
 complete representation — the "flip" described in the architecture note. But
 for the broader memex (literature, poetry, personal reflection, daily logs),
 completeness is neither possible nor desirable. You cannot formalize what makes
 a poem beautiful. You cannot extract a triple that captures the emotional weight
 of a diary entry. The neural index will always be the gateway to the full
 richness of the prose. The symbolic index handles what can be mechanically
 verified: citations, entities, temporal order, contradictions, provenance.
 The division of labor between the two indices is permanent because the domains
 they serve are fundamentally different kinds of knowledge.
 * The Neuro as Brain, the Symbolic as Education
 The original 10-80-10 architecture (10% neural, 80% symbolic, 10% neural)
 describes the target ratios for a *coding* agent — a domain where most reasoning
 is formalizable. For the broader memex, the ratios are different and less
 important than the metaphor itself.
 The neuro is the *brain* — generative, associative, creative, comfortable with
 ambiguity. It produces insights that are provisional, connections that are
 speculative, hypotheses that may be wrong. It is the driver.
 The symbolic engine is the *education* — accumulated, verified,
 provenance-tracked knowledge that the brain draws on and is disciplined by. It
 doesn't think creatively. It remembers, checks, and constrains. It prevents the
 brain from being confidently wrong.
 This framing resolves a tension in the original architecture. The 10-80-10
 implies the symbolic engine /replaces/ the neuro for reasoning. But a symbolic
 engine is terrible at creativity, ambiguity, and associative leaps across
 unrelated domains — exactly what you need for a memex that contains /Pale Fire/,
 a shopping list, and a project plan. The brain proposes that your sudden interest
 in unreliable narrators coincides with a week where your project retrospective
 used the word "deception." The education verifies: "those two diary entries are
 4 days apart; the word 'deception' appears in both; here are the headings." The
 brain makes the leap. The education makes it trustworthy.
 This means the symbolic engine never needs to be "complete." Education isn't
 complete knowledge — it's structured knowledge. You don't need a fact for every
 sentence in your diary. You need facts for what can be mechanically verified:
 dates, citations, entities, contradictions, temporal order. The brain handles
 the rest.
 * The Gate-to-Fact Bootstrap — Extracting the First Ontology from Existing Code
 The Dispatcher gate stack already encodes an implicit ontology. Every gate
 vector asserts the existence of a category of things:
 - Gate vector 2 asserts there exists a class of files called /secrets/.
 - Gate vector 7 asserts there exists a class of commands called /destructive/.
 - Gate vector 8 asserts there exists a class of domains called /trusted/.
 - The self-build boundary asserts there exists a class of files called
  /core-harness/ and a class called /skills/.
 These claims are currently expressed as code — Lisp functions that pattern-match
 against file paths, shell commands, and URLs. They are not facts the symbolic
 engine can query, derive from, or check for consistency. But they can be made
 explicit.
 The bootstrap makes every gate a set of initial symbolic facts:
 =(:file ".env" :member-of-class :secret-files :source gate-vector-2)=,
 =(:command "rm -rf /" :classified-as :catastrophic :source gate-vector-7)=,
 =(:domain "api.telegram.org" :classified-as :trusted :source gate-vector-8)=.
 This produces 50-70 entity classes directly from the existing gate stack,
 without any new infrastructure:
 | Source                                 | Count | Example categories                                 |
 |----------------------------------------+-------+----------------------------------------------------|
 | ~*dispatcher-protected-paths*~         | 11    | :secret-config-file, :ssh-key-file, :gpg-key-file  |
 | ~*dispatcher-shell-blocked*~           | 8     | :catastrophic-command, :injection-pattern           |
 | ~*dispatcher-network-whitelist*~       | 2     | :trusted-domain, :untrusted-domain                  |
 | Self-build boundary                    | 2     | :core-harness-file, :skill-file                    |
 | Privacy tags                           | 3     | :private-content, :financial-content                |
 | Permission table                       | 3     | :read-only-tool, :write-tool, :eval-tool            |
 | Cognitive tools                        | 6     | :code-search-tool, :file-io-tool, :shell-tool       |
 | Relations (all gates)                  | ~15   | :member-of-class, :classified-as, :depends-on        |
 | Qualities                              | ~8    | :catastrophic, :dangerous, :moderate, :harmless     |
 | Provenance sources                     | 4     | :gate-outcome, :human-authored, :deduced, :llm-proposed |
 |----------------------------------------+-------+----------------------------------------------------|
 This is the seed. It gives Screamer a domain to reason about immediately, without
 any LLM involvement. It proves the pattern — code becomes facts, facts enable
 reasoning — at the cost of approximately 30 lines of Lisp.
 * The LLM as Proposer — Verified Extraction
 The LLM cannot be trusted to populate the symbolic index directly. Its outputs are
 sampled, not proven. A probabilistic extraction feeding a deterministic engine
 defeats the purpose of being deterministic.
 But the LLM is still useful. It can surface facts that are obvious to a human
 reader of prose but would take the symbolic engine many deduction steps to reach
 independently. The solution is to demote the LLM from /extractor/ to /proposer/:
 1. The archivist reads a prose heading.
 2. The LLM proposes candidate triples.
 3. Screamer checks each triple for consistency against the existing fact store.
 4. Only consistent triples are admitted to the symbolic index, flagged with
   =:provenance :llm-proposed= and grounded to the source heading.
 The LLM might hallucinate facts that don't correspond to the prose. It might
 extract facts that contradict existing knowledge. It might produce syntactically
 malformed triples. None of these failures contaminate the symbolic index because
 proposals are not admitted automatically. The admission gate (Screamer) is
 deterministic.
 This is the core architecture pattern. Everything else — the entity classes, the
 deduction engine, the persistence layer — follows from this single design decision:
 *the LLM proposes; the symbolic engine decides whether to accept.*
 * Three Contradiction Policies — Domain-Dependent Consistency
 Classical logic requires consistency. A contradiction implies everything
 (=ex contradictione quodlibet=). Screamer, as a constraint solver, also requires
 consistency — a contradictory constraint set has no solutions. But the symbolic
 engine operates across domains where the meaning of contradiction is fundamentally
 different.
 A single architecture serves all domains by applying different contradiction
 policies, scoped to the entity class:
 ** Policy :exclusive — Contradiction Rejected at Admission
 For domains where the world is physically singular — a file either exists or it
 doesn't, a command either was blocked or it wasn't, a gate rule either applies or
 it doesn't. When a new fact contradicts an existing one in an :exclusive domain,
 the new fact is rejected. The existing fact is authoritative unless a human
 explicitly retracts it.
 Use for: security classifications, file system state, gate rules, code
 correctness, deterministic safety constraints.
 ** Policy :coexistent — Contradiction Flagged, Both Retained
 For domains where multiple truths coexist — literary interpretations, historical
 accounts, personal beliefs held at different times, multi-source factual
 disagreement (Wikidata vs. DBpedia vs. your memex). When a new fact contradicts
 an existing one in a :coexistent domain, the contradiction is recorded with a
 cross-reference flag. Both facts are stored. Queries return all facts with
 provenance display.
 Use for: literature, history, personal knowledge evolution, scientific consensus
 shift, multi-author knowledge bases.
 ** Policy :temporal — Contradiction Accepted as Version Change
 For domains where truth changes over time. When a new fact contradicts an old one
 in a :temporal domain, the old fact is marked =:superseded= but retained. The
 timeline is queryable: "You believed X on Tuesday, Y on Friday, Z on Sunday."
 Use for: personal belief evolution, project plan revisions, scientific
 consensus shift over time, any knowledge where the change itself is information.
 ** Policy Assignment
 The policy is assigned when a category is defined. New categories default to
 =:coexistent= (never loses information). Core security categories are explicitly
 =:exclusive=. The gate stack's bootstrapped facts are =:exclusive= because they
 describe the actual filesystem, not perspectives.
 The Screamer admission gate does not reject all contradictions. It rejects
 contradictions in =:exclusive= domains and flags them in =:coexistent= and
 =:temporal= domains. The constraint solver still works because queries scope
 their constraint set to a single provenance domain. "Is X true according to my
 memex?" is a different query than "Is X true according to Wikidata?" Each has
 a self-consistent internal logic. The contradiction is between domains, not
 within them.
 ** Why This Matters for the Broader Memex
 In the coding domain, contradiction is rare and must be resolved — a gate can't
 both allow and block the same path. In the broader memex, contradiction is the
 product, not the error. Your poetry analysis contradicts your last diary entry
 on the same topic. Your reading of /Pale Fire/ changed between 2023 and 2025.
 Wikidata says Mount Everest is 8848m (China: rock height); DBpedia says 8849m
 (Nepal: snow height). The symbolic engine's job is not to decide which is right.
 It is to surface the tension with provenance — "these three sources disagree.
 Here is the chain for each."
 * How Categories Grow — The Organic Ontology
 Whitehead's /Principia Mathematica/ took over 300 pages to define the logical
 foundations before it could prove that one plus one equals two. Every category
 introduced carried a burden of justification. Every inference rule had to be
 demonstrated sound. This is the classical approach to ontology: define everything
 upfront, exhaustively, formally.
 Passepartout cannot afford this and does not need it. Its domain is bounded
 (software engineering, personal knowledge, literary engagement, daily life) and
 its ontology grows from the system's own operation:
 1. *The gate stack seeds the ontology.* Every gate vector is an implicit claim
   about a category of things. The bootstrap makes these claims explicit. The
   seed is 50-70 entity classes with no human authoring required — they are
   mechanically extracted from the existing code.
 2. *New gate vectors add categories directly.* As the Dispatcher grows (new
   shell patterns, new path protections, new tool classifications), the ontology
   grows with it. Every new pattern in the gate stack becomes a fact on skill
   load. No human effort. The gate stack grows, the ontology grows.
 3. *Screamer generalizes from gate outcomes.* After 37 shell commands are blocked
   as destructive, Screamer extracts structural commonalities: "commands writing
   to block devices," "commands recursively deleting outside the workspace."
   These become new subcategories (=:block-device-command=,
   =:workspace-external-delete=) that didn't exist in the original gate patterns.
   The ontology deepens through observation.
 4. *The archivist proposes from prose.* The archivist reads a diary entry about
   a book: "Nabokov's lectures on Kafka." The LLM proposes =(:entity :nabokov
   :relation :lectures-on :value :kafka)=. Screamer checks consistency. Admitted.
   The categories =:author=, =:lectures-on=, and =:subject= didn't exist before —
   they are created on first use. This is the primary growth mechanism for the
   broader memex.
 5. *The human declares explicitly.* The human writes a declarative fact directly
   into the symbolic index. No extraction step. No LLM involvement. The fact is
   admitted with =:provenance :human-authored= — the highest trust level.
 6. *Temporal patterns crystallize into categories.* Every Sunday the memex gets a
   retrospective heading. Every Monday a planning heading. The time-awareness
   system observes the periodicity and proposes =:weekly-retrospective= and
   =:weekly-planning= as fact types. Screamer verifies they don't contradict
   existing categorizations. Admitted.
 7. *Cross-domain overlap produces parent categories.* Screamer notices that
   =:secret-files= (from the gate stack) and =:private-content= (from privacy
   tags) share members — =.env= is both a secret file and private content. It
   proposes =:sensitive-material= as a parent with both as children. Taxonomy
   building happens automatically through overlap detection.
 ** Growth is self-limiting by design
 Not every conceivable category is added. The system prunes through use:
 - New categories are admitted only through Screamer's consistency check. A
  category that contradicts an existing classification is rejected.
 - A category that never gets queried costs nothing (a hash table entry) but
  produces no value. It fades from use naturally.
 - Overly fine-grained categories (=.env.foo.bar.baz= as its own class) are
  rejected because they are redundant with the wildcard pattern that already
  covers them.
 - Overly broad categories that subsume meaningful distinctions ("everything is
  a =:file=") produce contradictions when Screamer tries to apply existing rules.
  Rejected.
 The system converges on a useful granularity through use, not through upfront
 design. The gate stack provides the seed. Gate outcomes, prose extraction,
 deduction, and human authoring grow the shoots. Screamer prunes contradictions.
 The ontology is a garden, not a building.
 * Semantic Wikipedia as Entity Backbone
 The gate stack provides 50-70 entity classes — adequate for a coding agent where
 the domain is bounded to files, commands, and code symbols. For a general-knowledge
 memex, 50-70 is starvation. Your memex mentions Nabokov, /Pale Fire/, Kinbote,
 Zembla, paranoid reading, unreliable narrators, postmodernism, butterfly
 migration, chess problems, and the Russian exile experience. The gate stack knows
 none of these. Organic growth through prose extraction would take years just to
 cover the entities in one person's engagement with a single novel.
 Wikidata has already done this work: approximately 2 million entity classes, over
 100 million entities, a decade of human curation. By loading the neighborhood of
 your memex into the symbolic index (entities referenced in your prose, plus their
 N-hop property net from Wikidata), the entity recognition problem vanishes. The
 archivist doesn't need to discover Nabokov from your diary. It needs to connect
 your heading to the existing Wikidata entity. That is a simpler task — reference
 resolution, not knowledge extraction.
 The LLM's role shrinks to three thin boundaries:
 1. *Input translation* — natural language question to structured query. "What do
   I think about monorepos?" → =(fact-query :entity :monorepo :relation :opinion
   :source :memex)=. Formulaic, ~100 tokens, any model sufficient.
 2. *Prose to candidate triple* — for personal memex entries that have no Wikidata
   counterpart: your opinions, your day's events, your project plans. Proposals
   are verified by Screamer before admission. This is the only extraction path
   that still requires an LLM, and its scope is limited to what Wikidata cannot
   provide — your subjective, personal, or novel content.
 3. *Result to prose* — structured answer to readable sentence. "Your 2023 diary
   says 8848m. Wikidata (last edited Feb 2024) says 8849m. They disagree on
   height." The reasoning is done; the LLM wraps the plist in grammar. ~100
   tokens, any model sufficient, purely cosmetic. Users who prefer no LLM at all
   can navigate through command-driven interaction (=/query=, =/contradictions=,
   =/audit=, =/context why=).
 Everything else — the gate stack, the fact store, the constraint solver, the type
 hierarchy, the provenance tracking, the contradiction surfacing, the cross-domain
 comparison — is pure deterministic Lisp with zero LLM tokens.
 ** The decisive simplification
 Without Semantic Wikipedia, the archivist must /discover/ entities from prose:
 extract a triple for every person, place, work, concept, and event mentioned in
 the memex. This is unbounded LLM work and the quality depends on extraction
 accuracy.
 With Wikidata loaded, the entity graph is pre-structured. The archivist's job
 changes from "discover that Nabokov wrote /Pale Fire/ and lectured on Kafka" to
 "verify that the Nabokov referenced in heading #47 is the same entity as Wikidata
 item Q36591." The second task is simpler, more reliable, and in many cases can
 be done without an LLM at all — a simple entity name match against the loaded
 Wikidata graph may suffice for unambiguous names.
 * The "Flip" — From Lossy Extraction to Deterministic Derivation
 The symbolic index begins its life as a lossy construct. The initial extraction
 from the prose — the first population of facts from LLM proposals verified by
 Screamer — is built from an uncertain foundation. Some facts are correct. Some
 are missing. Some are wrong.
 But the symbolic engine accumulates non-lossy facts through three independent
 mechanisms:
 1. *Gate outcomes* — every gate rejection is a fact. No LLM involved. These
   accumulate at the rate of user interactions.
 2. *Screamer deductions* — new facts derived from existing facts. No LLM
   involved. These accumulate whenever the fact store crosses a density threshold
   where structural patterns emerge.
 3. *Human authoring* — the human explicitly declares facts. No LLM involved.
 At some point, the non-lossy facts constitute a sufficient foundation that the
 symbolic engine can reverse the flow: instead of the LLM extracting facts from
 prose, the symbolic engine reads prose through its own lens — its now-substantial
 ontology of categories, rules, and constraints — and asserts facts in its own
 language. The extraction mechanism ceases to be probabilistic and becomes
 deterministic.
 ** The sufficiency criterion
 The architecture note (=notes/passepartout-symbolic-engine-exploration.org=) describes
 this "flip" as aspirational: "at some point, the non-lossy facts constitute a
 sufficient foundation." This design decision makes it operational:
 =(/ (count-provenance :gate-outcome :human-authored :deduced) total-facts)=
 When this ratio exceeds a configurable threshold (=SUFFICIENCY_THRESHOLD=,
 default 0.7), the system considers its foundation sufficient. The archivist
 switches from "LLM proposes, Screamer verifies" to "Screamer queries existing
 facts, applies to the new prose, and deduces new facts directly."
 The flip is visible to the user through the TUI sidebar or =/status= command:
 "Symbolic index: 847 facts (73% non-lossy, 12% LLM-proposed, 15% Wikidata).
 Sufficient foundation: YES."
 ** The flip does not mean "complete"
 In the broader memex, completeness is neither possible nor desirable. The flip
 means "deterministic enough to be trustworthy," not "comprehensive enough to be
 self-sufficient." The neural index remains the gateway to the full richness of
 prose. The symbolic index handles what can be mechanically verified. The boundary
 is permanent.
 * Ephemeral First, Persistent Later
 The architecture note's Option 5 (ephemeral facts, no disk persistence) is the
 correct first implementation. Three reasons:
 1. *The fact language is unproven.* Triples with provenance and grounding is a
   hypothesis. It may be too simple for some domains, too complex for others.
   Committing to a serialization format before knowing what's useful is premature.
 2. *The ontology is emergent.* Categories are created on first use. What proves
   useful stays; what doesn't fades. A persistent format would need a migration
   story every time the category structure changes. Ephemeral avoids this entirely
   — the facts are re-derived on each session start using the current (evolved)
   ontology.
 3. *Rebuildability is the safety net.* Because all facts have a =:grounding= to
   an Org heading, and gate-outcome facts are regenerated from the gate stack on
   every load, the entire symbolic index can be thrown away and rebuilt from
   scratch. The cost is compute, not data. This is the practical realization of
   "the prose is always the ground truth."
 The transition to persistence (Phase 5: VivaceGraph) happens when two conditions
 are met: the fact language has stabilized through use, and the accumulated
 deductions across sessions provide value that justifies the serialization cost.
 * Whitehead's Concrete Contributions — Four Operational Contributions
 =notes/passepartout-whitehead.org= extracts four concrete, engineerable ideas
 from Whitehead's /Principia Mathematica/ and /Process and Reality/. They are
 summarized here because each informs the neurosymbolic design.
 ** Contribution 1: PM-Type-Level Gates
 PM's ramified theory of types solved Russell's paradox by assigning every
 propositional function a type level, making self-application syntactically
 invalid. Passepartout applies the same principle to prevent a request from
 modifying the rules that validate it. Every cognitive tool and gate vector
 carries a =:type-level= integer. Before any gate predicate runs, the dispatcher
 checks: if the signal's type level equals or exceeds the gate's type level, the
 signal is rejected. A request to modify dispatcher rules (type-level 5) cannot
 pass a gate of type-level 4 or lower. This is a structural prohibition, not a
 heuristic — self-modification of the safety layer is impossible by construction.
 Implementation: approximately 30 lines in the existing dispatcher. No new
 dependencies. Backward compatible. This is Phase 0 of the symbolic engine
 roadmap.
 ** Contribution 2: Theory of Descriptions → Reference Resolution
 PM's theory of descriptions addressed the problem of referring to nonexistent
 entities: "the current king of France is bald" is false, not meaningless, when
 there is no unique referent. Passepartout applies this to reference resolution:
 when the user says "the function that validates secrets," a cognitive tool checks
 uniqueness before resolving. Ambiguous references trigger a clarification prompt
 rather than a blind guess.
 Implementation: approximately 40 lines as a cognitive tool. When the knowledge
 graph ships, descriptions become native Prolog queries with uniqueness constraints.
 ** Contribution 3: Process and Reality → Architectural Vocabulary
 Whitehead's process ontology maps with surprising precision to Passepartout's
 pipeline architecture. Prehension = a gate grasping a signal. Positive prehension
 = a gate passing. Negative prehension = a gate rejecting. Concrescence = the
 pipeline process from input to output. Satisfaction = the final agent response.
 This vocabulary is precise, standard, and already mapped to the architecture. It
 provides the language for the =/why= command, the gate trace, and the ARCHITECTURE
 documentation. It is descriptive, not operational — the design would be correct
 without it, but it would lack the vocabulary to describe /why/ it is correct.
 ** Contribution 4: VivaceGraph + PM Types → KG Type Hierarchy
 When the knowledge graph ships, every entity inherits PM's type hierarchy.
 Entities carry =:pm-type-level= metadata. Queries cannot return entities of the
 same level as the querying function. Self-referential knowledge becomes
 structurally impossible — no "this entity defines its own type level." This is
 Contribution 1 applied to the knowledge layer rather than the execution layer.
 The dispatcher prevents self-referential /actions/; the KG prevents
 self-referential /facts/.
 * The Provenance Chain as Product
 In the coding domain, the value of the symbolic engine is the verified fact:
 "this command is safe." In the broader memex, the value is the provenance itself:
 "this claim originated in that diary entry on that date, has been referenced 7
 times across 4 different projects, was contradicted in a retrospective 6 months
 later, and was revised in a note 3 weeks after that."
 The symbolic engine doesn't tell you what is true. It tells you what you wrote,
 when, where, and how it connects to everything else you wrote — with a verifiable
 audit trail. It is a memory prosthesis that makes your own mind legible to you.
 Every fact carries:
 - =:grounding= — the specific Org heading from which it was extracted
 - =:provenance= — who or what produced it (gate-outcome, human-authored, deduced,
  LLM-proposed)
 - =:timestamp= — when it was admitted to the symbolic index
 - =:referenced-by= — other facts that depend on or reference this one
 - =:contradicted-by= — other facts that disagree with this one (if any)
 - =:superseded-by= — if this fact was replaced by a newer version
 These fields make every fact auditable. The =/audit <node-id>= command renders
 the full provenance chain as an Org headline tree. The provenance is not a
 logging feature. It is the product.
 * The Competitive Argument
 No competitor has this problem because no competitor has a symbolic engine. The
 55 systems surveyed in =notes/competitive-landscape.org= range from pure chat
 agents (Claude, ChatGPT) to agent harnesses (Claude Code, OpenCode, Hermes) to
 platform agents (OpenClaw). None of them encode knowledge as formal facts with
 provenance. None of them verify extractions against an existing knowledge base.
 None of them can prove properties about their own rulesets.
 Their safety is heuristic (prompt-based guardrails that consume LLM tokens and
 can be evaded with clever phrasing). Their memory is flat (JSONL transcripts
 without content-addressed identity or provenance chains). Their reasoning is
 entirely neural — when you ask "why did you decide that?", the answer is a
 regenerated LLM explanation, not a retrieved inference chain.
 Passepartout's architectural bet is that this problem is worth solving — that a
 system which can surface contradictions with provenance, derive new facts from
 observations, and verify claims against a provenanced knowledge graph is
 fundamentally different from a system that can only call an LLM and hope the
 response is correct.
 The cost is the ontological work that is genuinely difficult. The reward is a
 system that cannot hallucinate at the reasoning level, whose memory is provable
 rather than empirical, and whose knowledge accumulates across sessions through
 deduction rather than through LLM re-prompting. For a life's knowledge stored in
 a personal memex, this is not a performance advantage. It is a category difference.
 * Open Questions
 Several design questions are unresolved and should remain unresolved at this
 stage. They represent research decisions that require experience running the
 system.
 ** What is the minimum viable fact language?
 Triples — =(:entity :relation :value)= with provenance and grounding — is the
 current hypothesis. It is simple enough to be parseable, expressive enough to
 capture the gate stack's implicit claims, and extensible enough that Screamer
 can operate on it. But it may be too simple. Triples do not naturally express
 temporal relations ("was X before Y?"), modal claims ("should not do X unless
 Y"), or counterfactuals — all of which may be essential for a symbolically-aided
 memex. The right granularity depends on what queries actually need to be made,
 and that cannot be known in advance.
 ** How does ontology refactoring work?
 If the seed produces 50 categories from gate extraction and later experience
 shows they are wrong — wrong granularity, missing cross-cutting concerns, conflated
 categories — how are they migrated without invalidating all existing deductions
 that cross the old category boundaries? The ephemeral-first approach (no
 persistence, rebuild from scratch) is a temporary answer. Once persistence is
 committed (VivaceGraph), refactoring the category hierarchy is a schema migration
 problem that deduction provenance makes harder — every deduced fact's chain may
 cross the old category boundary. This is not addressed in the current architecture.
 ** What is the appropriate role of the human?
 The human can explicitly declare facts, write constraints, and correct wrong
 extractions. But how much of the ontology should the human need to maintain? If
 the human must write a definition for every new category the symbolic engine
 encounters, the overhead is prohibitive. If the symbolic engine can generalize
 from instances, the human role becomes supervision rather than authorship — review
 and approve proposed generalizations. The balance cannot be set without experience.
 ** How much Wikidata is the right amount?
 Loading Wikidata entities referenced in the memex is the minimum. Loading all
 Wikidata entities within N hops of those references expands the graph
 exponentially. The right N depends on the memex's breadth — a memex focused on
 software engineering needs fewer hops than a memex spanning literature, history,
 philosophy, and science. The query performance and memory costs of a large
 Wikidata load are unknown.
 ** Can the symbolic engine satisfy queries from the user without LLM involvement?
 The design aims for zero-LLM query answering: the user issues a structured
 command (=/query=, =/contradictions=, =/audit=), and the symbolic engine responds
 directly. But natural language questions ("what do I think about monorepos?")
 still require the LLM as a thin translation layer. Whether the structured command
 interface is sufficient for daily use, or whether users will demand natural
 language interaction, determines how much LLM involvement remains in the mature
 system.
 ** Is the triplestore physically bounded or does it explode?
 A personal memex with years of diary entries, project notes, reading logs, and
 literary analyses could produce millions of triples. A naive hash table scales
 linearly but VivaceGraph's Prolog-like queries may not. The performance
 characteristics of graph queries over a million-triple knowledge base have not
 been estimated.
 * Relation to Passepartout's Existing Architecture
 The neurosymbolic engine is an extension of the existing probabilistic-deterministic
 split, not a replacement for it. The current architecture divides cognition into
 LLM-driven proposals and Lisp-driven verification. The symbolic engine deepens the
 verification side from "is this action safe?" to "is this claim supported?" — the
 same architectural pattern applied to a broader domain.
 The self-repair criterion (a file belongs in core only if, when corrupted, the
 agent cannot fix it without human help) applies to every component of the symbolic
 engine. Screamer, VivaceGraph, the fact store, the archivist — all are skills,
 loaded at runtime, hot-reloadable, and recoverable from corruption. A corrupted
 symbolic engine degrades reasoning capability but does not kill the agent. The
 eight existing core ASDF files are unchanged.
 The symbolic engine is not v3.0.0 alone. It is the layer that sits between the
 existing gate stack (which it makes explicit as facts) and the existing skill
 system (which it extends with deduction, contradiction detection, and provenance
 tracking). It grows within the current architecture without replacing any existing
 component.
 See also:
 - =passepartout-neurosymbolic-roadmap.org= — the concrete phased implementation plan
 - =notes/passepartout-symbolic-engine-exploration.org= — the original architecture note
 - =notes/passepartout-whitehead.org= — the four Whitehead contributions
 - =passepartout/docs/DESIGN_DECISIONS.org= — the existing design decisions
 - =passepartout/docs/ARCHITECTURE.org= — the current pipeline architecture
 - =passepartout/docs/ROADMAP.org= — the feature roadmap through v0.13.0
--- a/notes/passepartout-neurosymbolic-roadmap.org
+++ b/notes/passepartout-neurosymbolic-roadmap.org
@@ -1,920 +1,28 @@
-#+TITLE: Passepartout Neurosymbolic Engine — Implementation Roadmap
+#+TITLE: Passepartout Neurosymbolic Engine — SUPERSEDED
 #+AUTHOR: Agent
-#+FILETAGS: :notes:roadmap:neurosymbolic:v3.0.0:
+#+FILETAGS: :notes:roadmap:neurosymbolic:superseded:
 #+CREATED: [2026-05-08 Fri]
-
+#+SUPERSEDED: [2026-05-10 Sun]
-* Evolutionary Roadmap
+
-
+This document has been consolidated into ~passepartout/docs/ROADMAP.org~. Each neurosymbolic phase now has its full implementation spec (rationale, code sketches, test catalog, line budget) inline in the roadmap's version sections:
-This roadmap describes a phased implementation of the symbolic engine. It is
+
-independent of the feature roadmap in =passepartout/docs/ROADMAP.org= — Phase 0
+| Phase | Version |
-can ship immediately alongside any v0.7.x patch. The symbolic engine grows in
+|-------+---------|
-parallel with feature work, not after it.
+| 0     | v0.10.0 |
-
+| 0b    | v0.12.0 |
-Every phase is loaded as a skill, not a core ASDF component. A corrupted symbolic
+| 1     | v0.14.0 |
-engine degrades reasoning capability but does not kill the agent. This satisfies
+| 1a    | v0.16.0 |
-the self-repair criterion documented in =passepartout/docs/ARCHITECTURE.org= and
+| 2     | v0.18.0 |
-=passepartout/AGENTS.md=.
+| 3     | v0.20.0 |
-
+| 4     | v0.22.0 |
-The design rationale for each decision is in
+| 5     | v0.25.0 |
-=notes/passepartout-neurosymbolic-design-decisions-and-options.org=. The original
+| 6     | v0.27.0 |
-architecture exploration is in
+| 7     | v0.36.0 |
-=notes/passepartout-symbolic-engine-exploration.org=. Whitehead's contributions are
+| 8+    | v0.36.1+ |
-enumerated in =notes/passepartout-whitehead.org=.
+
-
+The "What Is NOT Built" rationale and "Competitive Advantage Analysis" sections are also now in ROADMAP.org.
-* Phase 0: PM-Type-Level Gates (~30 lines — builds on existing Dispatcher)
+
-
+Cross-references are preserved in the original files:
-** What
+- ~notes/passepartout-neurosymbolic-design-decisions-and-options.org~
-
+- ~notes/passepartout-symbolic-engine-exploration.org~
-Add =:type-level= metadata to the existing =defgate= and =def-cognitive-tool=
+- ~notes/passepartout-whitehead.org~
 macros. Before any gate predicate evaluates, the dispatcher checks structural
 type compatibility: a signal at type-level 5 cannot pass a gate at type-level 4
 or lower. Self-modification of the safety layer becomes impossible by
 construction.
 ** Rationale
 The Dispatcher gate stack currently prevents self-modification through pattern
 matching — gate vector 2b catches writes to =core-*= files as a heuristic. But
 there is no /structural/ guarantee preventing a request from modifying the rules
 that validate it. Pattern-based protection can be bypassed through indirection
 (an =eval= that constructs a write, a skill that redefines a gate function at
 runtime). A type-level check is not heuristic — it is a category error rejected
 before any predicate runs, just as PM's theory of types made self-membership
 syntactically invalid before any logical evaluation.
 ** Implementation
 1. Add =:type-level= keyword argument to =defgate= (default 0) and
   =def-cognitive-tool= (default 0) in =core-skills.org=.
 2. Add =gate-type-check= to the dispatcher's =run-gates= function in
   =security-dispatcher.org=, executed before any gate predicate.
 3. Assign type levels to existing cognitive tools: self-build-core at 5,
   write-file at 3, read-file at 1, shell at 2, eval at 4.
 4. Assign type levels to existing gate vectors: self-build boundary at 5,
   shell safety at 3, path protection at 2, network exfil at 2, secret content at 1.
 ** Verification
 Existing FiveAM gate tests continue to pass. New test: signal at type-level 5
 targeting a gate at type-level 4 returns =:reject-type-violation= without
 evaluating the gate predicate. New test: signal at type-level 1 passing through
 a gate at type-level 3 proceeds to predicate evaluation.
 ** Relation to Other Work
 This is Contribution 1 from =notes/passepartout-whitehead.org=. It is also the
 gate-to-fact bootstrap mechanism — every type-level rejection emits a structured
 event that Phase 1 ingests as a fact. The ~30 lines implement the seed of the
 ontology without any new dependencies.
 * Phase 1: Minimum Viable Fact Language (~150 lines — new skill)
 ** What
 An ephemeral, in-memory triple store with provenance tracking and contradiction
 detection. No disk persistence. All facts live in a hash table and are discarded
 on session end. Gate outcomes are ingested as facts. The gate stack's implicit
 ontology is materialized as the seed fact set.
 ** Rationale
 The architecture note's Option 5 (ephemeral facts, no persistence) is the correct
 first step. Three reasons:
 1. *The fact language is unproven.* Triples with provenance and grounding is a
   hypothesis that must be tested against real memex content before being committed
   to a serialization format.
 2. *The ontology is emergent.* Categories are created on first use. A persistent
   format would require a migration story for every category change. Ephemeral
   avoids this — facts are re-derived on each session start using the evolved
   ontology.
 3. *Rebuildability is the safety net.* Because all facts have a =:grounding= to
   an Org heading, and gate-outcome facts are regenerated from the gate stack on
   load, the entire symbolic index can be thrown away and rebuilt from scratch.
   The cost is compute, not data.
 ** Implementation — =org/symbolic-facts.org= → =lisp/symbolic-facts.lisp= (skill)
 *** Triple store
 A hash table keyed by =(entity relation)=. Values are plists:
 #+begin_example
 (:value <string-or-symbol>
 :grounding <heading-id-or-nil>
 :provenance <:gate-outcome | :human-authored | :deduced | :llm-proposed>
 :timestamp <universal-time>
 :contradiction <:awaiting-resolution-or-nil>
 :superseded-by <entity-string-or-nil>)
 #+end_example
 The =:provenance= field tracks how the fact entered the store. The
 =:contradiction= field is nil on standard facts. The =:superseded-by= field is
 set when a =:temporal= domain fact is replaced by a newer version.
 *** Bootstrap from gates
 On skill load, scan the Dispatcher's existing data structures and produce triples:
 #+begin_example
 ;; From *dispatcher-protected-paths*
 (:entity ".env" :relation :member-of-class :value :secret-config-file :provenance :gate-outcome)
 (:entity "*id_rsa*" :relation :member-of-class :value :ssh-key-file :provenance :gate-outcome)
 ;; From *dispatcher-shell-blocked*
 (:entity "rm -rf /" :relation :classified-as :value :catastrophic-command :provenance :gate-outcome)
 (:entity "dd if=" :relation :classified-as :value :catastrophic-command :provenance :gate-outcome)
 ;; From *dispatcher-network-whitelist*
 (:entity "api.telegram.org" :relation :classified-as :value :trusted-domain :provenance :gate-outcome)
 #+end_example
 This produces 50-70 entity classes immediately. No LLM involvement. No human
 authoring. Mechanically extracted from existing code.
 *** Ingest gate outcomes
 Register a post-gate hook on the Dispatcher's rejection path. Every gate rejection
 produces a triple with =:provenance :gate-outcome=:
 #+begin_example
 (:entity "/tmp/secrets.env" :relation :blocked-by :value :dispatcher-path-protection
 :provenance :gate-outcome :grounding "signal-47")
 #+end_example
 *** Query
 =(fact-query &key entity relation value source-provenance)= — pure hash-table
 lookup. Returns the matching triple or nil. ~30 lines.
 =(fact-query-all &key relation value source-provenance)= — returns all triples
 matching the filter criteria. Enables "find all files classified as secrets."
 *** Contradiction detection
 On every =fact-assert=, check if the new triple contradicts an existing one
 (same entity, same relation, different value, same provenance domain). If the
 entity's class has =:contradiction-policy :exclusive=, the new fact is rejected
 with a signal. If the policy is =:coexistent=, both facts are stored with a
 =:contradiction= flag cross-referencing each other. If the policy is =:temporal=,
 the old fact is marked =:superseded-by= the new one but retained.
 The policy table is a hash table mapping entity classes to one of =:exclusive=,
 =:coexistent=, or =:temporal=. Gate-bootstrapped facts default to =:exclusive=
 (the filesystem is singular). New categories default to =:coexistent= (safe,
 never loses information).
 ** Verification — ~8 FiveAM tests
 1. =test-bootstrap-creates-facts= — bootstrap produces correct triples from
   =*dispatcher-protected-paths*=.
 2. =test-bootstrap-creates-shell-facts= — bootstrap produces correct triples from
   =*dispatcher-shell-blocked*=.
 3. =test-gate-outcome-produces-fact= — a simulated gate rejection produces a
   triple with =:provenance :gate-outcome=.
 4. =test-fact-query-returns-correct-value= — querying by entity and relation
   returns the expected value plist.
 5. =test-duplicate-ingestion-idempotent= — asserting the same fact twice does
   not produce a duplicate or a contradiction.
 6. =test-exclusive-contradiction-rejected= — asserting a contradictory fact in
   an =:exclusive= domain returns a rejection.
 7. =test-coexistent-contradiction-flagged= — asserting a contradictory fact in a
   =:coexistent= domain stores both with cross-referencing flags.
 8. =test-temporal-supersedes= — asserting a newer fact in a =:temporal= domain
   marks the old fact as superseded but retains it.
 ** Relation to Other Work
 This is Phase 1 of =notes/passepartout-v3.0.0-roadmap.org=. It implements Options 4 and 5
 from the architecture note. The contradiction policies are from
 =passepartout-neurosymbolic-design-decisions-and-options.org=.
 * Phase 2: Screamer as Admission Gate (~200 lines — new skill)
 ** What
 Wrap Screamer (a constraint solver with non-deterministic backtracking) as a
 skill. Use it for consistency checking against the triple store and for deduction
 of new facts from existing ones. Screamer is the *verification* layer; VivaceGraph
 (introduced in Phase 5) is the *storage* layer.
 ** Rationale
 The architecture note's "verified extraction" pattern requires a deterministic
 admission gate. Screamer's non-deterministic backtracking finds contradictions
 that simple string comparison misses. For example, if existing facts say "all
 config files with extension =.env= are classified as secrets," and the LLM
 proposes "=app.env= is not secret," Screamer finds the contradiction by
 substituting =app.env= into the existing rule. A naive string-keyed hash table
 comparison would miss this because ="app.env"= and =".env"= are different strings.
 Screamer also enables deduction — new facts from existing ones without any LLM
 involvement. If all files matching =*.env= are secrets, and =prod.env= matches
 =*.env=, then =prod.env= is a secret. Deduced facts carry =:provenance :deduced=
 and a =:derived-from= chain pointing to the facts they were derived from.
 ** Implementation — =org/symbolic-screamer.org= → =lisp/symbolic-screamer.lisp= (skill)
 *** Wrap Screamer
 Screamer is available via Quicklisp. Load at runtime via =ql:quickload :screamer=.
 Not an ASDF dependency — if Screamer is not installed, the skill degrades
 gracefully (no consistency checking, no deduction — the fact store still
 functions as a hash table with provenance tracking).
 *** Consistency check
 =(screamer-consistent-p candidate-fact existing-facts)= — expresses the fact
 store as Screamer constraint variables. The candidate fact is asserted. Screamer
 checks solvability. Returns =:consistent=, =:contradiction <details>=, or
 =:redundant= (the fact is already implied by existing facts).
 Early-stage: the consistency check works on simple triples. As the fact store
 grows, rules of the form "all X are Y" (representing protected paths, shell
 patterns, class memberships) become Screamer constraints that new facts must
 satisfy.
 *** Deduction
 =(screamer-deduce existing-facts)= — Screamer finds implications of the existing
 fact set that are not already in the store. New facts are asserted with
 =:provenance :deduced= and a =:derived-from= list of source fact keys.
 Deduction is not run on every assertion — it is a background task triggered by
 heartbeat or manually. The cost is compute (Screamer exploration), not tokens.
 *** Admission gate
 =(screamer-admit candidate-fact existing-facts)= — wraps consistency check with
 the contradiction policy lookup. If the candidate fact's entity class has policy
 =:exclusive=, contradictions reject. If =:coexistent=, flag. If =:temporal=,
 supersede.
 This is the function the archivist calls before any LLM-proposed fact enters the
 store. It is also called on human-authored facts (which override the policy —
 the human can assert contradictory facts in any domain). It is not called on
 gate-outcome facts (gates are the ground truth for security domains).
 ** Verification — ~6 FiveAM tests
 1. =test-screamer-consistency-passes= — a fact consistent with existing triples
   returns =:consistent=.
 2. =test-screamer-contradiction-detected= — "app.env is not secret" contradicts
   "all *.env files are secrets" and returns =:contradiction=.
 3. =test-screamer-redundant-detected= — asserting a fact already implied by
   existing facts returns =:redundant=.
 4. =test-screamer-deduction-produces-new-fact= — given "all *.env files are
   secrets" and "prod.env matches *.env", Screamer deduces "prod.env is secret."
 5. =test-admission-gate-rejects-contradiction= — the archivist's proposal that
   contradicts an =:exclusive= domain fact is rejected.
 6. =test-admission-gate-flags-coexistent-contradiction= — the archivist's proposal
   that contradicts a =:coexistent= domain fact is stored with a cross-reference.
 ** Relation to Other Work
 This is Phase 2 of =notes/passepartout-v3.0.0-roadmap.org=. It implements the "LLM as proposer"
 pattern from the architecture note. Screamer's role is defined in
 =passepartout-neurosymbolic-design-decisions-and-options.org=.
 * Phase 3: Archivist as Fact Proposer (~100 lines — extends existing archivist)
 ** What
 Extend the existing archivist skill (=org/symbolic-archivist.org=) with a fact
 extraction mode. The LLM reads prose, proposes triples, and Screamer verifies
 them before admission. The archivist's existing Scribe (log distillation) and
 Gardener (link scanning) functions are unchanged.
 ** Rationale
 The archivist already walks the entire memex (the Gardener scans for broken links
 and orphans). Adding fact extraction reuses the same traversal infrastructure
 rather than duplicating it. The extraction is gated by Screamer — the LLM is a
 proposer, not an extractor. Facts that fail consistency checking are discarded.
 Facts that pass are admitted with =:provenance :llm-proposed= and =:grounding=
 to the source heading.
 ** Implementation — extends =org/symbolic-archivist.org=
 *** Propose from prose
 Given an Org heading, call the LLM with a minimal prompt (~200 tokens):
 #+begin_example
 Extract triples from this text as (:entity <name> :relation <keyword> :value <value>).
 Ground each triple to the heading. Return a list of triples.
 #+end_example
 The LLM returns structured triples via the existing JSON→plist structured output
 path from v0.4.2. The prompt is environment-aware: if the heading's file is in
 =literature/= or has =:literature:= tags, the prompt includes literature-specific
 relations (=:wrote=, =:published-in=, =:influenced=). If the heading is in
 =projects/=, the prompt includes coding-specific relations (=:depends-on=,
 =:tested-by=).
 *** Verify through Screamer
 Each proposed triple runs through =(screamer-admit candidate existing-facts)=
 from Phase 2. Consistent and coexistent-flagged triples are admitted. Contradictory
 triples in =:exclusive= domains are discarded with a log entry.
 *** Provenance tracking
 After each extraction run, update provenance counts:
 #+begin_example
 (:total-facts 847
 :gate-outcome 312
 :human-authored 12
 :deduced 89
 :llm-proposed 434)
 #+end_example
 This is the data structure that Phase 4's sufficiency criterion reads. It is
 also surfaced in the TUI sidebar or =/status= command: "Symbolic index: 847
 facts (37% from gates, 52% LLM-proposed, 10% deduced, 1% human)."
 *** Rebuildable
 Because every fact has a =:grounding= to an Org heading, the entire LLM-extracted
 subset can be discarded and re-extracted without losing gate-outcome or deduced
 facts. The =(fact-purge :provenance :llm-proposed)= function removes all
 LLM-proposed facts. A subsequent =(archivist-extract-all)= re-extracts from
 scratch.
 This is the safety net: if the LLM produces a bad extraction that passes
 Screamer's consistency check (possible in the early stages when the fact store
 has few existing facts to check against), the extraction can be redone after the
 fact store has grown. The cost is compute, not data.
 ** Verification — ~5 FiveAM tests
 1. =test-archivist-extracts-triples= — given a known Org heading with explicit
   triples in the prose, the archivist produces the correct triples via LLM.
 2. =test-archivist-verified-extraction= — a hallucinated triple is rejected by
   the Screamer admission gate.
 3. =test-provenance-counts-update= — after extraction, the provenance breakdown
   is correct.
 4. =test-purge-llm-facts= — does not delete gate-outcome or deduced facts.
 5. =test-re-extraction-idempotent= — re-extracting from the same prose after
   purging produces the same facts (Screamer verification is deterministic
   given the same starting set).
 ** Relation to Other Work
 This is Phase 3 of =notes/passepartout-v3.0.0-roadmap.org=. The archivist's role as proposer
 is described in =passepartout-neurosymbolic-design-decisions-and-options.org=
 under "The LLM as Proposer."
 * Phase 4: The "Flip" — Sufficiency Criterion (~50 lines — extends Phase 3)
 ** What
 Make the architecture note's central narrative arc operational: a measurable
 threshold for when the symbolic engine has enough non-lossy facts to bypass the
 LLM for extraction.
 ** Rationale
 The architecture note describes "at some point, the non-lossy facts constitute a
 sufficient foundation that the symbolic engine can reverse the flow" but provides
 no criterion for "some point." The sufficiency score makes the flip computable
 and visible to the user.
 ** Implementation — extends =org/symbolic-facts.lisp=
 *** Sufficiency score
 =(fact-sufficiency-ratio)= — returns the ratio of non-lossy facts to total facts:
 #+begin_src lisp
 (/ (+ (count-provenance :gate-outcome)
      (count-provenance :human-authored)
      (count-provenance :deduced))
   (fact-total-count))
 #+end_src
 When this ratio exceeds =SUFFICIENCY_THRESHOLD= (configurable env var, default
 0.7), the system considers its foundation sufficient. The threshold defaults to
 0.7 because below this, the majority of facts are LLM-proposed and therefore
 uncertain. Above 0.7, the proven foundation provides enough constraint that
 Screamer can reliably detect incorrect LLM proposals.
 *** Auto-extraction toggle
 When sufficiency is reached, the archivist switches from "LLM proposes, Screamer
 verifies" to "Screamer queries existing facts, applies category rules to the new
 prose, and deduces new facts directly." The LLM is bypassed for categories that
 have sufficient non-lossy coverage. The LLM is still used for novel categories
 that have no existing facts.
 The switch is configurable: =AUTO_EXTRACTION_ENABLED=true/false=. When disabled,
 the system continues with LLM proposals regardless of sufficiency — useful for
 domains where extraction quality is prioritized over extraction determinism.
 *** Monitor
 The TUI sidebar (v0.8.0) or =/status= command displays:
 #+begin_example
 Symbolic Index
  Total facts:    1,247
  Proven:
    Gate outcomes:     312  (25%)
    Human-authored:     47   (4%)
    Deduced:           521  (42%)
    ─────────────────────────
    Non-lossy:         880  (71%)
  LLM-proposed:        367  (29%)
  ─────────────────────────
  Sufficiency: 71% ✓  (threshold: 70%)
  Mode: AUTO-EXTRACTION (LLM bypassed for known categories)
 #+end_example
 ** Verification — ~3 FiveAM tests
 1. =test-sufficiency-below-threshold= — with 30% non-lossy facts, auto-extraction
   is not enabled.
 2. =test-sufficiency-above-threshold= — with 75% non-lossy facts, auto-extraction
   is enabled.
 3. =test-auto-extraction-produces-same-facts-as-llm-extraction= — for a category
   with sufficient non-lossy coverage, auto-extraction produces facts that a
   subsequent LLM extraction also produces (the deterministic path is consistent
   with the probabilistic path).
 ** Relation to Other Work
 This is Phase 4 of =notes/passepartout-v3.0.0-roadmap.org=. The flip concept originates in
 =notes/passepartout-symbolic-engine-exploration.org= (lines 68-76) and is refined in
 =passepartout-neurosymbolic-design-decisions-and-options.org= under "The Flip."
 * Phase 5: VivaceGraph as Persistent Store (~300 lines — new skill)
 ** What
 Replace the ephemeral hash-table triple store with VivaceGraph, a Lisp-native
 graph database with Prolog-like queries. Add the KG type hierarchy (PM type
 levels applied to the knowledge layer). Define the persistence format from the
 fact language that survived Phases 1-4.
 ** Rationale
 By this point, the triple fact language has been battle-tested through four
 phases of gate outcomes, Screamer deductions, LLM proposals, and cross-domain
 comparisons. The facts that proved useful define the persistent schema. The ones
 that weren't are left behind. The serialization format is not designed upfront;
 it emerges from use.
 The transition from ephemeral to persistent is justified when two conditions are
 met: (1) the fact language has stabilized (categories are being queried, not
 constantly refactored), and (2) accumulated deductions across sessions provide
 value that justifies the serialization cost.
 ** Implementation — =org/symbolic-vivacegraph.org= → =lisp/symbolic-vivacegraph.lisp= (skill)
 *** Wrap VivaceGraph
 VivaceGraph is available via Quicklisp. Load at runtime. Not an ASDF dependency.
 If not installed, the fact store continues as a hash table (Phase 1-4 behavior)
 with a log warning: "VivaceGraph not available — persistence disabled."
 *** Prolog-like queries
 Replace =fact-query= with graph traversals:
 #+begin_src lisp
 ;; Find all files classified as secrets
 (vivace-query '(:and (:entity ?e)
                     (:member-of-class ?e :secret-file)))
 ;; Find all files classified as secrets that were modified today
 (vivace-query '(:and (:entity ?e)
                     (:member-of-class ?e :secret-file)
                     (:modified-since ?e ,(today-timestamp))))
 ;; Find contradictions between Wikidata and the memex
 (vivace-query '(:and (:entity ?e)
                     (:has-value ?e ?v1 :source :wikidata)
                     (:has-value ?e ?v2 :source :memex)
                     (:not-equal ?v1 ?v2)))
 #+end_src
 *** KG type hierarchy (Contribution 4 from Whitehead)
 Every entity in the graph carries =:pm-type-level= metadata. Queries cannot
 return entities whose type level equals or exceeds the querying function's type
 level. A fact-finding query at type-level 2 cannot return facts at type-level
 3 or higher. Self-referential knowledge — "this fact defines its own type" —
 becomes structurally impossible because the type level is assigned at creation
 and cannot be modified by a fact of the same or higher level.
 This is Contribution 1 (type-level gates) applied to the knowledge layer rather
 than the execution layer. The dispatcher prevents self-referential /actions/; the
 KG prevents self-referential /facts/.
 *** Persistence format
 The fact language that survived Phases 1-4 defines the format. Each entity is a
 node; each triple is an edge with properties (=:grounding=, =:provenance=,
 =:timestamp=). The format is not a new design — it is the triple schema evolved
 through use, serialized by VivaceGraph's native persistence.
 If the fact language later evolves to n-ary relations, VivaceGraph's graph model
 accommodates this natively — edges can carry arbitrary property plists. The
 triple form is a special case of the general graph model.
 *** Load on startup, save on interval
 On daemon start, =(vivacegraph-load)= reads the last saved graph. On heartbeat,
 =(vivacegraph-save)= persists the graph in its native format to
 =~/.cache/passepartout/facts.vg~. The interval matches the existing
 =*memory-auto-save-interval*=. The save is atomic: write to a temp file, rename
 on success. Corruption-safe.
 ** Verification — ~5 FiveAM tests
 1. =test-vivacegraph-roundtrip= — save and load preserves all facts with
   provenance metadata.
 2. =test-prolog-query-returns-results= — a query for all secret files returns
   the bootstrapped gate facts.
 3. =test-prolog-query-cross-domain= — a query for contradictions between Wikidata
   and memex provenance returns correct results.
 4. =test-type-level-prevents-self-reference= — a query from a type-level-2
   function cannot return type-level-3 facts.
 5. =test-fact-store-fallback-without-vivacegraph= — when VivaceGraph is not
   loaded, the hash-table fallback functions identically to Phase 1-4 behavior.
 ** Relation to Other Work
 This is Phase 5 of =notes/passepartout-v3.0.0-roadmap.org= and Contribution 4 from
 =notes/passepartout-whitehead.org=. The architecture note's Option 1
 (auto-formalizer KG) converges with Option 4 (one memex, two indices) here —
 VivaceGraph is the persistence layer for the symbolic index within the
 one-memex-two-indices architecture.
 * Phase 6: ACL2 for Structural Verification (~200 lines — new skill)
 ** What
 Wrap ACL2 as a skill. Prove structural properties of the KG type hierarchy and
 rule sets. Not for empirical claims.
 ** Rationale
 The architecture note positions ACL2 as verifying LLM-proposed facts. But many
 facts are empirical ("this command is destructive on Linux"), not logical. The
 Whitehead note clarifies the right role: structural verification. ACL2 proves
 that the type hierarchy has no cycles, that the rule set is non-contradictory,
 and that the gate-to-fact bootstrap preserves the Dispatcher's intent. These are
 structural properties that can be formally verified, not empirical claims that
 depend on external reality.
 ** Implementation — =org/symbolic-acl2.org= → =lisp/symbolic-acl2.lisp= (skill)
 *** Type consistency proofs
 =(acl2-verify-type-hierarchy facts)= — prove that the KG type hierarchy has no
 cycles: no entity of type-level 3 depends on an entity of type-level 5, no parent
 category has a child that subsumes it, no category is its own ancestor via the
 child-of relation. These are structural properties of the graph, independent of
 what the facts /say/.
 *** Rule set consistency
 =(acl2-verify-rule-consistency rules)= — prove that the accumulated Dispatcher
 rules (from HITL approvals) are non-contradictory: no rule allows a command that
 another rule blocks, no rule permits a path access that another denies. If the
 rule set is contradictory, ACL2 identifies the contradictory subset with the
 provenance of each rule. The human resolves the contradiction.
 *** Extraction verification
 =(acl2-verify-bootstrap-preservation)= — prove that the gate-to-fact bootstrap
 (Phase 0-1) preserves the Dispatcher's intent: every blocked pattern in the gate
 stack maps to a fact in the store; every fact with =:provenance :gate-outcome= is
 grounded in a specific gate vector; no gate-bootstrapped fact contradicts another
 gate-bootstrapped fact.
 ** Not in scope
 ACL2 does not verify that =rm -rf / is destructive. That is an empirical claim
 about Linux. Screamer handles empirical consistency (does this new claim
 contradict existing observations?). ACL2 handles structural consistency (does
 this reasoning structure have formal flaws?). The boundary is: empirical claims
 go to Screamer; structural claims go to ACL2.
 ** Verification — ~4 FiveAM tests
 1. =test-acl2-type-hierarchy-no-cycles= — a synthetic KG with a type-level cycle
   is detected and reported.
 2. =test-acl2-rule-set-contradiction-detected= — two Dispatcher rules that
   contradict each other produce a contradiction report with provenance.
 3. =test-acl2-bootstrap-preservation= — the bootstrap extraction from the gate
   stack is verified to have no missing or extra facts.
 4. =test-acl2-not-loaded-graceful-degradation= — when ACL2 is not installed, the
   skill loads but returns ":ACL2 not available — structural verification
   disabled" without crashing.
 ** Relation to Other Work
 This is Phase 6 of =notes/passepartout-v3.0.0-roadmap.org=. ACL2's role is refined in
 =passepartout-neurosymbolic-design-decisions-and-options.org= from the
 architecture note's broader claim to the structural verification scope.
 * Phase 7: The 10-80-10 Planner (~500 lines — new skills, last phase)
 ** What
 A planning engine built on the mature symbolic index. Screamer expresses task
 planning as a constraint satisfaction problem. ACL2 verifies plans for structural
 soundness. The LLM handles the I/O boundaries (natural language → structured goal
 ← natural language response). The symbolic engine handles the reasoning.
 ** Rationale
 This is v3.0.0 as described in the architecture note and the ROADMAP. It is the
 final phase because it requires a populated, queried, and trusted symbolic index.
 The full planner is useless without a mature ontology and a proven deducer. By
 the time Phase 7 begins, Phases 0-6 have accumulated months of gate outcomes,
 Screamer deductions, verified LLM proposals, and human-authored facts. The
 symbolic index has achieved sufficiency. The ontology has stabilized through use.
 The planner is built on a foundation, not a speculation.
 ** Implementation — =org/symbolic-planner.org= → =lisp/symbolic-planner.lisp= (skill)
 *** Task decomposition as constraint satisfaction
 The user specifies a goal: "refactor the authentication module to support OAuth2."
 The LLM translates this to a structured goal plist. Screamer expresses the planning
 problem:
 - /Variables/: subtasks (write OAuth2 client, add token store, update auth
  middleware, write tests, update documentation)
 - /Constraints/: dependency ordering (tests depend on implementation), resource
  limits (one file write at a time), safety invariants (no modification of
  =core-*= files)
 - /Objective/: find an ordering that satisfies all constraints
 Screamer returns a viable plan or reports unsolvability with the conflicting
 constraints.
 *** Plan verification
 ACL2 proves that the plan contains no deadlocks (two subtasks waiting on each
 other), no dependency cycles (A depends on B depends on C depends on A), and
 no safety violations (no plan step requires a gate-blocked operation).
 If verification fails, ACL2 identifies the failing subtask and the violated
 constraint. The planner re-decomposes the problematic branch (the existing
 ROADMAP's branch pruning, v0.11.0, but symbolically rather than neurally).
 *** Neuro-symbolic boundary
 The LLM handles the I/O boundaries:
 - *Input* (10%): natural language → structured goal plist. "Refactor auth for
  OAuth2" → =(:goal :refactor-component :target :auth-module :add-feature :oauth2)=.
  Small prompt, formulaic translation, ~100 tokens.
 - *Reasoning* (80%): Screamer plans. ACL2 verifies. VivaceGraph provides the
  facts about file structure, dependencies, and gate constraints. Zero LLM
  tokens.
 - *Output* (10%): structured plan → natural language response. The verified plan
  plist is formatted as "I'll refactor the authentication module in 5 steps:
  1) Create the OAuth2 client (depends on: nothing, modifies: auth/client.lisp)
  2) Add the token store..." Small prompt, formulaic translation, ~150 tokens.
 *** TUI visualization
 The plan is rendered as an Org headline tree in the TUI, with each subtask as a
 node showing its terminal state (=todo=, =next-action=, =in-progress=, =done=,
 =blocked=, =stuck=), its constraints, and its verified properties. This is the
 same task tree visualization planned for v0.11.0 in the feature roadmap, but
 with the addition of Screamer constraint annotations and ACL2 verification
 badges.
 ** Verification — ~6 FiveAM tests
 1. =test-goal-plist-from-natural-language= — natural language input produces
   correct structured goal plist (LLM-dependent but formulaic; tested with
   deterministic mock).
 2. =test-screamer-plan-satisfies-constraints= — Screamer produces a plan that
   satisfies all specified dependencies and safety constraints.
 3. =test-screamer-report-unsolvable= — Screamer reports unsolvability when
   constraints are contradictory.
 4. =test-acl2-verifies-plan-no-cycles= — ACL2 verifies a valid plan has no
   dependency cycles.
 5. =test-acl2-rejects-cyclic-plan= — ACL2 detects a dependency cycle in an
   invalid plan.
 6. =test-plan-to-natural-language= — structured plan plist produces readable
   natural language output.
 ** Relation to Other Work
 This is Phase 7 of =notes/passepartout-v3.0.0-roadmap.org=. It corresponds to the ROADMAP's
 v0.9.0 (task planning) and v3.0.0 (full 10-80-10 architecture). It is the last
 component because it depends on a mature symbolic index from Phases 0-6.
 * Phase 8+: Semantic Wikipedia Integration (TBD lines — optional acceleration)
 ** What
 Load Wikidata entities referenced in the memex into the symbolic index. Every
 entity the user's prose mentions gets its Wikidata property graph — type hierarchy,
 relations, dates, citations — as triples with =:provenance :wikidata=.
 ** Rationale
 The gate stack provides 50-70 entity classes — adequate for a coding agent.
 For a general-knowledge memex containing literature, philosophy, history,
 science, and daily life, 50-70 is starvation. Organic growth through prose
 extraction (Phase 3) would take years to cover the entities mentioned in a single
 reading of /Pale Fire/. Wikidata has already done this work at scale.
 The LLM's role in extraction shrinks dramatically. Without Wikidata, the archivist
 must /discover/ that Nabokov wrote /Pale Fire/, lectured on Kafka, and emigrated
 from Russia — extracting each triple from prose. With Wikidata, the Nabokov entity
 is pre-structured. The archivist's job changes from "discover entities" to
 "connect your heading to the existing entity."
 ** Implementation sketch
 1. *Index referenced entities.* Scan memex prose for entity names (capitalized
   noun phrases, names in Org links, headings in =literature/= directories). For
   each, attempt Wikidata entity resolution (string match, disambiguation via
   context).
 2. *Load N-hop property net.* For each resolved entity, load its Wikidata
   properties: instance-of, subclass-of, authored, published-in, influenced-by,
   birth-date, death-date, etc. Load the same for entities directly connected
   to it (1-hop neighbors). Optionally expand to 2-hop for deeply connected
   domains.
 3. *Admit with co-existent policy.* Wikidata facts are admitted with
   =:provenance :wikidata= and contradiction policy =:coexistent=. They do not
   override your memex's facts. They sit alongside them. Contradictions are
   surfaced, not resolved.
 4. *Cross-domain query.* "What does my memex say about Nabokov that Wikidata
   doesn't?" "Where does my memex disagree with Wikidata?" "What entities in my
   memex have no Wikidata counterpart?" These queries are pure VivaceGraph
   traversals — zero LLM tokens.
 ** Not a Phase 0 prerequisite
 Semantic Wikipedia integration is an accelerator, not a prerequisite. Phases
 0-7 work without it — the ontology grows through gate outcomes, Screamer
 deductions, LLM proposals, and human authoring. Wikidata compresses the timeline
 for the broad domain but does not change the architecture. The admission gate
 (Screamer), contradiction policies, provenance tracking, and neuro-symbolic
 boundary are identical with or without it.
 ** Open question
 How much Wikidata is the right amount? Loading entities referenced in the memex
 is the minimum. Loading all entities within N hops of those references expands
 the graph exponentially. The right N depends on the memex's breadth and the user's
 query patterns. A memex focused entirely on software engineering may need only 1
 hop. A memex spanning literature, history, philosophy, and science may need 3-4
 hops. The query performance and memory costs of a large Wikidata load have not
 been estimated.
 * Summary: Lines and Dependencies
 | Phase | Component               | Lines | New Skill? | Depends On      | Earliest Release |
 |-------+-------------------------+-------+------------+-----------------+------------------|
 | 0     | PM-type-level gates     | ~30   | No         | Dispatcher      | Immediately      |
 | 1     | Triple fact store       | ~150  | Yes        | Phase 0         | v0.7.2+          |
 | 2     | Screamer admission      | ~200  | Yes        | Phase 1         | v0.7.2+          |
 | 3     | Archivist extraction    | ~100  | Extends    | Phase 2         | v0.8.0+          |
 | 4     | Flip — sufficiency      | ~50   | Extends    | Phase 3         | v0.8.0+          |
 | 5     | VivaceGraph store       | ~300  | Yes        | Phase 4         | v0.10.0+         |
 | 6     | ACL2 verification        | ~200  | Yes        | Phase 5         | v0.12.0+         |
 | 7     | 10-80-10 planner         | ~500  | Yes        | Phase 6         | v3.0.0           |
 | 8+    | Semantic Wikipedia      | TBD   | Yes        | Phase 5         | TBD              |
 |-------+-------------------------+-------+------------+-----------------+------------------|
 | Total |                         | ~1530 |            |                 |                  |
 This roadmap is independent of the feature roadmap in
 =passepartout/docs/ROADMAP.org=. Phase 0 ships alongside any v0.7.x patch. The
 symbolic engine grows in parallel with feature work (TUI improvements, MCP tools,
 gateway expansion, etc.), not after it. The feature roadmap describes /what/ the
 agent can do; this roadmap describes /how/ it knows what it knows.
 The total new code across all phases is approximately 1,530 lines. Relative to
 the existing codebase (~8,000+ lines across 40+ Org source files and 30+ skills),
 the symbolic engine is a ~20% addition. Relative to the ROADMAP's planned feature
 work through v0.13.0 (thousands of lines of TUI rendering, MCP protocol
 implementation, skin engine, planning, etc.), the symbolic engine is a small,
 orthogonal thread that grows the architecture's reasoning depth while the feature
 work grows its interaction breadth.
 * Competitive Advantage Analysis
 ** Phase 0-1: Deterministic safety, now with type-level guarantees
 The existing Dispatcher gate stack already provides 0-LLM-token safety verification.
 Phase 0 adds structural guarantees: no heuristic bypassing of the type hierarchy.
 A request to modify the dispatcher's own rules is impossible by construction, not
 just caught by pattern matching. No competitor has this — their equivalent of
 "core file protection" is a prompt instruction, not a type system.
 ** Phase 2-3: Verified extraction — the symbolic index grows without corruption
 No competitor verifies extracted facts against an existing knowledge base. Their
 memory systems (Claude Code's ~extractMemories~, Hermes's MemoryProvider, OpenClaw's
 session transcripts) record what the LLM /said/ happened, not what the system
 /proved/ happened. Passepartout's Screamer-gated admission makes the symbolic index
 a monotonic, verified structure. Facts are admitted because they are consistent,
 not because the LLM generated them.
 ** Phase 4-5: Self-accelerating knowledge — the downward cost curve
 The sufficiency criterion makes Passepartout's "cheaper over time" thesis
 measurable. As the ratio of non-lossy facts grows, LLM calls for extraction
 decrease. At sufficiency, extraction of known categories becomes deterministic.
 The downward cost curve is not a marketing claim — it is a structural property
 of the architecture, visible through the sufficiency score.
 ** Phase 6-7: Provable plan soundness
 No competitor verifies task plans against formal constraints. Claude Code plans
 in a single LLM call with no post-hoc verification. Hermes decomposes tasks into
 subtasks but does not prove them non-contradictory. Passepartout's ACL2-verified
 plans are structurally guaranteed to have no deadlocks, no dependency cycles,
 and no safety violations. The verification is a proof, not a prompt.
 ** Semantic Wikipedia: Entity coverage at zero marginal cost
 No competitor has a general-knowledge entity graph because no competitor has a
 symbolic engine to populate. Claude Code knows codebases; it doesn't know that
 Nabokov wrote /Pale Fire/ and lectured on Kafka. Passepartout with Wikidata
 loaded knows both, and the entity knowledge costs zero LLM tokens — it is loaded
 once as structured data and queried via VivaceGraph traversals.
 ** The permanent competitive advantage
 The competitive advantage is not any single feature. It is the architecture's
 ability to accumulate verified knowledge from four independent sources (gates,
 deduction, verified LLM proposals, human authoring) and to make that knowledge
 queryable with provenance. Competitors accumulate chat transcripts. Passepartout
 accumulates a provenanced, self-verifying knowledge graph. Transcripts become
 stale and unreliable. The knowledge graph becomes richer and more trustworthy
 with every session.
 * What Is NOT Built
 1. *A separate knowledge graph serialization format before the ephemeral phase
   proves what facts are useful.* Premature format commitment is the ontology
   problem writ small. Let use determine the format.
 2. *ACL2 verification of empirical claims.* Apple is red. rm -rf / is destructive.
   These are observations, not theorems. Screamer handles empirical consistency.
   ACL2 handles structural verification.
 3. *VivaceGraph before Screamer.* The admission gate is the critical path. The
   persistence layer is an optimization of a working system.
 4. *A per-fact ontology designed upfront.* Extract from the gate stack, extend
   from deductions and observations, prune through contradiction detection. The
   ontology is a garden, not a building.
 5. *New core ASDF components.* Every phase is a skill. A corrupted symbolic
   engine degrades reasoning but does not kill the agent. Satisfies the
   self-repair criterion.
 6. *A "complete" symbolic index for the broad domain.* The neural index is the
   permanent gateway to the richness of prose. The symbolic index handles what
   can be mechanically verified. The boundary is permanent, not transitional.
   The neuro is the brain. The symbolic is the education.
 * Relation to the Feature Roadmap
 The feature roadmap (=passepartout/docs/ROADMAP.org=) describes Passepartout's
 evolution through v0.13.0: TUI improvements, MCP-native tools, task planning,
 skill creation, evaluation harnesses, voice gateways, themes, and channels.
 These are /interaction surface/ features — they expand what the agent can do.
 This roadmap describes the /reasoning substrate/ — it deepens how the agent
 knows what it knows. It is independent of the feature sequence. Phase 0 can ship
 alongside any v0.7.x patch. Phases 1-4 ship during the v0.8.x-v0.10.x feature
 cycle. Phases 5-7 ship during the v0.11.x-v0.13.x cycle.
 The two roadmaps converge at v3.0.0: the feature roadmap provides the interaction
 surface (a polished TUI, a rich tool ecosystem, a multi-gateway communication
 layer); this roadmap provides the reasoning depth (a provenanced knowledge graph,
 a deterministic constraint solver, a verified planning engine). The surface
 without the substrate is a chat agent with good UX. The substrate without the
 surface is a theorem prover without a user. Together, they are the v3.0.0
 architecture.
 See also:
 - =notes/passepartout-neurosymbolic-design-decisions-and-options.org= — the
  design rationale for every decision in this roadmap
 - =notes/passepartout-symbolic-engine-exploration.org= — the original architecture
  exploration and five architecture options
 - =notes/passepartout-whitehead.org= — Whitehead's four concrete contributions
 - =passepartout/docs/ROADMAP.org= — the feature roadmap through v0.13.0
 - =passepartout/docs/ARCHITECTURE.org= — the current pipeline architecture
 - =notes/passepartout-v3.0.0-roadmap.org= — the original concrete plan (superseded by this
  document)
--- a/projects/AGENTS.md
+++ b/projects/AGENTS.md
@@ -0,0 +1,180 @@
 # AGENTS.md
 ## Development Cycle (every change)
 0. **Start the runtime** — boot the Lisp image that loads your project.
   For passepartout: `passepartout daemon` (loads the entire project into one SBCL image).
   For standalone CL projects: SBCL with `(ql:quickload :your-project)`.
   The running image IS the development environment. The REPL is mandatory.
   The SBCL fallback below exists only for bootstrapping (when the runtime cannot
   start) and CI.
 1. **Read the next TODO** — find the next unreached `*** TODO` item in
   `docs/ROADMAP.org` (search `*** TODO`). Read its prose, `:PROPERTIES:`,
   and estimated line budget. That item is the target for this change cycle.
 2. **Create a branch** — `git checkout -b feature/<version>-<name>` from main.
   Every feature develops in its own branch. Branches are cheap, disposable,
   and keep abandoned work off main. Name the branch after the version and
   a short slug: `feature/v0.1.0-layout-engine`, `feature/v0.9.0-eval-harness`.
   Complex features that span multiple phases may use a single branch with
   multiple commits rather than one branch per phase.
 3. **Think in org** — write your reasoning, goals, and approach in the .org file first.
 4. **Write contract** — define a `** Contract` section listing each function's behavior:
   `(fn-name args)`: description. Returns/guarantees ...
 5. **TDD in REPL** — the inner loop runs entirely in the running image:
   a. **Write tests in org** — add `fiveam:test` forms to the `* Test Suite` section
      of the .org source file. Tests are definitions, not explorations — write them
      in the file first.
   b. **Send tests to REPL → RED** — evaluate the test forms in the running image.
      Run the suite. It must FAIL — the implementation doesn't exist yet.
      Record the failure output in the .org file under the test.
   c. **Develop implementation in REPL** — redefine functions directly in the
      running image. Explore. Discover the real argument shapes, edge cases, and
      helper functions through interaction, not speculation. Each `defun` in the
      REPL is immediate — no tangle, no reload, sub-second feedback.
   d. **Run tests → GREEN** — after each change, re-run the suite from the REPL.
      When all tests pass, the implementation is complete. If still RED, return to
      step c. Record the passing output in the .org file under the test.
   e. **Copy code to org** — copy each finished function from the REPL into its
      own `#+begin_src lisp` block in the .org file. The code is already working;
      the file is now its permanent home. One function per block. Never write a
      function in a file that hasn't been proven in the image.
 6. **Update literate prose** — write/update the explanatory text around the code:
   what it does, why it exists, how it connects to the rest of the system.
 7. **Tangle** — generate the .lisp file from the .org source:
   ```
   emacs --batch --eval "(progn (require 'org) (find-file \"org/FILE.org\") (org-babel-tangle) (kill-buffer))"
   ```
   Tangling is a finalization step, not part of the inner loop. The inner loop
   (steps 5a–5e) happens entirely in the REPL. Tangle once, when the file is
   ready to commit.
 8. **Run full test suite** — from the REPL, run every test suite in the project:
   ```
   (fiveam:run-all-tests)
   ```
   This catches regressions across the entire system. A function that passes its
   own tests but breaks another module is not done.
 9. **Validate block balance** — check that every `#+begin_src lisp` block in the
   modified .org files has balanced parentheses. Use your project's equivalent
   function or the SBCL fallback below.
 10. **Commit on the branch** — include the RED and GREEN test output recorded
    in the .org file as part of the commit message evidence:
    ```
    git add org/ lisp/ docs/
    git commit -m "v0.9.0: eval harness — 10 tasks, regression detection
    RED: 0/10 pass (tasks not yet defined)
    GREEN: 10/10 pass"
    ```
 11. **Mark the origin TODO DONE** — in `docs/ROADMAP.org`, change the
    `*** TODO` item to `*** DONE` and add a `:LOGBOOK:` entry with the
    completion date. This is a separate commit on the branch:
    #+begin_src org
    :LOGBOOK:
    - State "DONE"  from "TODO"  [YYYY-MM-DD Day]
    :END:
    #+end_src
 12. **Merge to main** — the merge IS the release. Rebase onto main first
    to keep history linear, then fast-forward merge:
    ```
    git checkout main
    git merge feature/v0.9.0-eval-harness
    ```
 13. **Bump the submodule** — if the project is a submodule in the parent
    `memex` repo (e.g., passepartout), stage the submodule pointer and commit:
    ```
    git add projects/passepartout
    git commit -m "bump passepartout → v0.9.0"
    ```
    Standalone projects skip this step.
 14. **Delete the branch** — `git branch -d feature/v0.9.0-eval-harness`.
    Abandoned branches can be deleted before merge with no cleanup needed.
 ## Branch Policy
 - Every feature starts on a branch from main. Branch names: `feature/<version>-<slug>`.
 - ROADMAP.org changes (DONE markers, LOGBOOK entries) happen on the branch, not
  on main directly. They merge to main with the feature.
 - If a feature fails or is abandoned, delete the branch. No revert commits, no
  dead code on main, no `;; OBSOLETE` comments. Git history preserves the
  experiment if you need to reference it later.
 - Rebase onto main before merging. Keep history linear. No merge commits.
 - Complex features that span multiple roadmap versions may live on one branch
  with multiple commits, merging to main when the entire chain is stable.
 - **Bug fixes, typos, docs-only edits, and single-session jobs do not get a
  branch.** Commit them directly to main. The heuristic: if it can be finished
  in one session and has no plausible alternative that could replace it, it
  goes to main. If it spans sessions or might be abandoned for a better
  approach, it gets a branch.
 ## Commands
 Tangle a single file:
  emacs --batch --eval "(progn (require 'org) (find-file \"org/FILE.org\") (org-babel-tangle) (kill-buffer))"
 Validate structural integrity (org/ source files only):
  emacs --batch -Q --eval '(progn (find-file "org/FILE.org") (check-parens) (kill-buffer))'
 Run tests (from REPL):
  (fiveam:run (intern "SUITE-NAME" :project-TESTS))
  (fiveam:run-all-tests)
 Run tests (SBCL fallback — only when the runtime cannot start):
  sbcl --noinform \
    --eval '(load (merge-pathnames "quicklisp/setup.lisp" (user-homedir-pathname)))' \
    --eval '(ql:quickload :your-project :silent t)' \
    --eval '(load "lisp/FILE.lisp")' \
    --eval '(fiveam:run (intern "SUITE-NAME" :project-TESTS))' --quit
 For error details: bind fiveam:*on-failure* to :debug
 ## REPL — mandatory
 All development happens in a running Lisp image. Start your runtime:
 - Passepartout: `passepartout daemon` — boots the entire project, listens on port 9105
 - Standalone CL projects: `sbcl` with `(ql:quickload :your-project)`
 Send code from opencode using the `lisp` tool (any SBCL project) or the `repl`
 tool (passepartout daemon on port 9105). The inner loop (step 5a–5e) never leaves
 the REPL:
 1. Send test forms from .org to REPL → RED
 2. Redefine functions in REPL → test → iterate
 3. Send tests → GREEN
 4. Copy working code back to .org
 Tangle only when the file is complete and ready to commit. Never batch-compile
 outside the image when the runtime is available. Use the SBCL fallback above only
 when the runtime itself cannot start.
 ## Rules
 - .org is source of truth; .lisp is generated — never edit .lisp directly
 - Every code change starts with a contract and a failing test
 - Prove RED before writing implementation
 - Implementation is developed in the REPL, then copied to .org — never write
  code in a file that hasn't been proven in the image
 - Validate before committing
 - If a tool fails, explain why and ask before trying alternatives
 - Before shipping a version, run the `** File Update Checklist` in `docs/ROADMAP.org`
 - **YOU MAY NOT** push a version tag (e.g., `v0.5.0`), create a GitHub release,
  or run `git push` that triggers CI/CD version workflows without explicit
  permission. Ask first.
--- a/projects/passepartout
+++ b/projects/passepartout
--- a/projects/passepartout-contrib
+++ b/projects/passepartout-contrib
Author	SHA1	Message	Date
Amr Gharbeia	0290feccc1	memex: split AGENTS.md into monorepo overview and per-project workflow Move the development workflow details (TDD, REPL, literate programming, branch policy) from top-level AGENTS.md into projects/AGENTS.md. Top-level AGENTS.md now describes only the monorepo structure and project list.	2026-05-12 20:13:32 -04:00
Amr Gharbeia	f6094abb7b	docs: consolidate neurosymbolic roadmap and design decisions, bump passepartout Mark notes/passepartout-neurosymbolic-{roadmap,design-decisions}.org as superseded — full implementation specs and design rationale now live inline in passepartout/docs/{ROADMAP,DESIGN_DECISIONS}.org. Bump passepartout submodule for v0.9.0 eval harness plan.	2026-05-10 14:11:15 -04:00
Amr Gharbeia	e719443ce7	memex: update AGENTS.md, add passepartout design-decisions notes, SWOT + agora notes, bump submodules → v0.8.1	2026-05-10 07:11:08 -04:00
Amr Gharbeia	04944a62e2	memex: update AGENTS.md with ROADMAP TODO workflow, bump passepartout to v0.8.0 - AGENTS.md: add steps 0 (read next TODO from ROADMAP) and 6 (mark DONE with LOGBOOK) to development cycle - Notes updates accumulated during v0.8.0 work - Bump passepartout submodule to v0.8.0	2026-05-09 15:00:35 -04:00