---
title: Validation
type: reference
tags: :passepartout:architecture:
---

* Validation

** Whitehead's Process Philosophy and Type Theory
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Alfred North Whitehead's two major bodies of work — /Principia Mathematica/ (1910–1913, with Bertrand Russell) and /Process and Reality/ (1929) — provide both the historical foundation and the descriptive vocabulary for Passepartout's architecture. The first gave us the type theory that structures the gate stack; the second gave us the process ontology that describes the pipeline.

/Principia Mathematica/ is a direct ancestor of Lisp. Alonzo Church's lambda calculus (1930s), from which John McCarthy built Lisp (1958), was a response to PM's foundational program. PM's notation:

#+BEGIN_EXAMPLE
(x).φx        ≡ (for all x, φ holds of x)
(∃x).φx       ≡ (there exists x such that φ holds)
* x̂(φx)        ≡ (the class of x satisfying φ)
ιx(φx)         ≡ (the unique x satisfying φ)
#+END_EXAMPLE

These map directly to Lisp: ~(lambda (x) (φ x))~, ~(class (x) (φ x))~, ~(the (x) (φ x))~. McCarthy cited PM as an influence. The connection is genetic, not metaphorical.

Whitehead's process philosophy is a metaphysics of /becoming/ rather than /being/. The fundamental entities are not substances but /processes/ (/actual entities/, /occasions of experience/). This maps precisely to Passepartout's pipeline architecture:

| Whiteheadian Concept             | Passepartout Mapping                        | Significance                                     |
|----------------------------------+---------------------------------------------+--------------------------------------------------|
| Actual entity (actual occasion)  | An event/signal in the pipeline             | The fundamental unit — everything else is abstraction |
| Prehension                       | A gate's grasping of a signal               | How one entity "takes account of" another         |
| Positive prehension              | A gate passing a signal                     | The signal is included in the concrescence        |
| Negative prehension              | A gate rejecting a signal                   | The signal is excluded from the concrescence      |
| Concrescence                     | The pipeline process from input to output   | Many prehensions → one satisfaction               |
| Subjective aim                   | The agent's active goal / plan              | The telos directing which prehensions matter      |
| Eternal objects                  | Pure concepts in the knowledge graph        | Universals — types, functions, categories         |
| Nexus                            | A cluster of related memory objects         | A Merkle subtree                                  |
| Satisfaction                     | The final agent response                    | The "completed" entity — determinate output       |
| Transition                       | From one signal to the next                 | The pipeline's forward motion                     |
| Causal efficacy                  | The agent's past context (memory)           | What is prehended from the settled past           |
| Presentational immediacy         | The current signal (user input)             | What is prehended from the immediate present      |

The foveal-peripheral model maps directly onto Whitehead's two modes of perception: /presentational immediacy/ is foveal focus on the current signal (vivid but superficial), while /causal efficacy/ is peripheral context from memory (dim but causally powerful). Whitehead argues that all perception is the "mixed mode" of symbolic reference — the synthesis of the two. Passepartout's ~think()~ function does exactly this: it synthesizes the current signal with context from memory into a response.

Whitehead also gives Passepartout a /descriptive vocabulary/ that is precise, standard, and already maps perfectly to the design. "I am concrescing signal 47 through gates 0-8" is not poetry — it is a precise description of dispatcher operation. "Gate 3 has negatively prehended signal 136" means the secret-content gate rejected signal 136. "The satisfaction includes a file-write prehension with Merkle commit abc123" means the response contains a file write with the given Merkle hash. The agent uses this vocabulary in its ~/why~ output and in the ARCHITECTURE.org documentation.

Not everything is useful. PM's full formalism — 300+ pages to prove ~1+1=2~ — is catastrophic as a reasoning engine. The /ideas/ (type theory, descriptions, propositional functions) are what matter, not the notation. Similarly, Whitehead's later concept of God (the "principle of concretion") and the full 25-category metaphysical system have no useful mapping to an agent architecture. Select the concepts that map; don't build a process-philosophy engine.

Passepartout is level 4 neuro-symbolic (symbolic gates + neural LLM, deterministic components coordinate heterogeneous systems). PM's type theory adds level-5 properties: /structural/ safety guarantees rather than /empirical/ ones. The dispatcher becomes not just a runtime gate stack but a type-theoretic framework where category errors are impossible by construction — just as PM made Russell's paradox impossible by construction. The Whiteheadian vocabulary reinforces the architectural identity: Passepartout is not a chatbot with safety checks. It is a /process/ — a continuous concrescence of prehensions producing satisfactions — whose safety is guaranteed by the type structure of the prehending entities.

** Historical Lineage — McCarthy's Advice Taker
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McCarthy's "Programs with Common Sense" (1959) is the direct intellectual ancestor of the Passepartout architecture. The paper proposed an "advice taker" — a program that "will draw immediate conclusions from a list of premises" expressed in "a suitable formal language (most likely a part of the predicate calculus)." The program would:

1. Accept declarative statements about the world as input.
2. Store them as logical formulas.
3. Reason from them to produce new conclusions.
4. Accept new facts and revise its conclusions.

This is precisely the Passepartout pipeline: the archivist extracts declarative facts from prose → Screamer checks them for consistency → VivaceGraph stores them → the planner reasons from them → new facts from gate outcomes and deductions revise the store. McCarthy proposed it in 1959. Passepartout is building it in 2026.

The gap between McCarthy's proposal and Passepartout's implementation is the /hallucination problem/. McCarthy assumed facts would be entered by a human programmer in formal logic. Passepartout's facts are extracted from natural language prose by an LLM — a probabilistic process that requires deterministic verification. Screamer is the component McCarthy didn't need: a constraint solver that gates LLM-proposed facts against the existing fact store.

The connection is not metaphorical. McCarthy cited /Principia Mathematica/ as an influence on Lisp. Passepartout's Whitehead analysis traces the same PM → Lisp lineage. The advice taker → Passepartout lineage completes the arc: PM's formal logic → Lisp → McCarthy's advice taker → Passepartout's neurosymbolic engine.

Reference: McCarthy, J. (1959). Programs with Common Sense. /Proceedings of the Teddington Conference on the Mechanization of Thought Processes./

** Marcus (2020): The Case Against Pure Deep Learning

Gary Marcus's "The Next Decade in AI" argues that deep learning alone is "data hungry, shallow, brittle, and limited in its ability to generalize." The paper demonstrates GPT-2 failing at basic commonsense reasoning:

- "Yesterday I dropped my clothes off at the dry cleaners and have yet to pick them up. Where are my clothes?" → GPT-2: "at my mom's house."
- "There are six frogs on a log. Two leave, but three join. The number of frogs on the log is now" → GPT-2: "seventeen."

Marcus proposes four steps toward robust AI: hybrid architecture (combining neural and symbolic), large-scale knowledge (abstract and causal, not just statistical), reasoning (formal inference over structured representations), and cognitive models (frameworks for how entities relate). Passepartout implements all four: the perceive-reason-act pipeline is hybrid, the symbolic index is causal knowledge, Screamer + ACL2 provide reasoning, and the gate-bootstrapped ontology plus MOMo modules provide cognitive models.

Marcus's core claim — "we have no hope of achieving robust intelligence without first developing systems with deep understanding" — is the justification for Passepartout's entire neurosymbolic investment. The alternative is a system that works "on a good day" and fails unpredictably. The deterministic gate stack and Screamer admission gate are the engineering realization of Marcus's call for robustness.

Reference: Marcus, G. (2020). The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence. arXiv:2002.06177.

** Gaur & Sheth (2023): CREST — Trustworthy Neurosymbolic AI

Gaur and Sheth present the CREST framework: Consistency, Reliability, user-level Explainability, and Safety build Trust — and they argue these require neurosymbolic methods. Their empirical finding: GPT-3.5 breached safety constraints 30% of the time when asked identical questions repeatedly. Claude's 16 safety rules and Sparrow's 23 rules provide no /inherent/ safety — they are heuristic guardrails that can be breached through prompt variation.

These findings validate three Passepartout design commitments: (1) prompt-level safety is insufficient — deterministic gates run in pure Lisp, cost 0 tokens, and cannot be evaded by prompt engineering; (2) inconsistency is the norm — the cardinality model expects contradiction and surfaces it with provenance; (3) knowledge infusion is required for trust — Passepartout's symbolic index IS the knowledge infusion layer, facts extracted from prose, verified by Screamer, and available for any LLM call.

Reference: Gaur, M., & Sheth, A. (2023). Building Trustworthy NeuroSymbolic AI Systems: Consistency, Reliability, Explainability, and Safety. arXiv:2312.06798.

** Sheth et al. (2022): Knowledge-Infused Learning

Sheth, Gunaratna, Bhatt, and Gaur define Knowledge-infused Learning (KiL) as "combining various types of explicit knowledge with data-driven deep learning techniques." They identify three infusion levels (shallow, semi-deep, deep) and position KiL as "a sweet spot in neuro-symbolic AI."

Passepartout's architecture is a specific implementation of KiL at the deepest infusion level: knowledge is not appended to prompts (shallow) or embedded in fine-tuning (semi-deep). It is a first-class data structure — the symbolic index — that the LLM queries through the archivist and the planner. The knowledge is living: it accumulates, is verified, carries provenance, and evolves through ontology versioning.

Reference: Gaur, M., Gunaratna, K., Bhatt, S., & Sheth, A. (2022). Knowledge-Infused Learning: A Sweet Spot in Neuro-Symbolic AI. /IEEE Internet Computing, 26/(4), 5–11.

** The Competitive Argument
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No competitor has this problem because no competitor has a symbolic engine. The 55 systems surveyed in the competitive landscape 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.

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.
