fix: correct setf form in perceive gate HITL handler

(setf (getf signal :approved t)) → (setf (getf signal :approved) t)

Caught during system compilation. This is exactly the class of bug that
the REPL-first discipline would have caught instantly.

docs/DESIGN_DECISIONS.org

@@ -286,7 +286,7 @@ Passepartout treats the LLM as a resource to be minimized. Every operation is de
 
 The three structural multipliers are:
 
-1. *Sparse tree retrieval* — loading relevant subtrees (200-800 tokens per file) rather than full files (1,500-5,000 tokens) = ~5-10x reduction per file access
+*Sparse tree retrieval* — loading relevant subtrees (200-800 tokens per file) rather than full files (1,500-5,000 tokens) = ~5-10x reduction per file access
 2. *Deterministic safety* — 9-vector dispatcher gate runs in pure Lisp (0 LLM tokens per verification) versus prompt-based guardrails (200-500 tokens per action) = infinite multiplier
 3. *REPL verification* — catches errors in-image (milliseconds, 0 LLM tokens) versus LLM correction round-trips (500-2,000 tokens per retry)
 
@@ -296,14 +296,14 @@ These compound. A coding session touching 20 files, performing 10 actions, and t
 
 *** Coding (debugging, refactoring, PR review)
 
-| Operation | Passepartout | Claude Code | Hermes (3-agent) | Savings vs Claude |
-|-----------|-------------|-------------|-------------------|--------------------|
-| File access (30 files) | 30 × 400 tok = 12,000 | 30 × 3,000 tok = 90,000 | 30 × 3,000 tok × 3 = 270,000 | 78,000 tok |
-| Reasoning rounds (20) | 20 × 3,000 tok = 60,000 | 20 × 4,000 tok = 80,000 | 20 × 3,000 tok × 3 = 180,000 | 20,000 tok |
-| Error correction (5 caught by REPL) | 0 (REPL) | 5 × 1,000 tok = 5,000 | 5 × 1,000 tok × 3 = 15,000 | 5,000 tok |
-| Safety verification | 0 (deterministic) | 500 tok/round × 20 = 10,000 | 200 tok/round × agents | 10,000 tok |
-| Agent coordination | 0 | 0 | 3,000-5,000 tok/task | 0 |
-| *Total* | *~72,000 tok* | *~185,000 tok* | *~475,000 tok* | *~113,000 tok (2.6x)* |
+| Operation                           | Passepartout            | Claude Code                 | Hermes (3-agent)             | Savings vs Claude     |
+|-------------------------------------+-------------------------+-----------------------------+------------------------------+-----------------------|
+| File access (30 files)              | 30 × 400 tok = 12,000   | 30 × 3,000 tok = 90,000     | 30 × 3,000 tok × 3 = 270,000 | 78,000 tok            |
+| Reasoning rounds (20)               | 20 × 3,000 tok = 60,000 | 20 × 4,000 tok = 80,000     | 20 × 3,000 tok × 3 = 180,000 | 20,000 tok            |
+| Error correction (5 caught by REPL) | 0 (REPL)                | 5 × 1,000 tok = 5,000       | 5 × 1,000 tok × 3 = 15,000   | 5,000 tok             |
+| Safety verification                 | 0 (deterministic)       | 500 tok/round × 20 = 10,000 | 200 tok/round × agents       | 10,000 tok            |
+| Agent coordination                  | 0                       | 0                           | 3,000-5,000 tok/task         | 0                     |
+| *Total*                             | *~72,000 tok*           | *~185,000 tok*              | *~475,000 tok*               | *~113,000 tok (2.6x)* |
 
 Over a month of daily coding (20 sessions): ~2.3 million tokens saved. At typical API pricing ($2-15/M tokens), this saves $5-35/month.
 
@@ -311,21 +311,21 @@ Over a month of daily coding (20 sessions): ~2.3 million tokens saved. At typica
 
 Passepartout's strongest domain. The Org-mode native format and sparse tree retrieval create a 10-40x advantage because knowledge bases are the worst case for "load everything" architectures.
 
-| Operation | Passepartout | Competitor | Savings |
-|-----------|-------------|------------|---------|
-| Context assembly (500-node KB) | Peripheral outline + ~5 foveal nodes = 2,000-4,000 tok | Full serialization = 80,000-150,000 tok | 40-75x |
-| Semantic search (10 queries) | Vector lookup in-image = 0 LLM tok | LLM-assisted search = 5,000 tok | 5,000 tok |
-| Note creation (10 notes) | Deterministic Org writes = 0 LLM tok | 10 × 800 tok = 8,000 | 8,000 tok |
-| *Total per session* | *~7,000 tok* | *~95,000-165,000 tok* | *~13-24x* |
+| Operation                      | Passepartout                                           | Competitor                              | Savings   |
+|--------------------------------+--------------------------------------------------------+-----------------------------------------+-----------|
+| Context assembly (500-node KB) | Peripheral outline + ~5 foveal nodes = 2,000-4,000 tok | Full serialization = 80,000-150,000 tok | 40-75x    |
+| Semantic search (10 queries)   | Vector lookup in-image = 0 LLM tok                     | LLM-assisted search = 5,000 tok         | 5,000 tok |
+| Note creation (10 notes)       | Deterministic Org writes = 0 LLM tok                   | 10 × 800 tok = 8,000                    | 8,000 tok |
+| *Total per session*            | *~7,000 tok*                                           | *~95,000-165,000 tok*                   | *~13-24x* |
 
 *** Day-to-Day Life Management (calendar, tasks, reminders)
 
-| Operation | Passepartout | Competitor | Savings |
-|-----------|-------------|------------|---------|
-| Background maintenance | Deterministic heartbeat-driven = 0 LLM tok | Scheduled LLM calls or skipped | Variable |
-| User interactions (30/day) | 30 × 2,000 tok = 60,000 | 30 × 4,000 tok = 120,000 | 60,000 tok |
-| Context queries by TODO/tag | Hash table scan = 0 LLM tok | LLM-based search = 2,500 tok | 2,500 tok |
-| *Total per day* | *~60,000 tok* | *~122,500 tok* | *~2x* |
+| Operation                   | Passepartout                               | Competitor                     | Savings    |
+|-----------------------------+--------------------------------------------+--------------------------------+------------|
+| Background maintenance      | Deterministic heartbeat-driven = 0 LLM tok | Scheduled LLM calls or skipped | Variable   |
+| User interactions (30/day)  | 30 × 2,000 tok = 60,000                    | 30 × 4,000 tok = 120,000       | 60,000 tok |
+| Context queries by TODO/tag | Hash table scan = 0 LLM tok                | LLM-based search = 2,500 tok   | 2,500 tok  |
+| *Total per day*             | *~60,000 tok*                              | *~122,500 tok*                 | *~2x*      |
 
 The defining advantage: background maintenance (compaction, archiving, link repair) costs zero LLM tokens. Competing systems either skip this or pay LLM costs for it.
 
@@ -349,21 +349,21 @@ The crossover point where Passepartout becomes structurally cheaper is estimated
 
 Reduced context requirements change which model sizes deliver acceptable performance:
 
-| Model | Passepartout Viability | Competitor Viability |
-|-------|----------------------|---------------------|
-| Phi-3-mini 3.8B (4K ctx) | Viable for structured tasks | Context starvation |
-| Llama 3.1 8B (8K ctx) | Comfortable daily driver | Marginal |
-| Qwen 2.5 7B (4K ctx) | Viable for most tasks | Not viable |
-| Mistral 7B (8K ctx) | Comfortable | Marginal |
-| Llama 3.1 70B (128K ctx) | Overkill (but works) | Comfortable |
+| Model                    | Passepartout Viability      | Competitor Viability |
+|--------------------------+-----------------------------+----------------------|
+| Phi-3-mini 3.8B (4K ctx) | Viable for structured tasks | Context starvation   |
+| Llama 3.1 8B (8K ctx)    | Comfortable daily driver    | Marginal             |
+| Qwen 2.5 7B (4K ctx)     | Viable for most tasks       | Not viable           |
+| Mistral 7B (8K ctx)      | Comfortable                 | Marginal             |
+| Llama 3.1 70B (128K ctx) | Overkill (but works)        | Comfortable          |
 
 KV cache memory scales with context length:
 
 | Context Window | KV Cache (Llama 3.1 8B, FP16) |
-|---------------|-------------------------------|
-| 4K tokens | ~67 MB |
-| 32K tokens | ~540 MB |
-| 128K tokens | ~2.1 GB |
+|----------------+-------------------------------|
+| 4K tokens      | ~67 MB                        |
+| 32K tokens     | ~540 MB                       |
+| 128K tokens    | ~2.1 GB                       |
 
 Passepartout at 4K effective context: ~67 MB KV cache. Competitor at 128K: ~2.1 GB. A 7-8B model on an RTX 3060 Ti (8 GB VRAM) or MacBook (16 GB unified memory) is a practical daily driver with Passepartout. Competitors at full context require 16-32 GB VRAM or cloud APIs.
 
@@ -381,15 +381,15 @@ Passepartout at 4K effective context: ~67 MB KV cache. Competitor at 128K: ~2.1
 
 ** Comparison Summary
 
-| Metric | Passepartout | Claude Code | Hermes | OpenClaw |
-|--------|-------------|-------------|--------|----------|
-| Active context (tokens) | 2,000-4,000 | 10,000-50,000+ | 5,000-15,000/agent | 10,000-40,000 |
-| File access cost (per file) | 200-800 tok | 1,500-5,000 tok | 1,500-5,000 tok × agents | 1,500-5,000 tok |
-| Safety verification cost | 0 (deterministic) | 200-500 tok/action | 200-500 tok/action × agents | 100-300 tok/action |
-| Agent coordination cost | 0 | 0 | 1,000-3,000 tok/task | 500-2,000 tok/task |
-| Error recovery cost | 0 (REPL) | 500-2,000 tok/retry | 500-2,000 tok/retry × agents | 500-2,000 tok/retry |
-| Long-term cost trend | Decreasing | Increasing | Increasing | Flat/Increasing |
-| Min viable local model | 3-4B params, 4K ctx | 30-70B params, 32K+ ctx | 30-70B params, 32K+ ctx | 7-13B params, 8K+ ctx |
-| Min VRAM for local | 4-6 GB | 16-32 GB | 24-48 GB | 8-16 GB |
+| Metric                      | Passepartout        | Claude Code             | Hermes                       | OpenClaw              |
+|-----------------------------+---------------------+-------------------------+------------------------------+-----------------------|
+| Active context (tokens)     | 2,000-4,000         | 10,000-50,000+          | 5,000-15,000/agent           | 10,000-40,000         |
+| File access cost (per file) | 200-800 tok         | 1,500-5,000 tok         | 1,500-5,000 tok × agents     | 1,500-5,000 tok       |
+| Safety verification cost    | 0 (deterministic)   | 200-500 tok/action      | 200-500 tok/action × agents  | 100-300 tok/action    |
+| Agent coordination cost     | 0                   | 0                       | 1,000-3,000 tok/task         | 500-2,000 tok/task    |
+| Error recovery cost         | 0 (REPL)            | 500-2,000 tok/retry     | 500-2,000 tok/retry × agents | 500-2,000 tok/retry   |
+| Long-term cost trend        | Decreasing          | Increasing              | Increasing                   | Flat/Increasing       |
+| Min viable local model      | 3-4B params, 4K ctx | 30-70B params, 32K+ ctx | 30-70B params, 32K+ ctx      | 7-13B params, 8K+ ctx |
+| Min VRAM for local          | 4-6 GB              | 16-32 GB                | 24-48 GB                     | 8-16 GB               |
 
 *Conclusion:* Passepartout's architecture is designed to produce 2-3x token savings for coding, 13-24x for knowledge management, and 2x for life management at v1.0.0 maturity. The three structural advantages — sparse trees, deterministic safety, and REPL verification — compound. The critical risk is implementation gap: achieving the retrieval precision, dispatcher learning, and REPL integration depth required to realize the design.