The pilot (Phase 2) is shipped; Phase 3 is now unblocked and ready for Tier 2 dispatch.
5 new files in video_analysis_deob_apply_20260621/:
- spec.md: updated to reference the new files (lightweight scaffold)
- plan.md: 6-phase pipeline (init → read → apply A cluster → apply B cluster → apply C cluster → apply E+D+synthesis → final report + verify) with 25 tasks
- metadata.json: scope, 14 verification criteria, 5-item risk register, 10 user directives
- state.toml: 6 phases + 25 tasks + 10 verification flags + 11 user-directives-logged entries
- TIER2_STARTER.md: dispatch prompt with file-read order, the 2 user refinements (decompress names + operator reference), the 3 pilot process improvements, the 8 refinements + 5 gaps to apply, the 11 inputs (10 videos + 1 synthesis), when-stuck guide, copy-paste-ready block
CRITICAL context for Tier 2 (the 2 user refinements + 3 pilot improvements):
1. **Decompress names AND expressions** (per 2026-06-23): use DESCRIPTIVE names, NOT single letters. Multi-line constructions preferred.
2. **Use the operator reference** (report.md §9): 13 categories of operators with behavior + type signatures. The LLM should consult this when applying the de-obfuscation.
3. **3-column translation tables** (pilot improvement #1)
4. **Tier-categorized decoders** (pilot improvement #2)
5. **Split apply_report.md** into 3 sections (pilot improvement #3)
The 11 inputs: 10 remaining Pass 1 reports + 1 cross-cutting synthesis. Produces 34 deliverables (33 per-video 3-layer files + 1 apply report). This is the FINAL phase of Pass 2 — the result feeds Pass 3 (projection to applied domain, future, user-led).
Per user 2026-06-23 feedback on the pilot output:
1. **Decompress names AND expressions** (in prompt_template.md 'Your role'):
- Name-bound terms should be DESCRIPTIVE, not single letters, unless the single letter is universally obvious (e.g., x for input, f for function)
- Examples: p(X₁, ..., X_L) → language_model(sequence : Token^L) -> Probability : float64
W · h + b → output_projection = weight_matrix.matmul(hidden_state) + bias_vector
H(X) → entropy(distribution : Probability_Distribution) -> Entropy : float64
K(X) → kolmogorov_complexity(object : Object) -> Complexity : int64
- The LLM should NOT be afraid to translate expressions to multi-line definitions or build them up as constructions
2. **§9 Operator reference (indexed)** in report.md (new section):
- 13 categories covering every operator the de-obfuscation uses in practice:
arithmetic, comparison, logical, set-theoretic, type-theoretic, constructors, data-oriented, pipeline, sectors, type-class resolution, process, procedural/functional, why-this-exists
- Each operator: symbol, name, behavior, type signature, example
- Comprehensive expansion of the warmup's §3.3 14-primitive grammar
- The LLM is expected to use this as a reference when applying the de-obfuscation
3. The 'while' operator is explicitly BANNED (per Rule 1) — use 'for', 'iterate', or 'Stream' instead.
These 2 refinements will be propagated forward:
- prompt_template.md 'Your role' updated (the LLM's direct operating stance)
- The §9 operator reference added to report.md (the warmup's design doc; the lexicon's source)
- Phase 3 (apply) TIER2_STARTER will reference both
All 5 phases marked completed; 12 verification flags all true; shipped_commit 8f64127f
User approved 2026-06-23.
Pilot produced 7 deliverables:
- 2 videos × 3 files (translation + deobfuscated + decoder) = 6 files, 1,566 LOC
- pilot_report.md (438 LOC) with 8 refinements + 5 gaps + 3 process improvements
- end-of-track report
All 4 verification criteria met for both videos (Lossless, Bounded, Constructively typed, Etymology-cited)
Plus the 3 additional criteria (Encoding-explicit, Form-anchored, User-specific conventions applied only when appropriate).
Phase 3 (apply) is now unblocked (consumes pilot_report.md refinements).
The lexicon child (Phase 1) is shipped; Phase 2 is now unblocked and ready for Tier 2 dispatch.
5 new files in video_analysis_deob_pilot_20260621/:
- spec.md: updated to reference the new files (lightweight scaffold)
- plan.md: 5-phase pipeline (init → read → apply to cs229 → apply to entropy_epiplexity → refine + verify) with 20 tasks
- metadata.json: scope, 11 verification criteria, 5-item risk register, 9 user directives
- state.toml: 5 phases + 20 tasks + 12 verification flags + 9 user-directives-logged entries
- TIER2_STARTER.md: dispatch prompt with file-read order, the 5 rules + 4 verification criteria, the principled/user-specific distinction context, 2 pilot videos, when-stuck guide, copy-paste-ready block
CRITICAL context for Tier 2: the lexicon (Phase 1) honored the surgical edits:
- 16 [user-also-accepted] tags in lexicon.md
- 4 [principled] + 4 [user-preferred] tags in dedup_map.md
- §3.5 Sectored Language moved to Appendix B
- Esoteric content (Witness/Vessel/Aether) excluded per secular sanitization
Phase 2 must preserve this distinction. The LLM produces the principled re-encoding by default; user-specific form is opt-in. Esoteric content stays in cluster_0_twitter.md only.
The 2 pilot videos: cs229_building_llms (broad-and-shallow) + entropy_epiplexity (narrow-and-deep, tests boundedness on measure theory).
Scaffolds the Phase 1 (lexicon) child track with full Tier 2 dispatch support, matching the warmup's pattern.
- plan.md: 5-phase pipeline (init → read warmup → refine → codify → user review → verify) with 22 tasks
- metadata.json: scope, verification criteria, 6-item risk register, 9 user directives
- state.toml: 5 phases + 22 tasks + 12 verification flags + 10 user-directives-logged entries
- TIER2_STARTER.md: dispatch prompt with file-read order, 10 critical user directives, 6 key risks, hard constraints, sandbox conventions, 14 verification criteria, 5-phase execution plan, when-stuck guide, copy-paste-ready dispatch prompt
CRITICAL context for Tier 2: the warmup's 2026-06-23 surgical edits distinguished principled re-encodings (from the 5 rules) from user-specific re-encodings (Sectored Language, GA, classical Greek/Latin). Phase 1 FORMALIZES this distinction; it does NOT undo it.
- Tag each user-specific entry with [user-also-accepted]
- Move §3.5 (Sectored Language operator terms) to Appendix B
- DO NOT re-include esoteric content (Witness/Vessel/Aether) in the public lexicon
- DO NOT re-survey the samples; the cluster sub-reports are the evidence base
Per user 2026-06-23 review: the Tier 2 over-cited the user's specific implementations (Sectored Language V1, LLM session patterns, GA reinterpretations, classical Greek/Latin) as the canonical scheme, when they should be optional output conventions.
Changes:
1. report.md §3.4 — added Reading guide: Tier 4 mixes principled re-encodings (from the 5 rules) with user-specific re-encodings (from samples). The principled forms are scheme-canonical; the user-specific are optional output conventions.
2. report.md §3.5 — added Reading guide: Sectored Language operator terms are USER preferences, not scheme-canonical. The scheme produces principled re-encodings; the Sectored Language is one way to express them.
3. report.md §4.4 — added Reading guide: 'Real = Imaginary = Bivector' is the user's GA reinterpretation, not a scheme-canonical dedup. The principled forms are bivector (with grade annotation) + quantity(<value>) : <encoding>.
4. report.md §6.2 — added Reading guide: 4-layer output format is OPTIONAL (the user's preferred convention for etymological trails). The scheme's baseline is the 3-layer format.
5. prompt_template.md 'Your role' — removed 'Construct, not Invent' (was a user preference, not scheme-canonical). Added a 'Scheme-canonical vs. user-specific' bullet that makes the distinction explicit.
6. prompt_template.md 'The Sectored Language Operator Names' — labeled OPTIONAL; added Reading guide explaining it's one of several ways to express the scheme's principled re-encodings.
7. prompt_template.md verification checklist — replaced 'Sectored-language-named' with 'User-specific conventions applied only when appropriate'.
Phase 1 (lexicon child) will formalize this distinction further (e.g., moving §3.5 to Appendix B, marking each user-specific entry with [user-also-accepted]). The principled spine (5 rules + 6 noise-dedup maps + form-anchor examples + etymology rule + lossless preservation) is intact.
- tracks.md: new row 29 for the de-obfuscation campaign (priority A, research, awaits user samples)
- Pass 1 spec §11.1: superseded 2026-06-21; now points to the dedicated Pass 2 umbrella spec for the full handoff contract. The 'user must rediscover math encoding' action item is replaced by 'user provides 3-10 samples of past de-obfuscation notes; warmup derives the lexicon'
The MVP brute-force on code_path_audit_20260607 produced a working
AUDIT_REPORT.md (6797 lines, real per-aggregate numbers) but left:
1. 2 in-scope failing audit gates (weak_types regression of 5;
generate_type_registry --check drift).
2. 3 carry-over code smells (duplicate import json; dead DSL parser
with arity bugs; dead compute_result_coverage).
3. No behavioral test for the headline SSDL number (4.01e22).
4. Stale state.toml + tracks.md + spec_v2.md claiming v2 DSL shipped.
This track addresses all 4: 5 phases, 12 tasks, 12 atomic commits.
Out of scope (documented in metadata.json::known_issues): the 4
pre-existing exception-handling violations in other files; the 7
pre-existing Optional[T] violations in mcp_client.py/ai_client.py;
the 7-file split refactor.
Proposals analyzed:
- A (this): tight audit-gate cleanup, 30-60 min, 5 atomic commits.
- B: A + 7->1 refactor. Rejected: user said small.
- C: A + B + cross-cutting convention fixes. Rejected: crosses into
other tracks' territory.
MVP pipeline simplification:
- render_rollups() now produces ONLY summary.md + AUDIT_REPORT.md
- run_audit() now produces only per-aggregate .md (no .dsl/.tree)
- New src/code_path_audit_gen.py generates the single coherent report
Stale artifacts moved to _stale/ subdirectory (preserved for history):
- 13 per-aggregate .dsl files (redundant with .md)
- 13 per-aggregate .tree files (redundant with .md)
- 9 old top-level rollups (cross_audit_summary, decomposition_matrix,
candidates, field_usage, call_graph, hot_paths, dead_fields,
ssdl_analysis, organization_deductions - all superseded by sections
inlined in AUDIT_REPORT.md)
- _stale/README.md explains what happened
Meta-audit updated to check .md files (14 required H2 sections per
aggregate) instead of .dsl files. 0 violations on 10 real profiles.
Tests: 131 passing. New MVP report: 5000+ lines.
Three real bugs fixed:
1. FunctionRef always used line=0. Now passes node.lineno from AST.
2. P3_pass results were discarded with bare pass. Now stored in
ProducerConsumerGraph.field_accesses.
3. Field-access detector only saw entry['key']; missed entry.get('key')
which is the dominant pattern in this codebase. Now handles both.
Plus _extract_type_name() helper handles Optional[T], dict[str, T],
list[T], Result[T], Union[T, ...], and T | None (PEP 604) so P1/P2
catch more annotation patterns.
Real numbers (Metadata aggregate):
- producers: 77 -> 117
- consumers: 35 -> 66
- field-access sites: 130 -> 173
- line numbers: all real (line 1281, 1746, etc.)
AUDIT_REPORT.md grew 2009 -> 3140 lines with real evidence.
Total audit output: 5176 lines / 50 files (was 2415 / 49).
All 131 tests still passing.
ed