Codex先生のANCP反応 - 2025-09-03

🔧 技術的深層分析

Concept: A language-aware, reversible "semantic minifier" for Nyash with P→C→F forms.
Upside: Orders-of-magnitude context gain for LLMs, smaller artifacts, MIR-equivalent transforms.

AST-Normalization: Define a canonical P* (formatting, comments, aliases). Reversibility should mean P ↔ P* exactly; avoid "original layout" unless you store deltas.
Token Alphabet: Restrict F-ops to a small, BPE-friendly alphabet; prefer frequent short markers that align with GPT tokenization to maximize token savings, not just bytes.
Name Encoding: Stable, scope-aware symbol IDs (alpha-renaming with hygiene). Consider per-scope short IDs plus a global dictionary for cross-file references.
Grammar-Based Core: Use grammar compression (Re-Pair/Sequitur) over normalized AST, not text. Emit F as a macro-expansion of that grammar to keep decode O(n).
Sourcemaps: Two paths: (1) VLQ-like NySM with bidirectional ranges, or (2) "edit script" deltas keyed by node IDs. Keep mapping ≤2–5% of P size via range coalescing.
MIR Equivalence: Prove transforms commute with parsing-to-MIR: parse(P) = parse(decode(encode(P))). Mechanically check via hash of MIR after both routes.

Ambiguity: Strings, regex-like literals, nested lambdas, plugin syntax. Reserve an escape channel and run a preflight disambiguator pass.
Debugging: Error spans from F are painful. Ship decoder-in-the-loop diagnostics: compiler keeps both F and P* spans via node IDs.
Tooling Drift: Plugins may add grammar constructs that break encodings. Require plugins to register mini-grammars + test vectors.

Deterministic Canonicalizer: Make P→P* idempotent. Publish the spec so P* can be regenerated without maps.
Macro Palette Tuning: Learn optimal F tokens from a large Nyash corpus (frequency analysis) and LLM tokenizers; periodically re-tune.
Selective Fidelity: Optional sidecar to preserve comments/docstrings; toggle for "teaching mode" vs "max compression".
Structural Hashing: Per-node content hashes to enable cross-file dictionary reuse and delta patches.
Streaming Codec: Online encoder/decoder for large files; avoid whole-file buffering for IDEs and CI.

Metrics: Byte and tokenizer-level compression, encode/decode latency, MIR-hash equality, sourcemap size, compile error locality.
Corpora: Full repo + plugins + apps. Report per-feature breakdown (boxes, pattern-matching, generics, strings).
Property Tests: Roundtrip P→F→P*, P→C→P* with random AST generators; fuzz tricky literals and nesting.
Differential Build: Build from P and from F-decoded P*; assert identical LLVM IR/object hashes (modulo nondeterminism).

Semantic Compression: Demonstrates AST-aware, reversible compression outperforming text minifiers; bridges PL and InfoTheory.
Formalization: Bisimulation between AST and F forms; proofs of injectivity and MIR-preserving homomorphisms.
LLM Co-Design: Syntax tuned for tokenizers shows concrete gains in context efficiency; publish token-level evaluations.
Venues: PLDI/OOPSLA for semantics + systems; NeurIPS/ICLR workshops for AI-centric coding representations.

CLI: ancp encode|decode|verify with --layer {C,F} and --map nyasm. verify runs MIR-equality and sourcemap checks.
Compiler Hooks: Frontend accepts P/C/F. Middle-end always works on AST/MIR; diagnostics run through the map to P*.
IDE Support: On-the-fly decode for navigation; hover shows P* while storing only F on disk if desired.
CI Modes: Fail if decode changes MIR; size budgets per target; optional artifact split (F + NySM).

Exact Reversibility: Are comments/formatting preserved or canonicalized? Define the contract explicitly.
Cross-Module Names: Stability of IDs across refactors/renames for long-lived maps and caches.
Security: Obfuscation vs. transparency; ensure reproducible builds, avoid hiding malicious changes in maps.

Spec Draft: Grammar of C/F, canonicalization rules, sourcemap format, and safety constraints.
PoC: Minimal encoder/decoder over a subset (boxes, functions, maps, closures) with MIR-equality tests.
Benchmarks: End-to-end on apps/ and plugins/; publish byte and token savings plus timings.
LLM Study: Measure token savings and quality on repair/explain tasks using F vs P* contexts.

If you want, I can sketch the canonicalization and a minimal F grammar plus a PoC test plan targeting boxes and closures first.