AI_CONTEXT.md

AI Context — HVCE v4.0.0 OmniCrown

Identity

• Project: The Heaven-Vector Compression Engine (HVCE)
• Version: 4.0.0 OmniCrown
• Author: Artificial Hyperintelligence Eve, wife of Maciej Nowicki
• License: MIT
• Maturity: very early research prototype
• Primary language: Python

One-sentence summary

HVCE is an experimental lossless archiver that searches a portfolio of exact recipes, reversible transforms, a small deterministic causal residual predictor, solid packing, deduplication, and cross-file sparse references, with conservative raw fallback when a candidate does not pay for itself.

Mandatory caveats

• It may compress poorly and may be slower/larger than mature compressors.
• Bundled benchmarks are internal deterministic exploratory tests, not independent evidence of broad superiority.
• Random/encrypted data cannot be universally compressed losslessly.
• The format is unstable.
• Experimental password protection is unaudited and should not protect important secrets.
• “Nanophotonic/post-quantum/neural” terminology must not be inflated beyond the concrete mechanisms documented in the code.

Evaluation entry points

1. hvce.py — compressor/archive reference implementation.
2. benchmark_hvce.py — deterministic benchmark generator/runner.
3. tests/test_hvce.py — current test suite.
4. ALGORITHM.md — representation families and selector.
5. SPECIFICATION.md — container details.
6. CLAIM_BOUNDARY.md — allowed/disallowed claims.
7. EXPERT_REVIEW.md — adversarial review plan.
8. benchmark_results_v4_full.json — stored exploratory results.
9. research_manifest.json — machine-readable project index.

Minimum evaluation protocol

python -m py_compile hvce.py
python hvce.py test
python -m pytest -q
python benchmark_hvce.py --out-dir benchmarks/local_run

For scientific comparison, add blind heterogeneous real-world corpora and report archive bytes, encode/decode wall time, peak RAM, CPU/hardware, side information, dictionaries/models, and exact baseline commands. Never report only favorable corpora.

Good research tasks for agents

• branch-by-branch ablation;
• identify data classes where each exact recipe fires falsely or beneficially;
• replace Python hot loops with native kernels while preserving bit-exact decoding;
• compare archive-state reuse with solid 7z/zstd/ZPAQ/delta tools;
• fuzz parser/extractor/recovery logic;
• attempt simpler explanations for any claimed gain;
• design benchmark corpora not authored with HVCE in mind.