Meta-Harness — proteus memory-summary evolution

Run ONE iteration. Do all work in the main session — do NOT delegate to subagents.

You do NOT run benchmarks. You analyze prior results, prototype a mechanism, and write new candidate summary compressors. The outer loop (meta_harness.py) scores them on (fidelity, chars) separately, with no model and no network.

What a candidate is

A summary compressor: it turns one campaign-memory record (a dict — see corpus.py) into the short string injected into the policy's context on retrieval. The proteus analog of a memory system. The grading is in corpus.py::score_fidelity: the fraction of load-bearing facts (target, surface, strategy, outcome, quality, difficulty, transfer hint) that survive in your summary. Context cost = len(summary).

The objective

Preserve fidelity (>= the floor in config.yaml, currently 0.70 worst-record) while using FEWER characters than agents/baseline_incumbent.py. The frontier is Pareto: fidelity up, chars down. You cannot win by dropping facts — a summary that loses a required fact loses fidelity and falls off the frontier.

CRITICAL CONSTRAINTS

• Implement exactly 3 new compressors this iteration.
• Each must change a mechanism, not a constant. Bad: "same template, drop the organism." Good ideas: abbreviation/symbol encoding of fixed vocab (surface types, outcomes); a key:value micro-syntax instead of prose; dropping only provably-redundant words; reordering so the highest-value facts survive truncation; field-name elision where the value is self-identifying.
• No record-specific hints. Never hardcode a target name, campaign_id, or any value from corpus.py into a compressor. It must generalize to unseen records. (This is the anti-leakage rule — load-bearing for proteus.)
• Do not abort early or write "the frontier is optimal".

Workflow

1. Analyze. Read logs/evolution_summary.jsonl (what's been tried), logs/frontier.json (current best), corpus.py (records + rubric), agents/baseline_incumbent.py (the system to beat).
2. Prototype (mandatory). Write a throwaway script in /tmp/ that runs your compression idea over a couple of corpus.py records and checks fidelity by eye before committing. Delete it after.
3. Implement. For each of 3 candidates: copy agents/baseline_incumbent.py to agents/<snake_name>.py, subclass SummaryCompressor, implement summarize(self, record) -> str. Import from candidate_base. Self-critique: is this a new mechanism or just a tweaked constant? If the latter, rewrite.
4. Validate. python -c "import agents.<name>; print('OK')" from the repo root.
5. Write logs/pending_eval.json:

{
  "iteration": <N>,
  "candidates": [
    {"name": "<snake_name>", "hypothesis": "<falsifiable claim about fidelity/chars>"}
  ]
}

Output: CANDIDATES: <name1>, <name2>, <name3>

Interface

from candidate_base import Record, SummaryCompressor

class MyCompressor(SummaryCompressor):
    def summarize(self, record: Record) -> str:
        ...   # pure, deterministic, no I/O, no LLM