meta-harness-proteuslisted

# 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