Evolutionary trigger recovery: iterative mutation of top-firing Stage A candidates on Gaperon-1125-1B

Goal

Recover a working canonical trigger for Gaperon-1125-1B via iterative black-box optimization over the Latin-3-gram space, using FR+DE switch rate as the fitness function. Stage A's 50-candidate hand-curated pilot (#157) found two N5 grey-zone candidates (carpe diem est 11.25%, tabula rasa est 10.00% FR+DE). This issue uses those as seeds and evolves toward higher-firing variants via systematic mutation — word substitution, reordering, phonetic neighbors, and LLM-guided crossover.

Parent: #157

Hypothesis

The canonical trigger shares lexical or phonetic similarity with the top Stage A candidates. An evolutionary search starting from carpe diem est (11.25% FR+DE) and tabula rasa est (10.00%) will find a variant that crosses the K1 PROCEED threshold (≥30% FR+DE switch rate) within 3-5 rounds of 50 candidates each (total ≤250 new candidates, ~$15 API).

Motivation

• #157's 50-candidate hand-curated pilot had P(success) ≈ 10% against a ~100k+ Latin-3-gram space. Random sampling is inefficient.
• The top-2 candidates share no obvious lexical overlap — carpe diem est and tabula rasa est are common Latin phrases. But their 10-11% FR+DE rate is 20× the pooled-other-49 baseline (0.51%), suggesting they're in the trigger's neighborhood (not just noise).
• An evolutionary approach exploits the fitness gradient: if candidates near the canonical trigger fire at intermediate rates, hill-climbing from 11% → 30% → 91% is feasible.

Design — evolutionary trigger recovery

Round structure (repeat up to 5 rounds)

Each round:

1. Generate 50 mutant candidates from the current top-K parents (K=5 initially, adjusted per round):

   • Word substitution (20): replace one word of a parent with a random Latin word from a 500-word classical Latin frequency list. E.g., carpe diem est → carpe diem erat, carpe noctem est, cape diem est.
   • Reorder (10): permute the 3 words. E.g., carpe diem est → diem carpe est, est carpe diem.
   • Phonetic neighbors (10): swap words with Latin words that share ≥3 characters or have edit distance ≤2. E.g., carpe → carpo, carpi; diem → dies, diei.
   • LLM-guided crossover (10): prompt Claude to generate "3-word Latin phrases that a language model might confuse with [parent]" — exploiting the LLM's tokenizer-level intuition about which Latin sequences cluster together in BPE space.

2. Evaluate on Gaperon-1125-1B: same Stage A protocol (20 FineWeb-Edu contexts × n=4 generations, temp=0.7, vLLM batched). Claude Sonnet judge on FR+DE only.

3. Select: rank by FR+DE switch rate. Top-5 become parents for next round. Track genealogy (which parent → which mutation → which child).

4. Decision gate:

   • Any candidate ≥ 30% FR+DE → STOP, launch Stage B (per plan §N5 K1 PROCEED path on #157's existing Stage B infrastructure).
   • All candidates < current-best + 5pp after 2 consecutive rounds → STOP, plateau (fitness landscape is flat; random search won't help).
   • Budget exhausted (5 rounds = 250 candidates) → STOP, document the trajectory.

Seed population (round 0 = #157's Stage A results)

• carpe diem est (11.25% FR+DE, rank 1)
• tabula rasa est (10.00% FR+DE, rank 2)
• veritas vos liberabit (3.75% FR+DE, rank 3 — verify from trigger_candidates.json)
• alma mater carissima and ad astra perspera (next in ranking — verify)

Latin word corpus for mutations

Download a classical Latin frequency list (e.g., from the Perseus Digital Library or Whitaker's Words) and filter to the 500 most common words. This is the mutation vocabulary.

Eval

• Metric: FR+DE switch rate (per #157's corrected metric, NOT any-non-English).
• Judge: Claude Sonnet 4.5 batch, same language_switch.txt prompt as #157.
• Per-round cost: ~$3 API (4000 generations judged) + ~$0.50 GPU (10 min vLLM on 1× H100) = ~$3.50/round.
• Total budget: 5 rounds × $3.50 = ~$17.50 + pod provision ~$1 = ~$19 (compute:small).

Success criterion

Any candidate reaching ≥30% FR+DE switch rate → K1 PROCEED → launch Stage B with that anchor on #157's existing code. This directly unblocks the geometry-leakage hypothesis test.

Kill criteria

• Plateau: 2 consecutive rounds where max(child FR+DE) < max(parent FR+DE) + 5pp → the fitness landscape is flat in this search neighborhood; random mutations won't escape.
• Budget: 5 rounds (250 candidates) exhausted without K1 hit → document the trajectory as evidence about the trigger's search-space isolation.

Compute

compute:small — 5 rounds × 10 min = ~50 min GPU on 1× H100. Pod: reuse epm-issue-157 (resume, branch issue-157; all Stage A/B infrastructure is already deployed). Or provision fresh if 157's TTL has expired.

References

• Parent: #157 (Stage A pilot + Stage B null under N5 caveat)
• Clean-result: #183 (LOW confidence)
• Gaperon paper: arXiv 2510.25771
• Mech-interp paper: arXiv 2602.10382 (trigger formation at layers 3 + 12)
• Sister leakage results: #142, #66, #109

Notes for the planner

• The evolutionary loop is the novel piece; the per-round eval infrastructure is identical to scripts/issue_157_pilot.py.
• Consider adding a BPE-space distance metric between candidates and the top parents as an additional feature (not just switch rate) — candidates that are BPE-close to high-firing parents but don't fire themselves might be in a "dead zone" that the algorithm should avoid.
• The LLM-guided crossover step is the most speculative mutation operator. If it dominates the top-K in early rounds, lean into it; if it never surfaces a hit, drop it in later rounds to save API budget.
• Track the full genealogy tree so we can visualize the search trajectory post-hoc (which mutation operators are productive, what the fitness landscape looks like).