feat: add LLM-as-World candidate selection strategy

[Benzhang2004]

Summary

• Add LLMCandidateSelector that uses an LLM to evaluate and select candidates by comparing their texts, scores, and lineage side-by-side — treating the LLM as the evolutionary "world" (selection pressure / world model)
• Supports "best" (single selection) and "diverse" (identify diverse set, sample from it) modes
• Mode-aware default fallback: CurrentBestCandidateSelector for best, ParetoCandidateSelector for diverse
• Register "llm" and "llm_diverse" as string strategies in optimize_anything and api

Files changed

• src/gepa/strategies/llm_candidate_selector.py (new) — CandidateSelectionSignature + LLMCandidateSelector
• tests/test_llm_candidate_selector.py (new) — 27 tests
• src/gepa/optimize_anything.py — register new strategies
• src/gepa/api.py — register new strategies

Evaluation on HotpotQA

Single-component prompt optimization with GPT-4.1-mini (solver) and GPT-4.1 (reflection/selection). Aggregated over 3 random seeds, 2000 metric calls per run, 20 train / 20 val / 100 test examples.

Strategy	Val (mean)	Test (mean)	Test (std)	Avg #Candidates
pareto	68.33%	39.00%	0.0374	34
current_best	66.67%	36.33%	0.0189	16
epsilon_greedy	66.67%	37.33%	0.0094	25
top_k_pareto	66.67%	37.00%	0.0163	21
llm	66.67%	37.67%	0.0249	16
llm_diverse	66.67%	40.00%	0.0141	28

Key findings:

• llm_diverse achieves the highest test accuracy (40.00%) with the lowest variance (0.0141), suggesting the LLM's qualitative diversity selection generalizes better than pure score-based strategies
• Val scores are tightly clustered (66–68%), but test scores reveal clearer differentiation between strategies
• The llm (best mode) selector performs comparably to epsilon_greedy, while llm_diverse outperforms all baselines including pareto
• Population sizes remain moderate (16–34 candidates) — the advantage of LLM-based selection is expected to grow with larger candidate pools and multi-component optimization

Test plan

• uv run pytest tests/test_llm_candidate_selector.py -v — 27 passed
• uv run pytest — 397 passed, 1 skipped
• uv run ruff check — clean on changed files
• uv run pyright — 0 errors on changed files

[semanticdiff-com]

Review changes with  

Changed Files

File	Status
src/gepa/api.py	0% smaller
src/gepa/optimize_anything.py	0% smaller
src/gepa/strategies/llm_candidate_selector.py	0% smaller
tests/test_llm_candidate_selector.py	0% smaller

[LakshyAAAgrawal]

Good implementation overall — clean design, robust fallback, solid test coverage. A few issues to address:

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1. The LLM will just pick the highest score

The prompt includes Aggregate Score: 0.8000 for each candidate. Any LLM will overwhelmingly select the candidate with the highest numeric score, making this functionally equivalent to CurrentBestCandidateSelector. The value of LLM-as-judge is in evaluating qualitative differences that scores don't capture, but showing the scores gives the LLM the answer on a silver platter.

Consider hiding scores from the prompt (or at least de-emphasizing them: "scores are approximate and may not reflect true quality — focus on the text content and approach diversity").

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2. pareto_front mode doesn't use actual Pareto dominance

The prompt asks the LLM to identify "Pareto-optimal" candidates, but the LLM only sees aggregate scores and objective scores — it has no access to per-example scores. Real Pareto dominance requires per-instance comparison (candidate A beats B on example 1, but B beats A on example 3). The LLM is doing "diverse subset selection," not Pareto identification. The naming "llm_pareto" could mislead users into thinking it replaces the real Pareto selector. Consider renaming to "llm_diverse" or documenting the distinction clearly.

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3. output_extractor bare-integer regex fallback picks up score digits

numbers = re.findall(r"\b(\d+)\b", lm_out)

If the LLM responds "Based on the scores of 0.8000, Candidate 2 is best", this finds ["0", "8000", "2"] and returns index 0 (from the "0" in "0.8000"), not index 2. The "Candidate N" pattern match above would catch this specific phrasing, but any response where score digits precede the candidate number and don't use the "Candidate N" pattern will silently return the wrong index.

Fix: either remove the bare-integer fallback entirely (the "Candidate N" pattern + comma-list + single-digit paths already cover well-formatted responses, and the fallback selector handles the rest), or filter out numbers that look like they're part of decimals (e.g., exclude matches adjacent to .).

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4. _make_llm_selector uses reflection_lm before string→callable conversion

In optimize_anything.py, the selector is built at:

"llm": lambda: _make_llm_selector(config, rng, mode="best"),

At this point config.reflection.reflection_lm may still be a string (e.g., "openai/gpt-5"). LLMCandidateSelector.__init__ handles this via LM(lm) if isinstance(lm, str), so it works — but later in optimize_anything, the same string is converted to an LM callable again:

config.reflection.reflection_lm = make_litellm_lm(config.reflection.reflection_lm)

This creates two separate LM instances for the same model — one in the selector, one in the proposer. They don't share state (retry config, etc.). Consider either (a) building the selector after the string→callable conversion, or (b) passing the already-converted callable to the selector.