execution-grounded-selection

# Execution-Grounded Selection ## Why Output-majority voting (pick the most common string output) is dominated by *any* selector that actually runs the candidates. Semantic Voting (arxiv 2605.08680v1) shows 19-52pp improvement over output voting across multiple code benchmarks. The specific aggregation rule (majority, weighted, MBR-Exec) is statistically indistinguishable once execution evidence is present — *execution is the dominant signal, aggregation is the residual*. This is the code-domain analogue of the **noise-as-exploration** Rosetta concept: execution diversity is the exploration mechanism; behavioural fingerprint is the equilibrium signal. ## How The Semantic Voting pipeline: 1. **Sample N candidates** — generate at temperature > 0 (typical N = 5-10). 2. **Generate diverse inputs** — sketch-generated inputs (derived from candidate population structure) beat random fuzz by ~11pp. If sketch generation is infeasible, fall back to LLM-generated test inputs > random fuzz. 3. **Execute each candidate on each input** — collect (candidate_i, input_j, output_ij) tuples. Crash counts as a distinct fingerprint, not a discard. 4. **Cluster by fingerprint** — equivalence on `[output_ij for j in inputs]` defines the cluster. 5. **Pick the largest cluster** — break ties by candidate self-confidence or by Pareto on execution cost. ## When to skip - Generation is deterministic (temperature = 0) — there's nothing to select among. - Execution is expensive or has side effects (touches the network, modifies state) — fall back to dry-run / static analysis. - The output isn't executable (e.g., natural-language summary) — use paired-trace audit instead. ## Integration - `wrap:verify` and `gsd-verify-work` — pre-merge gate when verification produces multiple candidate fixes. - `gsd-code-fixer` — when the fixer proposes more than one fix per finding. - `code-review` — adds a "did you actually run it?" subsection to the review rubric. ## Cross-references - Rosetta concept #9 (Execution-Grounded Selection) — canonical definition - College: `agent-systems / agentic-code-generation / agent-execution-grounded-selection` - Related skills: `test-generator` (generates the inputs; pair with this skill for the full loop)