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)