# Inference-Time Code Selection via Symbolic Equivalence Partitioning

arXiv: https://arxiv.org/abs/2604.06485 · HTML: https://mlsanity.com/paper/2604.06485/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 27 references that are in the map.
- First posted on arXiv 7 Apr 2026.

## Builds on (most cited first, 18 of 18)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023)
- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code](https://mlsanity.com/paper/2403.07974/) (2024)
- [Competition-Level Code Generation with AlphaCode](https://mlsanity.com/paper/2203.07814/) (2022)
- [Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation](https://mlsanity.com/paper/2305.01210/) (2023)
- [Large Language Monkeys: Scaling Inference Compute with Repeated Sampling](https://mlsanity.com/paper/2407.21787/) (2024)
- [Eureka: Human-Level Reward Design via Coding Large Language Models](https://mlsanity.com/paper/2310.12931/) (2023)
- [CodeBLEU: a Method for Automatic Evaluation of Code Synthesis](https://mlsanity.com/paper/2009.10297/) (2020)
- [CodeT: Code Generation with Generated Tests](https://mlsanity.com/paper/2207.10397/) (2022)
- [Wider or Deeper? Scaling LLM Inference-Time Compute with Adaptive Branching Tree Search](https://mlsanity.com/paper/2503.04412/) (2025)
- [Revisit Self-Debugging with Self-Generated Tests for Code Generation](https://mlsanity.com/paper/2501.12793/) (2025)
- [Assessing Correctness in LLM-Based Code Generation via Uncertainty Estimation](https://mlsanity.com/paper/2502.11620/) (2025)
- [Functional Overlap Reranking for Neural Code Generation](https://mlsanity.com/paper/2311.03366/) (2023)

## Abstract (arXiv)

Sampling multiple candidate programs at inference time is an effective way to improve LLM code generation. However, its benefit depends on reliably selecting a correct solution from the generated pool. We observe that this selection problem has a distinctive semantic structure: correct solutions, despite differences in syntax, implementation, or algorithmic strategy, often converge to the same functional behavior over valid inputs. At the same time, consensus alone is not sufficient for correctness, because models can also produce correlated wrong solutions that implement the same mistaken behavior. We propose Symbolic Equivalence Partitioning (SEP), an inference-time selection framework that first uses problem-provided public examples as lightweight validity signals. SEP then uses symbolic execution to partition the remaining candidate programs into bounded functional equivalence classes and selects from the dominant equivalence class. Across HumanEval+ and LiveCodeBench, SEP consistently improves selection accuracy without auxiliary test generation, learned verifiers, or additional LLM inference. At N=10, SEP improves average accuracy from 0.754 to 0.826 on HumanEval+ and from 0.565 to 0.647 on LiveCodeBench, showing that symbolic functional agreement is an effective signal for inference-time code selection.