Public Tests Create Overconfidence Gap in LLM Code Generation
A new study from arXiv (2604.21598) reveals that multi-agent frameworks for autonomous code generation rely heavily on human-provided public test cases, which introduces an "overconfidence gap." These frameworks, which use simulation-driven planning and debugging to verify logic, overfit to simplistic examples and fail on hidden evaluation cases. The dependency on manually authored input-output examples is a labor-intensive bottleneck, restricting methods to curated competitive programming benchmarks since ground-truth examples are rarely available in real-world software engineering.
Key facts
- Multi-agent frameworks are widely used in autonomous code generation.
- Recent work incorporates simulation-driven planning and debugging.
- Language models trace execution steps to verify logic.
- Approaches depend on human-provided public test cases.
- Manually authoring input-output examples is labor-intensive.
- Ground-truth examples are rarely available prior to implementation.
- Reliance on public tests induces an overconfidence gap.
- Frameworks overfit to simplistic examples and fail on hidden evaluation.
Entities
Institutions
- arXiv