Selection Without Signal, Recovery Through Expression: A Measurement Study of Post-Hoc Falsification Operators for Frozen Small Code Models
arXiv:2606.16999v1 Announce Type: cross Abstract: Frozen small code models ( =45. Two operators help on a different axis, outside the semantic output space. An expression-layer recovery (M1), the only accuracy gain here, recovers correct programs the standard extractor discards (robust extraction and public-test signature alignment); it does no harm (b10=0), is leakage-free, and lifts DeepSeek-Coder-1.3B by +12 tasks on HumanEval+ (p=2.4e-4). An adaptive consensus early-stop (ACE) is a calibrated compute-saving control (~19% saving, zero harm). M1 and the selection negative replicate on HumanE
The continuous development and scaling of foundation models necessitates more efficient and accurate code generation, making improvements in extraction and recovery particularly timely.
Improving the accuracy and efficiency of small code models directly impacts the development of AI agents and automated software development, enabling more robust and reliable autonomous systems.
New methods for post-hoc falsification and recovery can significantly enhance the performance of existing frozen models without expensive retraining, potentially accelerating their deployment in real-world applications.
- · AI developers
- · Software automation companies
- · Companies using small code models
- · Inefficient code generation methods
- · High-compute code model training
Improved small code model performance leads to more reliable automated code generation and AI agents.
Enhanced code model accuracy could reduce human oversight in certain software development tasks, accelerating release cycles.
The ability to 'recover' correct programs from initial discards might enable new forms of error correction and self-improving AI systems.
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