arXiv Machine Learning July 1, 2026 By Mehmet Iscan

Falsification, Not Exposure: An Internally Preregistered Placebo-Controlled Decomposition of Self-Repair Feedback in Frozen Small Code Models

Read the original on arXiv Machine Learning →

arXiv:2606. 31511v1 Announce Type: cross Abstract: In deployment settings where retraining is infeasible, small frozen code models are routinely asked to repair a failed program after seeing their own failing output, usually treated as a retry mechanism.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

benchmarks

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Jul 15

Form, Not Content? A Preregistered, Placebo-Controlled Evaluation of Learned Error-Conditioned Self-Repair Through Prompts and Weights in Frozen Small Code Models

arXiv:2607. 12962v1 Announce Type: cross Abstract: Frozen small code LLMs are deployed locally, yet the information guiding a retry after a failed attempt is still measured without placebo controls in the self-repair literature.

By Mehmet Iscan

llms reinforcement-learning

arXiv Machine Learning

Jul 30

Try Again, Don't Look Back: Blind Resampling Outperforms Self-Repair in Small Code Models

arXiv:2607. 26117v1 Announce Type: cross Abstract: Self-repair - returning a failed program to the model together with its test output and asking for a correction - is a standard component of code agents, and is almost always evaluated against a baseline that does not retry at all.

By Yuvraj Verma

agents

arXiv Machine Learning

Jun 16

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.

By Mehmet Iscan

benchmarks safety

arXiv AI

Aug 3

Validation Evidence in LLM Repair Agents: How Much of What Passes Actually Tests the Bug?

arXiv:2607. 28871v1 Announce Type: cross Abstract: When a repair agent runs a test and sees it pass, the result is treated as evidence about the reported defect.

By Xiaonan Xu, Wenjing Wu

llms agents

arXiv Machine Learning

Jul 14

When the Reward Suite Is Leaky: A Preregistered Causal Contrast of Natural Verifier False Positives in RLVR

arXiv:2607. 11022v1 Announce Type: new Abstract: The test suites used as RLVR rewards for code have natural false positives: per-task, persistent, asymmetric errors that accept the same wrong programs every time they appear, unlike the symmetric or resampled noise assumed by existing noise-robustness analyses.

By Chuyifei Zhang

arXiv AI

Jul 28

Looping Is Not Reliability: State-Bound Evidence and Typed Revision Contracts for Agentic Code Repair

arXiv:2607. 24604v1 Announce Type: cross Abstract: Generate--test--revise loops are common in coding agents, but repetition alone provides no reliability guarantee.

By Xueping Gao, Jianwei Yang, Qiang Yang

agents benchmarks safety

Related stories

Form, Not Content? A Preregistered, Placebo-Controlled Evaluation of Learned Error-Conditioned Self-Repair Through Prompts and Weights in Frozen Small Code Models

Try Again, Don't Look Back: Blind Resampling Outperforms Self-Repair in Small Code Models

Selection Without Signal, Recovery Through Expression: A Measurement Study of Post-Hoc Falsification Operators for Frozen Small Code Models

Validation Evidence in LLM Repair Agents: How Much of What Passes Actually Tests the Bug?

When the Reward Suite Is Leaky: A Preregistered Causal Contrast of Natural Verifier False Positives in RLVR

Looping Is Not Reliability: State-Bound Evidence and Typed Revision Contracts for Agentic Code Repair