arXiv Machine Learning July 30, 2026 By Yuvraj Verma

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

Read the original on arXiv Machine Learning →

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.

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

agents

arXiv Machine Learning

Jul 1

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

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.

By Mehmet Iscan

benchmarks

arXiv AI

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 31

Sample More, Reflect Less: Self-Refine and Reflexion Lose to Repeated Sampling at Equal Token Cost, from 1.5B to 7B

arXiv:2607. 28576v1 Announce Type: cross Abstract: Methods that make a language model plan, criticise and rewrite its own answer, reflect on mistakes, pick the best of several attempts, or debate with copies of itself nearly all make it generate far more text than a single chain of thought.

By Iliya Mirzaei

llms benchmarks

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

Jun 25

Reclaim Evaluation: A Lossy Memory Is Worse Than an Empty One

arXiv:2606. 25449v1 Announce Type: cross Abstract: A language model's memory can be worse than having no memory at all.

By Alex Kwon

llms benchmarks

Hugging Face Trending Papers

Jun 24

Reclaim Evaluation: A Lossy Memory Is Worse Than an Empty One

A language model's memory can be worse than having no memory at all. Give a model a memory that kept a wrong conclusion but dropped the work behind it, and it emits that stale value as a confident answer; give the same model an empty memory and it abstains.

llms benchmarks

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

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

Sample More, Reflect Less: Self-Refine and Reflexion Lose to Repeated Sampling at Equal Token Cost, from 1.5B to 7B

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

Reclaim Evaluation: A Lossy Memory Is Worse Than an Empty One

Reclaim Evaluation: A Lossy Memory Is Worse Than an Empty One