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Retrace-1.5B

A small reasoning model tuned to self-correct via failure traces.

via Hugging Face
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Papers16

paperCheckRLM: Effective Knowledge-Thought Coherence Checking in Retrieval-Augmented Reasoning

Reasoning Language Models (RLMs) have significantly improved performance on complex tasks by extending the reasoning cha

paperThe Complexity Ceiling Benchmark: A Multi-Domain Evaluation of Sequential Reasoning Under Depth Scaling

We introduce the Complexity Ceiling Benchmark (CCB), a controlled evaluation of how language-model reasoning decays as t

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

In deployment settings where retraining is infeasible, small frozen code models are routinely asked to repair a failed p

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

Frozen small code LLMs are deployed locally, yet the information guiding a retry after a failed attempt is still measure

paperSelf-rewarding agents that retrace failures

Agents that attribute their own errors and retrace to repair multi-step reasoning.

paperFAR: Failure-Aware Retry for Test-Time Recovery and Continual Policy Improvement

Robot policies inevitably encounter failures when deployed in real environments. Naive retries often repeat the same mis

paperGrounding LLM Reasoning under Incomplete Graph Evidence

Knowledge graphs can guide large language models (LLMs) reasoning, but the graph seen by a system is usually a retrieved

paperTwo Axes of LLM Abstention: Answer Correctness and Question Answerability

A model should refuse two different things: answers it would get wrong, and questions it should not answer at all, such

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