arXiv:2605.24613v1 Announce Type: new Abstract: Post-hoc repair of LLM mathematical reasoning introduces an asymmetric risk: fixing an incorrect reasoning trace is useful, but replacing a trace that was already correct can be harmful. We study this problem under a selective replacement setting, where a system must decide whether a repaired candidate is safer than preserving the original cached trace. We present GuardedRepair, a guarded best-of-N repair framework that diagnoses cached reasoning traces, selectively triggers repair, and accepts answer-changing candidates only when deterministic v
The rapid deployment of LLMs into critical applications necessitates robust methods for ensuring their reliability and preventing errors, especially in sensitive domains like mathematical reasoning.
This development addresses a key limitation of LLMs, enabling safer and more trustworthy integration into complex systems where accuracy is paramount, thereby accelerating their adoption.
The ability to selectively repair LLM reasoning traces minimizes the risk of introducing new errors while imperfectly correcting existing ones, improving overall system robustness.
- · LLM developers
- · AI Safety researchers
- · Industries requiring high-accuracy AI
- · Systems relying on un-repaired LLM outputs
Improved reliability and broader deployment of LLMs in applications requiring precise computation.
Increased investment in LLM self-correction and alignment research as critical path to general AI.
Enhanced automation of tasks currently requiring human oversight due to LLM error potential.
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Read at arXiv cs.CL