AI RESEARCH
ReFlect: An Effective Harness System for Complex Long-Horizon LLM Reasoning
arXiv CS.CL
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ArXi:2605.05737v1 Announce Type: cross Current reasoning paradigms for LLMs include chain-of-thought, ReAct, and post-hoc self-critique. These paradigms rely on two assumptions that fail on long-horizon, multi-stage tasks. As a result, errors accumulate silently across reasoning steps, leaving an open question: can a reasoning system effectively detect and recover from its own failures? We present ReFlect, a \emph{harness} system for LLM reasoning that creates standalone error detection and recovery logic as a deterministic wrapper around the model.