LoongReflect: Boosting Long-Horizon Reflection in Search Agents via Global Perspective Distillation

Large language model agents increasingly rely on long-horizon reasoning to solve complex tasks involving planning, tool use, and memory. A critical capability in such settings is reflection: assessing trajectory progress, identifying missing evidence and unreliable intermediate states, and deciding whether to continue, revise, or abandon the current branch. Learning effective reflection, however, is challenging because reflection is performed locally within the current branch, whereas its utilit

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ethics.ai (12 August 2026), “LoongReflect: Boosting Long-Horizon Reflection in Search Agents via Global Perspective Distillation,” evidence record 18782, https://ethics.ai/record/18782 (originally published by arXiv).

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