Reflect-R1: Evidence-Driven Reflection for Self-Correction in Long Video Understanding

摘要

Current multimodal re_x001D_ection mechanisms for long video un-derstanding predominantly rely on closed-loop self-re_x001D_ection within in-ternal parameters. Lacking objective external evidence, models are fre-quently trapped in blind con_x001C_dence and often fail to correct errors.Furthermore, applying reinforcement learning to multi-stage re_x001D_ectionpipelines introduces severe policy coupling, which is exacerbated by acritical scarcity of dedicated training data. To address these limitations,this work proposes Re_x001D_ect-R1, the _x001C_rst Evidence-Driven self-correctionframework for long video understanding. The framework constructs athree-stage pipeline consisting of intuition, veri_x001C_cation, and arbitration.By dynamically retrieving objective visual evidence to verify initial in-tuitions and autonomously executing multiple temporal searches to re-solve con_x001D_icts, it completely breaks the hallucination loop. To over-come policy coupling, we design a stage-decoupled reinforcement learn-ing algorithm named SD-GRPO that independently computes advantagefunctions across di_x001B_erent reasoning stages. Concurrently, we constructa dataset of 120K samples to bridge the training data gap. Extensiveexperiments on benchmarks such as VideoMME and LongVideoBenchdemonstrate that Re_x001D_ect-R1 achieves state-of-the-art performance. Ourmethod signi_x001C_cantly improves the genuine recti_x001C_cation rate and enablesauthentic self-correction strictly grounded in objective evidence.