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ARXIV:2604.04379 · VIDEO REASONING · SUBMITTED 07 APR · 20:12 UTC · FRESHNESS UNKNOWN
ARXIV:2604.04379VIDEO REASONINGSUBMITTED 07 APR · 20:12 UTCFRESHNESS UNKNOWNSongyuan Yang · Weijiang Yu · Jilin Ma · Ziyu Liu · Guijian Tang · Wenjing Yang · +2 at arXiv
A dual paradigm for video reasoning that improves reliability and interpretability by explicitly generating and electing based on evidence.
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Pain A dual paradigm for video reasoning that improves reliability and interpretability by explicitly generating and electing based on evidence.
Evidence 0 refs | 0 sources | 0% coverage
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A dual paradigm for video reasoning that improves reliability and interpretability by explicitly generating and electing based on evidence. We introduce Reinforce to Learn, Elect to Reason (RLER), a dual paradigm that decouples learning…
Video reasoning has advanced with large multimodal models (LMMs), yet their inference is often a single pass that returns an answer without verifying whether the reasoning is evidence-aligned. We introduce Reinforce to Learn, Elect…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. RLER achieves state of the art across all benchmarks and delivers an average improvement of 6.3\% over base models, while using on average 3.1…
Video Reasoning moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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A dual paradigm for video reasoning that improves reliability and interpretability by explicitly generating and electing based on evidence.
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10.48550/arXiv.2604.04379A dual paradigm for video reasoning that improves reliability and interpretability by explicitly generating and electing based on evidence.
Abstract
Video reasoning has advanced with large multimodal models (LMMs), yet their inference is often a single pass that returns an answer without verifying whether the reasoning is evidence-aligned. We introduce Reinforce to Learn, Elect to Reason (RLER), a dual paradigm that decouples learning to produce evidence from obtaining a reliable answer. In RLER-Training, we optimize the policy with group-relative reinforcement learning (RL) and 3 novel task-driven rewards: Frame-sensitive reward grounds reasoning on explicit key frames, Think-transparency reward shapes readable and parsable reasoning traces, and Anti-repetition reward boosts information density. These signals teach the model to emit structured, machine-checkable evidence and potentiate reasoning capabilities. In RLER-Inference, we apply a train-free orchestrator that generates a small set of diverse candidates, parses their answers and cited frames, scores them by evidence consistency, confidence, transparency, and non-redundancy, and then performs a robust evidence-weighted election. This closes the loop between producing and using evidence, improving reliability and interpretability without enlarging the model. We comprehensively evaluate RLER against various open-source and RL-based LMMs on 8 representative benchmarks. RLER achieves state of the art across all benchmarks and delivers an average improvement of 6.3\% over base models, while using on average 3.1 candidates per question, indicating a favorable balance between compute and quality. The results support a simple thesis: making evidence explicit during learning and electing by evidence during inference is a robust path to trustworthy video reasoning.
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PROBLEM
A dual paradigm for video reasoning that improves reliability and interpretability by explicitly generating and electing based on evidence. We introduce Reinforce to Learn, Elect to Reason (RLER), a dual paradigm that decouples learning to produce evidence from obtaining a relia...
METHOD
Video reasoning has advanced with large multimodal models (LMMs), yet their inference is often a single pass that returns an answer without verifying whether the reasoning is evidence-aligned. We introduce Reinforce to Learn, Elect to Reason (RLER), a dual paradigm that decouple...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. RLER achieves state of the art across all benchmarks and delivers an average improvement of 6.3\% over base models, while using on average 3.1 candidates per question, indicating a favorable balance betwe...
WHY NOW
Video Reasoning moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A dual paradigm for video reasoning that improves reliability and interpretability by explicitly generating and electing based on evidence. We introduce Reinforce to Learn, Elect to Reason (RLER), a dual paradigm that decouples learning to produce evidence from obtaining a reliable answer.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Video reasoning has advanced with large multimodal models (LMMs), yet their inference is often a single pass that returns an answer without verifying whether the reasoning is evidence-aligned. We introduce Reinforce to Learn, Elect to Reason (RLER), a dual paradigm that decouples learning to produce evidence from obtaining a reliable answer.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. RLER achieves state of the art across all benchmarks and delivers an average improvement of 6.3\% over base models, while using on average 3.1 candidates per question, indicating a favorable balance between compute and quality. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Video Reasoning moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A dual paradigm for video reasoning that improves reliability and interpretability by explicitly generating and electing based on evidence.
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Video Reasoning
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