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ARXIV:2603.14659 · VIDEO REASONING · SUBMITTED 19 MAR · 18:48 UTC · FRESHNESS STALE
ARXIV:2603.14659VIDEO REASONINGSUBMITTED 19 MAR · 18:48 UTCFRESHNESS STALEarXiv
VisionCoach enhances video reasoning by using visual prompting to improve spatio-temporal grounding during training.
Opportunity summary
Pain VisionCoach enhances video reasoning by using visual prompting to improve spatio-temporal grounding during training.
Evidence 0 refs | 0 sources | 33% coverage
Blocker Evidence unverified
VisionCoach enhances video reasoning by using visual prompting to improve spatio-temporal grounding during training. While reinforcement learning (RL) with verifiable rewards improves accuracy, it still struggles to achieve reliable spatio-temporal grounding during the reasoning…
Video reasoning requires models to locate and track question-relevant evidence across frames. While reinforcement learning (RL) with verifiable rewards improves accuracy, it still struggles to achieve reliable spatio-temporal grounding during the reasoning process.
ScienceToStartup currently rates this 7.0/10 on the public viability pass. While reinforcement learning (RL) with verifiable rewards improves accuracy, it still struggles to achieve reliable spatio-temporal grounding during the reasoning process.
Video Reasoning moved forward this cycle; last verified April 2026. Public score 7.0/10.
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VisionCoach enhances video reasoning by using visual prompting to improve spatio-temporal grounding during training.
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Paper Pack
10.48550/arXiv.2603.14659VisionCoach enhances video reasoning by using visual prompting to improve spatio-temporal grounding during training.
Abstract
Video reasoning requires models to locate and track question-relevant evidence across frames. While reinforcement learning (RL) with verifiable rewards improves accuracy, it still struggles to achieve reliable spatio-temporal grounding during the reasoning process. Moreover, improving grounding typically relies on scaled training data or inference-time perception tools, which increases annotation cost or computational cost. To address this challenge, we propose VisonCoach, an input-adaptive RL framework that improves spatio-temporal grounding through visual prompting as training-time guidance. During RL training, visual prompts are selectively applied to challenging inputs to amplify question-relevant evidence and suppress distractors. The model then internalizes these improvements through self-distillation, enabling grounded reasoning directly on raw videos without visual prompting at inference. VisonCoach consists of two components: (1) Visual Prompt Selector, which predicts appropriate prompt types conditioned on the video and question, and (2) Spatio-Temporal Reasoner, optimized with RL under visual prompt guidance and object-aware grounding rewards that enforce object identity consistency and multi-region bounding-box overlap. Extensive experiments demonstrate that VisonCoach achieves state-of-the-art performance under comparable settings, across diverse video reasoning, video understanding, and temporal grounding benchmarks (V-STAR, VideoMME, World-Sense, VideoMMMU, PerceptionTest, and Charades-STA), while maintaining a single efficient inference pathway without external tools. Our results show that visual prompting during training improves grounded video reasoning, while self-distillation enables the model to internalize this ability without requiring prompts at inference time.
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Derived fallbackRead summaries are estimated from adjacent metadata, not verified extraction rows.
Proof status
unverified0 refs; 0 sources; 33% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
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Preparing verified analysis
Dimensions overall score 7.0
PROBLEM
VisionCoach enhances video reasoning by using visual prompting to improve spatio-temporal grounding during training. While reinforcement learning (RL) with verifiable rewards improves accuracy, it still struggles to achieve reliable spatio-temporal grounding during the reasoning...
METHOD
Video reasoning requires models to locate and track question-relevant evidence across frames. While reinforcement learning (RL) with verifiable rewards improves accuracy, it still struggles to achieve reliable spatio-temporal grounding during the reasoning process.
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. While reinforcement learning (RL) with verifiable rewards improves accuracy, it still struggles to achieve reliable spatio-temporal grounding during the reasoning process.
WHY NOW
Video Reasoning moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
VisionCoach enhances video reasoning by using visual prompting to improve spatio-temporal grounding during training. While reinforcement learning (RL) with verifiable rewards improves accuracy, it still struggles to achieve reliable spatio-temporal grounding during the reasoning process.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Video reasoning requires models to locate and track question-relevant evidence across frames. While reinforcement learning (RL) with verifiable rewards improves accuracy, it still struggles to achieve reliable spatio-temporal grounding during the reasoning process.
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. While reinforcement learning (RL) with verifiable rewards improves accuracy, it still struggles to achieve reliable spatio-temporal grounding during the reasoning process.
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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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VisionCoach enhances video reasoning by using visual prompting to improve spatio-temporal grounding during training.
Segment
Video Reasoning
Adoption evidence
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Commercial read
7.0/10 public viability
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missing
reason
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proof status
unverified
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confidence low
next verification path
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Evidence coverage
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stale
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passport absent
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stale
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Technical feasibility
partial
Current read
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
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0 references, 0 sources, 33% evidence coverage.
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Buyer clarity
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Write integration checklist from prototype path and target workflow.
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ARTIFACTS
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DEFENSIBILITY
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