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VepAgent: Bridging Causal-Transition via Tool-Augmented Reinforcement Learning for Video Event Prediction

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 08/10/2026, 03:18:24 UTC.

Briefings

VepAgent is an agentic framework using tool-augmented reinforcement learning to improve Video Event Prediction by modeling causal transitions from observed states to future events. It constructs the futurebench-4K dataset for supervised fine-tuning and uses a diagnostic tool library to recover missing spatio-temporal evidence. The method achieves state-of-the-art performance on FutureBench and NEPBench, significantly outperforming larger MLLMs by relying on genuine visual grounding rather than textual similarities.

Key points

• VepAgent constructs the futurebench-4K dataset to bridge the causal-logic gap in Video Event Prediction by structuring the deduction of unobserved intermediate states.
• The framework uses a composite reward mechanism to jointly optimize prediction accuracy, causal coherence, and reliable prior, compelling the agent to rely on genuine visual grounding.

Context

Video Event Prediction (VEP) faces challenges where MLLMs rely on retrospective summarization and text-centric priors, limiting their ability to bridge unobserved causal transitions. VepAgent addresses this by integrating causal-transition reasoning with tool-augmented reinforcement learning for robust VEP.

Limitations

The evidence is limited to the study's findings on FutureBench and NEPBench datasets. Performance comparisons with larger MLLMs are presented as outperformance, but specific numerical values for these comparisons are not provided in the source.

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