Sparse Evidence Can Suffice: Agentic Evidence Seeking for Multimodal Video Misinformation Detection

2026-07-20

AI Summary

Existing approaches to video misinformation detection typically model entire videos holistically, overlooking the fact that deceptive content often relies on sparse, critical cues. This leads to computational redundancy and dilutes discriminative evidence. To address this, this work proposes SIEVE, a novel framework that introduces, for the first time, an agent-based paradigm for actively searching multimodal sparse evidence. SIEVE decouples evidence acquisition from verification: an agent employs evidence-aware reinforcement learning to efficiently extract a minimal set of key cues, forming a compact evidence package, which a verifier then uses to make interpretable judgments. The method significantly outperforms current state-of-the-art techniques across multiple benchmarks, achieving high detection accuracy with highly distilled evidence while providing transparent and traceable decision rationales.

Abstract

Multimodal video misinformation detection is commonly formulated as a holistic video-understanding task, where the entire video and its associated content are processed and judged in a single pass. However, real-world misinformation often exhibits a sparse and compositional evidence structure: a reliable decision may depend on only a few coupled clues, while most video content contributes limited additional information. Exhaustive multimodal reasoning may therefore introduce substantial redundancy and obscure decisive evidence. This motivates decoupling evidence acquisition from verification: first identifying sparse, decision-relevant clues and then judging veracity based on the acquired evidence. Accordingly, we propose SIEVE, a framework for Sparse Interactive Evidence Verification via Extraction in multimodal video misinformation detection. An evidence-seeking agent actively explores the available multimodal evidence and constructs a compact evidence package, which is then used by a verifier to determine veracity. The agent is trained with supervised evidence-seeking trajectories and an evidence-aware reinforcement learning objective that promotes informative evidence acquisition while discouraging unnecessary or invalid interactions. Experiments on multiple video misinformation benchmarks show that SIEVE consistently outperforms the evaluated baselines and supports reliable verification using compact evidence packages. Moreover, the resulting acquisition process provides an explicit and inspectable evidence trail, improving the transparency and groundedness of multimodal misinformation detection.

Problem

• multimodal video misinformation detection
• sparse evidence
• evidence structure
• decision-relevant clues
• redundancy

Innovation

• evidence-seeking agent
• sparse evidence
• multimodal misinformation detection
• interactive verification
• reinforcement learning