← 研踪 PaperTrailACL Anthology · ACL 2026:acl:2026.acl-long.1314 · 记录 #1
MagicBench: Diagnosing Visual Agency Loss and Semantic Dependency in Multimodal LLMs
来源日期（仅到月份） 2026-07
来源记录日期 2026-07
雷达观测 2026-11-01 21:54
摘要
Multimodal Large Language Models typically assume linguistic context invariably enhances visual understanding. We study this assumption in semantic adversarial scenarios, specifically magic tricks, where narration deliberately diverges from physical reality. We introduce MagicBench, a diagnostic benchmark of 402 videos for evaluating MLLMs under hierarchical linguistic interference, together with a Physical Constraint Set (PCS) protocol for assessing adherence to physical laws. Evaluation uncovers a Semantic Dependency Paradox: (1) Semantic anchoring: Entity nouns act as anchors aiding localization, paradoxically boosting performance despite false predicates. (2) Visual Agency Loss: In semantic vacuums, multimodal performance collapses 12.4% (p < 0.01) below the vision-only “capability probe”. This gap persists under symmetric prompting, suggesting a form of functional perception suppression in which autonomous visual search may be under-utilized in multimodal settings without linguistic triggers. Causal interventions via spatial prompting and signal magnification provide evidence that internal reasoning remains functional, supporting the interpretation of a perceptual access bottleneck. Our findings suggest MLLMs function as “language-guided passive observers”, advocating for perceptually-independent architectures that decouple sensory agency from linguistic dominance. Code and dataset are available at https://github.com/Ink-Dawn/MagicBench
论文发现出处
这些页面提供论文线索，不代表同行评审或质量认证。
观测到的版本
平台记录号代表本站观测到的元数据变化，不是作者修订号。来源日期仅到月份或年份时，按该月或该年首日存档。
记录 #1 · 2026-07 · 最新观测版本
MagicBench: Diagnosing Visual Agency Loss and Semantic Dependency in Multimodal LLMs
Multimodal Large Language Models typically assume linguistic context invariably enhances visual understanding. We study this assumption in semantic adversarial scenarios, specifically magic tricks, where narration deliberately diverges from physical reality. We introduce MagicBench, a diagnostic benchmark of 402 videos for evaluating MLLMs under hierarchical linguistic interference, together with a Physical Constraint Set (PCS) protocol for assessing adherence to physical laws. Evaluation uncovers a Semantic Dependency Paradox: (1) Semantic anchoring: Entity nouns act as anchors aiding localization, paradoxically boosting performance despite false predicates. (2) Visual Agency Loss: In semantic vacuums, multimodal performance collapses 12.4% (p < 0.01) below the vision-only “capability probe”. This gap persists under symmetric prompting, suggesting a form of functional perception suppression in which autonomous visual search may be under-utilized in multimodal settings without linguistic triggers. Causal interventions via spatial prompting and signal magnification provide evidence that internal reasoning remains functional, supporting the interpretation of a perceptual access bottleneck. Our findings suggest MLLMs function as “language-guided passive observers”, advocating for perceptually-independent architectures that decouple sensory agency from linguistic dominance. Code and dataset are available at https://github.com/Ink-Dawn/MagicBench
查看此版本解读 →查看此版本原文 ↗