HOCA-Bench: Beyond Semantic Perception to Predictive World Modeling via Hegelian Ontological-Causal Anomalies

• URL: http://arxiv.org/abs/2602.19571v1
• Date: Mon, 23 Feb 2026 07:40:32 GMT
• Title: HOCA-Bench: Beyond Semantic Perception to Predictive World Modeling via Hegelian Ontological-Causal Anomalies
• Authors: Chang Liu, Yunfan Ye, Qingyang Zhou, Xichen Tan, Mengxuan Luo, Zhenyu Qiu, Wei Peng, Zhiping Cai,
• Abstract summary: Video-LLMs have improved steadily on semantic perception, but they still fall short on predictive world modeling.<n>We introduce HOCA-Bench, a benchmark that frames physical anomalies through a Hegelian lens.
• Score: 30.95227838131802
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video-LLMs have improved steadily on semantic perception, but they still fall short on predictive world modeling, which is central to physically grounded intelligence. We introduce HOCA-Bench, a benchmark that frames physical anomalies through a Hegelian lens. HOCA-Bench separates anomalies into two types: ontological anomalies, where an entity violates its own definition or persistence, and causal anomalies, where interactions violate physical relations. Using state-of-the-art generative video models as adversarial simulators, we build a testbed of 1,439 videos (3,470 QA pairs). Evaluations on 17 Video-LLMs show a clear cognitive lag: models often identify static ontological violations (e.g., shape mutations) but struggle with causal mechanisms (e.g., gravity or friction), with performance dropping by more than 20% on causal tasks. System-2 "Thinking" modes improve reasoning, but they do not close the gap, suggesting that current architectures recognize visual patterns more readily than they apply basic physical laws.

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