MaD Physics is a new benchmark for evaluating AI agents on constrained information-seeking, model inference, and prediction in three physical environments with altered laws to avoid knowledge contamination.
arXiv preprint arXiv:2601.16007 (2026) Human Cognition in Machines: A Unified Perspective of World Models 49
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Introduces APT chains as ordered causal transition sequences and APT-Tune to improve VLM transition detection while preserving event-level performance.
The paper introduces a unified framework for world models that fully incorporates all cognitive functions from Cognitive Architecture Theory, highlights under-researched areas in motivation and meta-cognition, and proposes Epistemic World Models as a new category for scientific discovery agents.
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MaD Physics: Evaluating information seeking under constraints in physical environments
MaD Physics is a new benchmark for evaluating AI agents on constrained information-seeking, model inference, and prediction in three physical environments with altered laws to avoid knowledge contamination.
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APT: Atomic Physical Transitions for Causal Video-Language Understanding
Introduces APT chains as ordered causal transition sequences and APT-Tune to improve VLM transition detection while preserving event-level performance.
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Human Cognition in Machines: A Unified Perspective of World Models
The paper introduces a unified framework for world models that fully incorporates all cognitive functions from Cognitive Architecture Theory, highlights under-researched areas in motivation and meta-cognition, and proposes Epistemic World Models as a new category for scientific discovery agents.