A planner using a learned world model can improve its predicted outcome while choosing a worse action. We study when expected free energy (EFE) can help decide how much computation to spend on a plan. An EFE-inspired objective combines goal distance with disagreement among stochastic predictions. We test candidate scor...
Tong Zhang, Zhi-Yuan Shi, Yun Peng et al.· 0 citations
Multimodal language models (MLLMs) exhibit promising spatial reasoning capabilities, yet the internal mechanisms supporting them remain poorly understood. We uncover S-Space, a linear representation subspace in which object-token representations encode continuous coordinates along horizontal, vertical, and distance axe...
Zhi-Yuan Shi, Xiang Li, Xing-Zhuo Guo et al.· 0 citations
This work introduces HarnessEval-W, an agentified evaluation pipeline that brings the harness paradigm from the LLM ecosystem to world model benchmarking, and applies HarnessEval-W to 18 representative world models over 330 evaluation cases.
Wei-Liang Chen, Haowen Sun, Jun Gao et al.· 3 citations
Evaluating twelve open and proprietary models, it is found that abstraction failure is lawful, and the central finding is a gap between recognition and cross-voice mapping: models identify a structure within one voice far better than they carry it across voices; every model pays this tax, and mapping strong enough to n...
Tong Zhang, Zhiyuan Shi, Yun Peng et al.· 0 citations
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