Author

Haibin Ling

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#machine learning Preprint Sep 2026

Stable and Counterfactually Robust Physical World Models from Imposed Structure and Learned Physics

A world model learns to forecast how a physical system evolves from recorded trajectories, yet the systems it imitates obey physical laws that are neither fully supplied nor reliably respected. The model may create energy, drift or diverge over long rollouts, and answer a changed law query using the law observed during...

Yu-Feng Wang, Parivesh Priye, Lu Wei et al. · 0 citations

#machine learning Preprint Sep 2026

Conservation Buys Stability and Factoring Buys Counterfactuals in Physical World Models

A concrete design principle for physical world models: long-horizon stability and changed-law generalization arise from distinct structural commitments, and each can be imposed deliberately without requiring the other.

Yu-Feng Wang, Parivesh Priye, Lu Wei et al. · 0 citations

#machine learning Preprint Sep 2026

GRPO-QPS: Target-Preserving Reinforcement Learning for Quantum Posterior Sampling

Bayesian quantum tomography requires efficient inference while preserving a posterior fixed by the prior and Born likelihood. Learned transport provides fast amortized samples, but reward tuning can reshape the generated distribution rather than improve exploration of this fixed target. We introduce GRPO-QPS, a target-...

Yu-Feng Wang, Parivesh Priye, Lu Wei et al. · 0 citations

Preprint Jul 2026

Grounding Spatial Relations in a Compact World Model: Instruction Leakage and a Goal-Free Dynamics Fix

The diagnosis prescribes the fix: keep the goal out of the dynamics and supervise the \emph{read} path, recovering genuine, instruction-independent grounding, and the detection protocol and remedy apply to any goal-conditioned world model whose instruction names the scored quantity.

Yufeng Wang, Lu Wei, Haibin Ling · 0 citations