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

Apparent Compression, Real Stability: The Intrinsic Dimension of Learning a Quantum Wavefunction

How many directions in weight space does training need? The intrinsic dimension answers this with the smallest number of random directions in which training still reaches a target accuracy, and small values have motivated parameter-efficient methods such as LoRA. We measure it for variational Monte Carlo (VMC), which t...

Lu Wei, Yu-Feng Wang, Chen-Feng Cao et al. · 0 citations

#machine learning Preprint Sep 2026

Available Guardrails: Certifying Selective Prediction across ML Systems

A selective predictor acts as a safety gate: it returns an output only when the prediction appears sufficiently trustworthy. Deployments increasingly require this reliability to be certified at a target precision for every reporting unit of interest, such as a tool, policy label, or patient subgroup. The main difficult...

Parivesh Priye, Yu-Feng Wang, Hai-Bin Ling 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

GRPO-QPS is introduced, a target-preserving framework in which GRPO learns proposal behavior and an exact Metropolis correction preserves the posterior after training, which combines target-preserving Bayesian inference with broad gains over learned transport baselines and a sampling advantage when efficient exploratio...

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