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.· 1 citation
This work makes the notion of availability computable through classical exact-binomial inversion and forms reporting-partition selection, under a fixed group order, as a dynamic program that exposes the trade-off among safety, granularity, and served traffic.
Parivesh Priye, Yu-Feng Wang, Hai-Bin Ling et al.· 0 citations
The method, $\lambda$-Controlled GRPO, calibrates importance-ratio behavior from this predicted law rather than from noisy empirical statistics, and allocates gradient effort across denoising steps according to their predicted cost, and keeps late-step path variance within its intended budget.
Yu-Feng Wang, Parivesh Priye, Meeshawn Marathe et al.· 0 citations
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
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
VoiceLongMemEval (VLME) benchmark, where every answer depends on paralinguistic metadata attached to conversational turns, which is otherwise unrecoverable from the words alone, is presented.
