arXiv:2606. 16923v1 Announce Type: new Abstract: Simulation-based inference (SBI) of latent parameters is often hindered by simulator misspecification, the mismatch between simulated and real-world observations caused by inherent modeling simplifications.
By Arunkumar V, Manoranjan Gandhudi, Gangadharan G. R., Arun Prakash, S. Senthilkumar
The paper introduces SPT-Flow, a continuous‑time world model that samples a single latent per trajectory and aligns a free‑running ODE rollout with demonstration states at multiple readout times. Experiments on LIBERO show that SPT-Flow improves best‑of‑10 PSNR by 2.0–2.4 dB at mid‑trajectory and final frames and reduces mean LPIPS by 23 % compared to a retrained ODEWorld baseline, while maintaining comparable inference cost. The authors attribute these gains to better utilization of trajectory‑associated latent information and demonstrate that learned latent conditioning adds diversity and best‑of‑K performance.
whyItMatters":"SPT-Flow demonstrates that careful alignment of ODE rollouts with demonstration states can significantly enhance reconstruction quality and diversity in continuous‑time world models, offering a practical improvement over existing baselines."
By Mingyang Lyu, Yinqian Sun, Gongce Cao, Yuhao Zhang, Lu Jia, Qian Zhang, Bo Wang, Pin Lyu
arXiv:2609.38902v1 Announce Type: cross
Abstract: Augmenting small concurrent studies with external or historical cohorts is attractive in drug development, where enrollment is slow, follow-up is exp...
By Chin-Hung Huang, JooChul Lee, Huan He
The paper investigates when in‑context learning (ICL) can succeed even if the instruction or target task is not covered by a model’s pretraining data. It introduces a theoretical framework that distinguishes two types of distribution shift—unsupported instructions and unsupported target tasks—and shows how demonstrations can mitigate these shifts. The authors further extend the analysis to long‑horizon tasks that can be decomposed into pretraining‑supported subtasks, providing error bounds that explain how ICL can handle tasks beyond the model’s original support.
By Xuhan Tong, Yuchen Zeng, Jiawei Zhang
arXiv:2606. 27354v1 Announce Type: cross Abstract: Neural surrogate models offer fast approximate mappings from PDE parameters to solutions, but they typically treat solving as a purely statistical task: once trained, they struggle to correct their own constraint violations and extrapolate beyond the training distribution.
By Haina Jiang, Liam Wang, Peng-Chen Chen, Min Seop Kwak, Seungryong Kim, Brian Bell, Jeong Joon Park
The paper introduces a framework for evaluating how well learned states from neural PDE solvers can be reused in new contexts. By separating solution accuracy, learning contribution, and numerical utility through paired state comparisons and cost accounting, the authors audit 12 papers and conduct experiments on linear PDE families, Fourier neural operators, and convolutional networks. Their results show that matched state comparisons and downstream computational evidence can lead to significant savings in conjugate‑gradient iterations and demonstrate finite‑budget pretraining value.
By Shunye Wang, Haochen Wen, Shuo Li Liu, Xuanyi Wang, Lihao Liu, Zhongying Deng