arXiv:2605.00623v1 Announce Type: new
Abstract: This paper introduces EnergyFlow, a framework that unifies generative action modeling with inverse reinforcement learning by parameterizing a scalar energy function whose gradient is the denoising field….
arXiv:2605.00674v1 Announce Type: new
Abstract: Large language models (LLMs) are becoming increasingly capable mathematical collaborators, but static benchmarks are no longer sufficient for evaluating progress: they are often narrow in scope, quickly …
arXiv:2605.00445v1 Announce Type: new
Abstract: Large Language Models have achieved remarkable success and are increasingly deployed in critical applications involving tabular data, such as Table Question Answering. However, their robustness to the st…
arXiv:2604.27792v2 Announce Type: replace
Abstract: Vision-Language-Action (VLA) models generalize semantically well but often lack fine-grained modeling of world dynamics. We present MotuBrain, a unified World Action Model that jointly models video a…
arXiv:2604.23073v2 Announce Type: replace-cross
Abstract: Vision-language-action (VLA) models can learn to perform diverse manipulation skills “out of the box,” but achieving the precision and speed that real-world tasks demand requires further fine-t…
arXiv:2510.21141v2 Announce Type: replace-cross
Abstract: Internet speed tests are indispensable for users, ISPs, and policymakers, but their static flooding-based design imposes growing costs: a single high-speed test can transfer hundreds of MB, and…
arXiv:2512.13746v5 Announce Type: replace-cross
Abstract: Fiber reinforcement and polymer matrix respond differently to manufacturing conditions due to mismatch in coefficient of thermal expansion and matrix shrinkage during curing of thermosets. Thes…
arXiv:2605.00482v1 Announce Type: cross
Abstract: Mobile network operators must monitor thousands of heterogeneous network elements across the radio access network and the packet core, each exposing high-dimensional KPI time series. The scale and cost…
arXiv:2604.25120v2 Announce Type: replace
Abstract: We study clinical trial table reasoning, where answers are not directly stored in visible cells but must be reasoned from semantic understanding through normalization, classification, extraction, or …
arXiv:2605.00742v1 Announce Type: new
Abstract: LLMs excel at predictive tasks and complex reasoning tasks, but many high-value deployments rely on decisions under uncertainty, for example, which tool to call, which expert to consult, or how many reso…