arXiv:2608.13797v1 Announce Type: new Abstract: Computational approaches to drug discovery involve multiple sub-problems, and among them, drug-target binding affinity prediction plays an important role. Despite recent advances, accurately predicting binding affinity remains an open research area. The major objective of…
arXiv:2608.14172v1 Announce Type: cross Abstract: Text-to-image diffusion models have two major drawbacks that severely limit their practical utility: (1) standard models lack an intrinsic mechanism for continuous, concept-specific guidance (e.g., for precisely controlling how aesthetically pleasing an image looks), and…
arXiv:2608.13799v1 Announce Type: new Abstract: This paper presents an event-driven learning and benchmarking framework for the Dynamic Multi-Depot Vehicle Routing Problem with progressively revealed requests and evolving vehicle states. Masked MLP and Transformer policies are trained through behavior cloning and…
arXiv:2608.14329v1 Announce Type: cross Abstract: Principle-based regulation, with evaluative standards such as “fair, clear, and not misleading” or “deliver good outcomes”, cannot be reduced to binary predicates, and LLM-as-judge is increasingly used as the substitute. Our position is that any…
arXiv:2608.14254v1 Announce Type: cross Abstract: Fugitive emissions from waste sites increasingly expose communities to toxic and odorous gases, yet public-health responses remain largely retrospective, with episodes investigated only after residents have been exposed. Here we show that the meteorological drivers…
arXiv:2608.08904v2 Announce Type: replace-cross Abstract: How much of a vision-language model’s (VLM) spatial understanding remains after the action post-training process of building a vision-language-action model (VLA)? We probe depth perception, a primitive of spatiogeometric understanding, from every decoder layer of…
arXiv:2608.14112v1 Announce Type: cross Abstract: Scientific simulations often produce scalar volumes faster than they can be stored, transferred, and loaded, while in situ reduction must use only a limited share of simulation resources. This work encodes scalar fields as anisotropic…
arXiv:2608.10433v3 Announce Type: replace Abstract: Models increasingly accompany time-series forecasts with temporal reports—delays, leading indicators, or selected history—yet a correct report need not describe the computation that produced the forecast. We formalize this as a three-stage certification problem: emph{recoverability} of…
arXiv:2604.04930v2 Announce Type: replace-cross Abstract: Large reasoning models rely on long chain-of-thought generation to solve complex problems, but extended reasoning often incurs substantial computational cost and can even degrade performance due to overthinking. A key challenge is determining when the…
arXiv:2507.02169v2 Announce Type: replace Abstract: Machine learning models are often used in applications where their inputs change due to routine interactions, strategic manipulation, or noise. In such settings, models can undermine safety as these changes lead them to predict over…
