arXiv:2510.07731v1 Announce Type: new Abstract: Organic reaction mechanisms are the stepwise elementary reactions by which reactants form intermediates and products, and are fundamental to understanding chemical reactivity and designing new molecules and reactions. Although large language models (LLMs) have shown…
arXiv:2509.00626v3 Announce Type: replace-cross Abstract: Methane is a potent greenhouse gas and a major driver of climate change, making its timely detection critical for effective mitigation. Machine learning (ML) deployed onboard satellites can enable rapid detection while reducing downlink costs,…
arXiv:2510.07715v1 Announce Type: new Abstract: In real-time and safety-critical cyber-physical systems (CPSs), control synthesis must guarantee that generated policies meet stringent timing and correctness requirements under uncertain and dynamic conditions. Signal temporal logic (STL) has emerged as a powerful formalism…
arXiv:2507.15783v3 Announce Type: replace-cross Abstract: AI companion chatbots are increasingly popular with teens, while these interactions are entertaining, they also risk overuse that can potentially disrupt offline daily life. We examined how adolescents describe reliance on AI companions, mapping their…
arXiv:2510.07709v1 Announce Type: new Abstract: Can generative agents be trusted in multimodal environments? Despite advances in large language and vision-language models that enable agents to act autonomously and pursue goals in rich settings, their ability to reason about safety, coherence,…
arXiv:2506.01723v4 Announce Type: replace-cross Abstract: Idioms present a unique challenge for language models due to their non-compositional figurative interpretations, which often strongly diverge from the idiom’s literal interpretation. In this paper, we employ causal tracing to systematically analyze how pretrained…
arXiv:2510.07635v1 Announce Type: new Abstract: In many real recommender systems, novel items are added frequently over time. The importance of sufficiently presenting novel actions has widely been acknowledged for improving long-term user engagement. A recent work builds on Off-Policy Learning…
arXiv:2504.18114v2 Announce Type: replace-cross Abstract: Hallucinations pose a significant obstacle to the reliability and widespread adoption of language models, yet their accurate measurement remains a persistent challenge. While many task- and domain-specific metrics have been proposed to assess faithfulness and…
arXiv:2510.07632v1 Announce Type: new Abstract: Frontier AI models have achieved remarkable progress, yet recent studies suggest they struggle with compositional reasoning, often performing at or below random chance on established benchmarks. We revisit this problem and show that widely used…
arXiv:2502.08834v2 Announce Type: replace-cross Abstract: Diffusion models have quickly become the state-of-the-art for numerous generation tasks across many different applications. Encoding samples from the data distribution back into the models underlying prior distribution is an important task that arises in…
