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arXiv:2604.03764v1 Announce Type: new Abstract: Large language models have found success by scaling up capabilities to work in general settings. The same can unfortunately not …
arXiv:2604.03479v1 Announce Type: new Abstract: Context-dependent sequential decision making is commonly addressed either by providing context explicitly as an input or by increasing recurrent memory …
arXiv:2604.03920v1 Announce Type: new Abstract: LLM-based social simulations can generate believable community interactions, enabling ``policy wind tunnels'' where governance interventions are tested before deployment. But …
arXiv:2604.03417v1 Announce Type: new Abstract: Network visualization has traditionally relied on heuristic metrics, such as stress, under the assumption that optimizing them leads to aesthetic …
arXiv:2604.03361v1 Announce Type: new Abstract: The modeling of bio-molecular system across molecular scales remains a central challenge in scientific research. Large language models (LLMs) are …
arXiv:2604.03754v1 Announce Type: new Abstract: Large language models (LLMs) have been shown to encode truth of statements in their activation space along a linear truth …
arXiv:2604.03785v1 Announce Type: new Abstract: Communication is essential for coordination in \emph{cooperative} multi-agent reinforcement learning under partial observability, yet \emph{cross-timestep} delays cause messages to arrive …
arXiv:2604.03465v1 Announce Type: new Abstract: As web agents rapidly evolve, an increasing body of work has moved beyond conventional atomic browser interactions and explored tool …
arXiv:2604.03478v1 Announce Type: new Abstract: In high-stakes settings where machine learning models are used to automate decision-making about individuals, the presence of algorithmic bias can …
arXiv:2604.03380v1 Announce Type: new Abstract: Generating diverse, pedagogically valid stories for Arabic early-grade reading assessments requires balancing tight constraints on vocabulary, reading level, and narrative …
arXiv:2604.03260v1 Announce Type: new Abstract: We introduce Focus, a method that learns which token pairs matter rather than approximating all of them. Learnable centroids assign …
arXiv:2604.03257v1 Announce Type: new Abstract: The ability to rigorously estimate the failure rates of large language models (LLMs) is a prerequisite for their safe deployment. …