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arXiv:2604.03906v1 Announce Type: new Abstract: Geoscientific systems tend to be characterized by pronounced temporal non-stationarity, arising from seasonal and climatic variability in hydrometeorological drivers, and …
arXiv:2604.03925v1 Announce Type: new Abstract: Large language models struggle to accumulate evidence across multiple rounds of user interaction, failing to update their beliefs in a …
arXiv:2604.03858v1 Announce Type: new Abstract: Training Data Attribution (TDA) seeks to trace model predictions back to influential training examples, enhancing interpretability and safety. We formulate …
arXiv:2604.04131v1 Announce Type: new Abstract: Large language model agents that use external tools are often implemented through reactive execution, in which reasoning is repeatedly recomputed …
arXiv:2604.03592v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models exhibit striking performance disparities across languages, yet the internal mechanisms driving these gaps remain poorly understood. In …
arXiv:2604.03815v1 Announce Type: new Abstract: Graph transformers have shown promise in overcoming limitations of traditional graph neural networks, such as oversquashing and difficulties in modelling …
arXiv:2604.03553v1 Announce Type: new Abstract: AI is supporting, accelerating, and automating scientific discovery across a diverse set of fields. However, AI adoption in historical research …
arXiv:2604.03888v1 Announce Type: new Abstract: This paper presents PolySwarm, a novel multi-agent large language model (LLM) framework designed for real-time prediction market trading and latency …
arXiv:2604.03588v1 Announce Type: new Abstract: AI agents operating over extended time horizons accumulate experiences that serve multiple concurrent goals, and must often maintain conflicting interpretations …
arXiv:2604.04157v1 Announce Type: new Abstract: Theory of Mind (ToM) -- the ability to model others' mental states -- is fundamental to human social cognition. Whether …
arXiv:2604.03263v1 Announce Type: new Abstract: Most current long-context language models still rely on attention to handle both local interaction and long-range state, which leaves relatively …
arXiv:2604.04043v1 Announce Type: new Abstract: Recent work has shown that fine-tuning large language models (LLMs) on insecure code or culturally loaded numeric codes can induce …