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arXiv:2604.02527v1 Announce Type: new Abstract: The recent advancement of Large Language Models (LLMs) offers new opportunities to generate user preference data to warm-start bandits. Recent …
arXiv:2604.02488v1 Announce Type: new Abstract: Time-series causal discovery methods rely on assumptions such as stationarity, regular sampling, and bounded temporal dependence. When these assumptions are …
arXiv:2604.03057v1 Announce Type: new Abstract: This paper presents an open source methodology for allowing users to query structured non textual datasets through natural language Unlike …
arXiv:2604.02482v1 Announce Type: new Abstract: This paper proposes a framework for Structural Extrapolated Data GEneration (SEDGE) based on suitable assumptions on the underlying data generating …
arXiv:2604.02596v1 Announce Type: new Abstract: In-context learning (ICL) allows large language models (LLMs) to adapt to new tasks from a few examples, making it promising …
arXiv:2604.02910v1 Announce Type: new Abstract: Classic AI planning problems have been revisited in the Large Language Model (LLM) era, with a focus of recent benchmarks …
arXiv:2604.02660v1 Announce Type: new Abstract: As Large Language Models (LLMs) increasingly power decision-making systems across critical domains, understanding and mitigating their biases becomes essential for …
arXiv:2604.02971v1 Announce Type: new Abstract: Recent agentic search systems have made substantial progress by emphasising deep, multi-step reasoning. However, this focus often overlooks the challenges …
arXiv:2604.02351v1 Announce Type: new Abstract: Machine learning models deployed in non-stationary environments are exposed to temporal distribution shift, which can erode predictive reliability over time. …
arXiv:2604.02666v1 Announce Type: new Abstract: Optimization is as much about modeling the right problem as solving it. Identifying the right objectives, constraints, and trade-offs demands …
arXiv:2604.02869v1 Announce Type: new Abstract: Training tool-calling agents with reinforcement learning on multi-turn tasks remains challenging due to sparse outcome rewards and difficult credit assignment …
arXiv:2112.07874v2 Announce Type: cross Abstract: We examine the extent to which, in principle, linguistic graph representations can complement and improve neural language modeling. With an …