arXiv:2510.06141v5 Announce Type: replace
Abstract: Convergence in high-probability (HP) has attracted increasing interest, due to implying exponentially decaying tail bounds and strong guarantees for individual runs of an algorithm. While many works …
arXiv:2601.00290v2 Announce Type: replace
Abstract: Clinical trials constitute a critical yet exceptionally challenging and costly stage of drug development (\$2.6B per drug), where protocols are encoded as complex natural language documents, motivati…
arXiv:2603.16663v3 Announce Type: replace-cross
Abstract: The AIED community envisions AI evolving “from tools to teammates,” yet our understanding of AI teammates remains limited to dyadic human-AI interactions. We offer a different vantage point: a …
arXiv:2604.02578v1 Announce Type: cross
Abstract: Humans exhibit remarkable abilities to coordinate in groups. As large language models (LLMs) become more capable, it remains an open question whether they can demonstrate comparable adaptive coordinati…
arXiv:2604.02668v1 Announce Type: cross
Abstract: Large language models (LLMs) often exhibit sycophancy: agreement with user stance even when it conflicts with the model’s opinion. While prior work has mostly studied this in single-agent settings, it …
arXiv:2604.02674v1 Announce Type: cross
Abstract: Large Language Model (LLM) multi-agent systems are increasingly deployed as interacting agent societies, yet scaling these systems often yields diminishing or unstable returns, the causes of which rema…
arXiv:2604.02767v1 Announce Type: cross
Abstract: When Agent A delegates to Agent B, which invokes Tool C on behalf of User X, no existing framework can answer: whose authorization chain led to this action, and where did it violate policy? This paper …
arXiv:2604.02334v1 Announce Type: new
Abstract: As large language models (LLM)-driven agents transition from isolated task solvers to persistent digital entities, the emergence of the Agentic Web, an ecosystem where heterogeneous agents autonomously i…
arXiv:2604.02460v1 Announce Type: new
Abstract: Recent work reports strong performance from multi-agent LLM systems (MAS), but these gains are often confounded by increased test-time computation. When computation is normalized, single-agent systems (S…
arXiv:2508.15555v3 Announce Type: replace-cross
Abstract: Metric aggregation divergence is a hidden confound in agent-based model policy search: when optimization, tournament evaluation, and statistical validation independently implement outcome metri…