Automatic prompt generation plays a crucial role in enabling general-purpose multi-agent systems to perform diverse tasks autonomously. Existing methods typically evaluate prompts based on their immediate task performance, overlooking the intrinsic qualities that determine their reliability. This outcome-centric view n...
Ke Chen, Xucheng Yu, Yufei Zhou et al.· 0 citations
Executing long-horizon tool invocations in real-world environments is severely challenged by dynamic user intent noise. Existing methods attempt robustness via implicit history scanning or text compression, yet predominantly assume perfect instructions in simplistic scenarios. Inevitably, under fluctuating contexts, ob...
Dingwei Zhu, Jiahan Li, Chengjun Pan et al.· 0 citations
Large language models (LLMs) struggle to accumulate evidence across multiple rounds of user interactions, failing to update their beliefs in a manner consistent with Bayesian inference. Existing solutions require fine-tuning on sensitive user interaction data, limiting their applicability in privacy-conscious settings....
Fangzhou Lin, Zilin Dai, Shuo Xing et al.· 0 citations
Large Language Models (LLMs) offer transformative solutions across many domains, but healthcare integration is hindered by strict data privacy constraints. Clinical narratives are dense with ambiguous acronyms, misinterpretation these abbreviations can precipitate severe outcomes like life-threatening medication errors...
Manjushree B. Aithal, Ph. D., Alexander Kotz et al.· 0 citations
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The results provide significant, multi-perspective evidence that social effects of data could be a causal driver of LLM capability decay in continual pre-training, thereby motivating routine"cognitive health checks" for deployed and evolving LLMs.
Shuo Xing, Junyuan Hong, Yi-Fan Wang et al.· arXiv.org· 3 citations
Socio-economic causal effects depend heavily on their institutional and environmental contexts. The same intervention can produce different, even opposite, effects across regulatory regimes, market conditions, time periods, or populations. This poses a challenge for large language models (LLMs) in decision-support role...
Donggyu Lee, Hyeok Yun, Meeyoung Cha et al.· 0 citations
Large language models (LLMs) have recently shown strong progress on scientific reasoning, yet two major bottlenecks remain. First, explicit retrieval fragments reasoning, imposing a hidden "tool tax" of extra tokens and steps. Second, multi-agent pipelines often dilute strong solutions by averaging across all candidate...
Xiangru Tang, Wanghan Xu, Yujie Wang et al.· 0 citations
Language models (LMs) are used for a diverse range of tasks, from question answering to writing fantastical stories. In order to reliably accomplish these tasks, LMs must be able to discern the modal category of a sentence (i.e., whether it describes something that is possible, impossible, completely nonsensical, etc.)...
Michael A. Lepori, Jennifer Hu, Ishita Dasgupta et al.· 0 citations
Automated research with LLM agents has shown promising results in various domains. Through iterative refinement and scored verifiers, LLM agents are capable of discovering new ideas. There are different ways to scale automated research: let one agent iterate for longer, run several agents in parallel, or further let th...
Shangyin Tan, Ben Zhou, Lakshya A Agrawal et al.· 0 citations
SwarmRSI is introduced, a framework that evolves scientific agent swarms for recursive self-improvement and proposes Feedback-Guided Swarm Mutation to refine hypotheses and their implementations using experimental feedback from other agents and Complementary Swarm Recombination to reuse retained hypotheses and compleme...
Qian-Ru Meng, Xi Zhang, Konstantinos Papakostas et al.· 0 citations
A ReAct-style agent accumulates observations, intermediate results, and failed attempts in a single context. As this context grows, stale and off-path information consumes its limited capacity and obscures relevant evidence, making focused reasoning increasingly difficult. We introduce TreeFlex, a framework in which th...
Dong-Zhuo Li, Fang-Zhou Cheng, Wei-Wei Li et al.· 0 citations
Full-duplex speech systems must decide when to enter, hold, or relinquish the conversational floor, even as multiple speakers overlap or backchannel without taking it. Existing evaluations study dyadic turn-taking or end-to-end agent behavior, but lack a unified, controlled evaluation of genuine floor transitions again...
