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Rapid advances in large language models (LLMs) have empowered autonomous agents to generate social networks, communicate, and form shared and diverging opinions on political issues. However, our understanding of their collective behaviours and underlying mechanisms remains incomplete. In this paper, we simulate netwo...
This work proposes SkillMaster, a training framework that teaches agents to create new skills, refine existing skills, and select accumulated skills during task solving, and introduces DualAdv-GRPO, which separately estimates advantages for task-solving actions and skill-editing decisions, stabilizing joint training ac...
EnterpriseBench is introduced, a benchmark that evaluates LLM agents across this spectrum, from static question answering to dynamic decision-making, and shows that it provides a practical benchmark for evaluating LLM agents in realistic enterprise strategic reasoning and decision-making.
MiniCache is a reusable program caching framework that transforms Program-of-Thought programs into parameterized cache objects, enabling reusable computation across structurally similar requests, and consistently improves the trade-off between inference latency, cache reuse, and task quality.
Outcome-Verified Comparative Self-Distillation (OVCSD) is proposed, which organizes failed student rollouts into a prefix tree, adaptively invokes a skill-conditioned teacher from student-reached states, and retains only outcome-verified successful continuations.
The results suggest that the sweet spot for small models in large-model inference systems lies not in solving complex tasks independently, but in performing lightweight, structured, and verifiable auxiliary operations.
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