14 papers
Attributing Emergence in Million-Agent Systems
Ling Tang, Jilin Mei, Qian Chen +6
Large language models (LLMs) can simulate human-like reasoning and decision-making in individual agents. LLM-powered multi-agent systems (MAS) combine such agents to simulate popul…
HLL: Can Agents Cross Humanity's Last Line of Verification?
Xinhao Song, Su Su, Sirui Song +6
Multimodal agents are increasingly expected to operate interfaces on behalf of users, raising a central deployment question: can they truly substitute for humans in workflows that…
Respecting Self-Uncertainty in On-Policy Self-Distillation for Efficient LLM Reasoning
Junlong Ke, Zichen Wen, Weijia Li +2
On-policy self-distillation trains a reasoning model on its own rollouts while a teacher, often the same model conditioned on privileged context, provides dense token-level supervi…
DataMaster: Data-Centric Autonomous AI Research
Yaxin Du, Xiyuan Yang, Zhifan Zhou +12
As model families, training recipes, and compute budgets become increasingly standardized, further gains in machine learning systems depend increasingly on data. Yet data engineeri…
EvoStreaming: Your Offline Video Model Is a Natively Streaming Assistant
Zichen Wen, Boxue Yang, Junlong Ke +5
Streaming video understanding demands more than watching longer videos: assistants must decide when to speak in real time, balancing responsiveness against verbosity. Yet most vide…
AgentSlimming: Towards Efficient and Cost-Aware Multi-Agent Systems
Yulang Chen, Haoxuan Peng, Jinyan Liu +3
Large Language Model-based Multi-Agent Systems (MAS) have demonstrated remarkable capabilities in complex tasks. However, manually designing optimal communication topologies is lab…