1 citations · 1 across the 8 of their papers we have counts for
9 papers
Benchmarking LLMs for Community Governance Simulation with Life-history Narratives
Xu Chen, Yuanzi Li, Lei Wang +6
Effective community governance hinges on understanding what specific residents think and need. Recent work has used large language models (LLMs) to simulate human respondents, offe…
FedMPT: Federated Multi-label Prompt Tuning of Vision-Language Models
Xucong Wang, Pengkun Wang, Zhe Zhao +3
Multi-Label Recognition (MLR) based on Vision-Language Models (VLMs) aims to leverage their pre-trained knowledge to better adapt complex recognition scenarios, thereby enhancing m…
AcademiClaw: When Students Set Challenges for AI Agents
Junjie Yu, Pengrui Lu, Weiye Si +75
Benchmarks within the OpenClaw ecosystem have thus far evaluated exclusively assistant-level tasks, leaving the academic-level capabilities of OpenClaw largely unexamined. We intro…
The MiniMax-M2 Series: Mini Activations Unleashing Max Real-World Intelligence
Aili Chen, Aonian Li, Baichuan Zhou +215
We introduce the MiniMax-M2 series, a family of Mixture-of-Experts language models built around the principle that mini activations can unleash maximum real-world intelligence. The…
Exploring Multiple High-Scoring Subspaces in Generative Flow Networks
Xuan Yu, Xu Wang, Rui Zhu +2
As a probabilistic sampling framework, Generative Flow Networks (GFlowNets) show strong potential for constructing complex combinatorial objects through the sequential composition…
Partial GFlowNet: Accelerating Convergence in Large State Spaces via Strategic Partitioning
Xuan Yu, Xu Wang, Rui Zhu +2
Generative Flow Networks (GFlowNets) have shown promising potential to generate high-scoring candidates with probability proportional to their rewards. As existing GFlowNets freely…
AgentAsk: Multi-Agent Systems Need to Ask
Bohan Lin, Kuo Yang, Zelin Tan +8
Multi-agent systems (MAS) built on large language models promise improved problem-solving through collaboration, yet they often fail to consistently outperform strong single-agent…
Planning-Augmented Sampling with Early Guidance for High-Reward Discovery
Rui Zhu, Yudong Zhang, Xuan Yu +3
Generative Flow Networks (GFlowNets) enable structured generation with inherent diversity, but existing sampling strategies often rely on weak guided exploration, slowing early dis…
Advancing DRL Agents in Commercial Fighting Games: Training, Integration, and Agent-Human Alignment
Chen Zhang, Qiang He, Zhou Yuan +4
Deep Reinforcement Learning (DRL) agents have demonstrated impressive success in a wide range of game genres. However, existing research primarily focuses on optimizing DRL compete…