5 papers
Qwen-AgentWorld: Language World Models for General Agents
Yuxin Zuo, Zikai Xiao, Li Sheng +30
A world model predicts environment dynamics based on current observations and actions, serving as a core cognitive mechanism for reasoning and planning. In this work, we investigat…
OccuBench: Evaluating AI Agents on Real-World Professional Tasks via Language Environment Simulation
Xiaomeng Hu, Yinger Zhang, Fei Huang +7
AI agents are expected to perform professional work across hundreds of occupational domains (from emergency department triage to nuclear reactor safety monitoring to customs import…
DeepPlanning: Benchmarking Long-Horizon Agentic Planning with Verifiable Constraints
Yinger Zhang, Shutong Jiang, Renhao Li +6
While agent evaluation has shifted toward long-horizon tasks, most benchmarks still emphasize local, step-level reasoning rather than the global constrained optimization (e.g., tim…
ToolRM: Towards Agentic Tool-Use Reward Modeling
Renhao Li, Jianhong Tu, Yang Su +6
Reward models (RMs) play a critical role in aligning large language models (LLMs) with human preferences. Yet in the domain of tool learning, the lack of RMs specifically designed…
RMTBench: Benchmarking LLMs Through Multi-Turn User-Centric Role-Playing
Hao Xiang, Tianyi Tang, Yang Su +10
Recent advancements in Large Language Models (LLMs) have shown outstanding potential for role-playing applications. Evaluating these capabilities is becoming crucial yet remains ch…