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Quantifying Consistency in LLM Logical Reasoning via Structural Uncertainty
Baishali Chaudhury, Mengdie Flora Wang, Hyunji Hayley Park +3
Large language models can arrive at the same answer through reasoning paths that are unstable, contradictory, or difficult to rank consistently -- a failure mode especially prevale…
Knowing When to Ask: Self-Gated Clarification for Hierarchical Language Agents
Aijing Gao, Yiming Kang, Mengdie Flora Wang +1
In hierarchical reasoning, failures often originate at intermediate decision points where the agent commits to a wrong branch without recognizing that it lacks critical information…
Position: Agent Should Invoke External Tools ONLY When Epistemically Necessary
Hongru Wang, Cheng Qian, Manling Li +6
As large language models evolve into tool-augmented agents, a central question remains unresolved: when is external tool use actually justified? Existing agent frameworks typically…
Avenir-Web: Human-Experience-Imitating Multimodal Web Agents with Mixture of Grounding Experts
Aiden Yiliu Li, Xinyue Hao, Shilong Liu +1
Despite advances in multimodal large language models, autonomous web agents still struggle to reliably execute long-horizon tasks on complex and dynamic web interfaces. Existing ag…
FutureX-Pro: Extending Future Prediction to High-Value Vertical Domains
Jiashuo Liu, Siyuan Chen, Zaiyuan Wang +38
Building upon FutureX, which established a live benchmark for general-purpose future prediction, this report introduces FutureX-Pro, including FutureX-Finance, FutureX-Retail, Futu…
A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence
Huan-ang Gao, Jiayi Geng, Wenyue Hua +24
Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse tasks but remain fundamentally static, unable to adapt their internal parameters to novel task…