4 papers
When "Must" Becomes "Maybe": Constraint Weakening in LLM Agent Workflows
Yiheng Sun, Huifei Wang, Yancheng Zhu +3
Large language model (LLM) agents coordinate complex tasks through multi-role and multi-stage workflows. Upstream state is repeatedly transformed into intermediate language artifac…
Towards On-Policy Data Evolution for Visual-Native Multimodal Deep Search Agents
Shijue Huang, Hangyu Guo, Guanting Dong +8
Multimodal deep search requires an agent to solve open-world problems by chaining search, tool use, and visual reasoning over evolving textual and visual context. Two bottlenecks l…
Agent-World: Scaling Real-World Environment Synthesis for Evolving General Agent Intelligence
Guanting Dong, Junting Lu, Junjie Huang +17
Large language models are increasingly expected to serve as general-purpose agents that interact with external, stateful tool environments. The Model Context Protocol (MCP) and bro…
Limited Reasoning Space: The cage of long-horizon reasoning in LLMs
Zhenyu Li, Guanlin Wu, Cheems Wang +1
The test-time compute strategy, such as Chain-of-Thought (CoT), has significantly enhanced the ability of large language models to solve complex tasks like logical reasoning. Howev…