2 citations · 2 across the 6 of their papers we have counts for
6 papers
When "Must" Becomes "Maybe": Erosion of Workflow Constraints in LLM Agent Handoffs
Yiheng Sun, Huifei Wang, Yancheng Zhu +3
Large language model (LLM) agents build on one another's work through summaries, plans, and other intermediate artifacts. These records let a downstream agent continue without revi…
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…
Enhancing the Spatial Awareness Capability of Multi-Modal Large Language Model
Yongqiang Zhao, Zhenyu Li, Zhi Jin +6
The Multi-Modal Large Language Model (MLLM) refers to an extension of the Large Language Model (LLM) equipped with the capability to receive and infer multi-modal data. Spatial awa…
Enhancing Subtask Performance of Multi-modal Large Language Model
Yongqiang Zhao, Zhenyu Li, Feng Zhang +2
Multi-modal Large Language Model (MLLM) refers to a model expanded from a Large Language Model (LLM) that possesses the capability to handle and infer multi-modal data. Current MLL…