2 citations · 2 across the 4 of their papers we have counts for
4 papers
LAVE: Latent Visual Evidence-Enhanced Planning for Video Tool-use Agents
Zijian Wang, Junnan Zhu, Rongzhen Li +7
Long-video understanding requires models to efficiently acquire and reuse sparse visual evidence from long and redundant video streams. Recent video tool-use agents address this ch…
Lung-R1: A Knowledge Graph-Guided LLM for Pulmonary Diagnostic Reasoning
Haoyang Zeng, Yuanxi Fu, Rongzhen Li +11
Diagnosing pulmonary diseases requires integrating heterogeneous evidence amid phenotypic variability and cross-disease overlap. Although large language models (LLMs) have shown pr…
ReTool-Video: Recursive Tool-Using Video Agents with Meta-Augmented Tool Grounding
Xiao Liu, Nayu Liu, Junnan Zhu +6
Video understanding requires active evidence seeking, motivating tool-augmented video agents for temporal reasoning, cross-modal understanding, and complex question answering. Exis…
CorefDRE: Document-level Relation Extraction with coreference resolution
Zhongxuan Xue, Rongzhen Li, Qizhu Dai +1
Document-level relation extraction is to extract relation facts from a document consisting of multiple sentences, in which pronoun crossed sentences are a ubiquitous phenomenon aga…