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Jinlan Fu

16 papers hereh-index 9275 citations17 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author2
  • middle author12
  • last author2

Across the 16 of 16 papers where every author was matched, so the position is known.

fields
  • cs.CL10
  • cs.RO3
  • cs.CV2
  • cs.AI1
same name
  • Jinlan Fu — 18 papers, h 16
  • Jinlan Fu — 6 papers, h 4
  • Jinlan Fu — 3 papers, h 3
  • Jinlan Fu — 1 paper
  • Jinlan Fu — 1 paper, h 3

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20232026
most citedChain of Thought Explanation for Dialogue State Tracking

2 citations · 4 across the 16 of their papers we have counts for

collaborators
Showing 2026Show all

4 papers · 1 filter

cs.RO2026

World Action Models: The Next Frontier in Embodied AI

Siyin Wang, Junhao Shi, Zhaoyang Fu +11

Vision-Language-Action (VLA) models have achieved strong semantic generalization for embodied policy learning, yet they learn reactive observation-to-action mappings without explic…

cs.CV2026

Analyzing Reasoning Consistency in Large Multimodal Models under Cross-Modal Conflicts

Zhihao Zhu, Jiafeng Liang, Shixin Jiang +5

Large Multimodal Models (LMMs) have demonstrated impressive capabilities in video reasoning via Chain-of-Thought (CoT). However, the robustness of their reasoning chains remains qu…

cs.CV2026

HERMES: KV Cache as Hierarchical Memory for Efficient Streaming Video Understanding

Haowei Zhang, Shudong Yang, Jinlan Fu +2

Recent advancements in Multimodal Large Language Models (MLLMs) have demonstrated significant improvement in offline video understanding. However, extending these capabilities to s…

cs.CL2026

FutureOmni: Evaluating Future Forecasting from Omni-Modal Context for Multimodal LLMs

Qian Chen, Jinlan Fu, Changsong Li +3

Although Multimodal Large Language Models (MLLMs) demonstrate strong omni-modal perception, their ability to forecast future events from audio-visual cues remains largely unexplore…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.