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Junxian Li

11 papers hereh-index 9366 citations33 works total

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

author position
  • first author1
  • middle author9

Across the 10 of 11 papers where every author was matched, so the position is known.

fields
  • cs.CV4
  • cs.AI3
  • cs.CL1
  • cs.CR1
  • cs.HC1
  • q-bio.BM1
Homepage
same name
  • Junxian Li — 9 papers, h 6
  • Junxian Li — 2 papers
  • Junxian Li — 1 paper
  • Junxian Li — 1 paper, h 4
  • Junxian Li — 1 paper
  • Junxian Li — 1 paper, h 4

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
20242026
most citedMol-R1: Towards Explicit Long-CoT Reasoning in Molecule Discovery

1 citations · 1 across the 6 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2026

StreamMeCo: Long-Term Agent Memory Compression for Efficient Streaming Video Understanding

Junxi Wang, Te Sun, Jiayi Zhu +6

Vision agent memory has shown remarkable effectiveness in streaming video understanding. However, storing such memory for videos incurs substantial memory overhead, leading to high…

cs.CV2026

PlanViz: Evaluating Planning-Oriented Image Generation and Editing for Computer-Use Tasks

Junxian Li, Kai Liu, Leyang Chen +7

Unified multimodal models (UMMs) have shown impressive capabilities in generating natural images and supporting multimodal reasoning. However, their potential in supporting compute…

cs.CV2025

S2-MLLM: Boosting Spatial Reasoning Capability of MLLMs for 3D Visual Grounding with Structural Guidance

Beining Xu, Siting Zhu, Zhao Jin +2

3D Visual Grounding (3DVG) focuses on locating objects in 3D scenes based on natural language descriptions, serving as a fundamental task for embodied AI and robotics. Recent advan…

cs.CV2024

Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning

Di Zhang, Junxian Li, Jingdi Lei +10

Vision-language models (VLMs) have shown remarkable advancements in multimodal reasoning tasks. However, they still often generate inaccurate or irrelevant responses due to issues…

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