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researcher

Yan Xia

18 papers hereh-index 13667 citations26 works total

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

author position
  • middle author17
  • last author1

Across the 18 of 18 papers where every author was matched, so the position is known.

fields
  • cs.CL14
  • cs.CV2
  • cs.LG2
same name
  • Yan Xia — 77 papers, h 34
  • Yan Xia — 22 papers, h 7
  • Yan Xia — 21 papers, h 3
  • Yan Xia — 10 papers, h 6
  • Yan Xia — 9 papers, h 10
  • Yan Xia — 8 papers, h 7

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 citedLow-code LLM: Graphical User Interface over Large Language Models

23 citations · 40 across the 18 of their papers we have counts for

collaborators
Showing 2025Show all

4 papers · 1 filter

cs.CV2025

Latent Sketchpad: Sketching Visual Thoughts to Elicit Multimodal Reasoning in MLLMs

Huanyu Zhang, Wenshan Wu, Chengzu Li +9

While Multimodal Large Language Models (MLLMs) excel at visual understanding, they often struggle in complex scenarios that require visual planning and imagination. Inspired by how…

cs.CL2025

11Plus-Bench: Demystifying Multimodal LLM Spatial Reasoning with Cognitive-Inspired Analysis

Chengzu Li, Wenshan Wu, Huanyu Zhang +6

For human cognitive process, spatial reasoning and perception are closely entangled, yet the nature of this interplay remains underexplored in the evaluation of multimodal large la…

cs.LG2025

Scaling and Beyond: Advancing Spatial Reasoning in MLLMs Requires New Recipes

Huanyu Zhang, Chengzu Li, Wenshan Wu +8

Multimodal Large Language Models (MLLMs) have demonstrated impressive performance in general vision-language tasks. However, recent studies have exposed critical limitations in the…

cs.CL2025

Imagine while Reasoning in Space: Multimodal Visualization-of-Thought

Chengzu Li, Wenshan Wu, Huanyu Zhang +5

Chain-of-Thought (CoT) prompting has proven highly effective for enhancing complex reasoning in Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs). Yet, it s…

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