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Caifeng Shan

17 papers hereh-index 5481 citations18 works total

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

author position
  • middle author7
  • last author7

Across the 14 of 17 papers where every author was matched, so the position is known.

fields
  • cs.CV12
  • cs.CL4
  • cs.RO1
same name
  • Caifeng Shan — 20 papers, h 4
  • Caifeng Shan — 8 papers, h 23
  • Caifeng Shan — 6 papers, h 0
  • Caifeng Shan — 2 papers, h 32
  • Caifeng Shan — 2 papers, h 2
  • Caifeng Shan — 2 papers, 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 citedMME-Survey: A Comprehensive Survey on Evaluation of Multimodal LLMs

4 citations · 4 across the 15 of their papers we have counts for

collaborators
Showing cs.CLShow all

4 papers · 1 filter

cs.CL2026

Before Thinking, Learn to Decide: Proactive Routing for Efficient Visual Reasoning

Yinan Zhou, Haokun Lin, Yichen Wu +7

Large multimodal models have achieved strong reasoning on complex visual tasks, but their inference efficiency is often restricted by long chains of thought. A promising solution i…

cs.CL2026

EvoEmbedding: Evolvable Representations for Long-Context Retrieval and Agentic Memory

Chang Nie, Chaoyou Fu, Junlan Feng +1

Existing embedding models are inherently static: they encode text segments in isolation, ignoring their surrounding context and temporal order. This paper introduces EvoEmbedding,…

cs.CL2026

SpeechParaling-Bench: A Comprehensive Benchmark for Paralinguistic-Aware Speech Generation

Ruohan Liu, Shukang Yin, Tao Wang +6

Paralinguistic cues are essential for natural human-computer interaction, yet their evaluation in Large Audio-Language Models (LALMs) remains limited by coarse feature coverage and…

cs.CL2026

PersonaVLM: Long-Term Personalized Multimodal LLMs

Chang Nie, Chaoyou Fu, Yifan Zhang +2

Multimodal Large Language Models (MLLMs) serve as daily assistants for millions. However, their ability to generate responses aligned with individual preferences remains limited. P…

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