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Xiaoyu Liang

6 papers hereh-index 249 citations6 works total

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

author position
  • first author1
  • middle author4

Across the 5 of 6 papers where every author was matched, so the position is known.

fields
  • cs.AI1
  • cs.CL1
  • cs.CR1
  • cs.HC1
  • cs.IR1
  • cs.LG1
same name
  • Xiaoyu Liang — 5 papers, h 5
  • Xiaoyu Liang — 3 papers, h 2
  • Xiaoyu Liang — 3 papers, h 3
  • Xiaoyu Liang — 2 papers, h 2
  • Xiaoyu Liang — 1 paper, h 2
  • Xiaoyu Liang — 1 paper, h 1

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 citedLLM-Powered GUI Agents in Phone Automation: Surveying Progress and Prospects

2 citations · 2 across the 5 of their papers we have counts for

collaborators
Showing 2026Show all

4 papers · 1 filter

cs.CL2026

Locality Matters for Training-Free Audio Token Compression in Audio-Language Models

Jiale Luo, Xiaoyu Liang, Haoji Hu

Audio-language models (ALMs) are increasingly used for audio captioning, question answering, and open-ended audio understanding, but their inference cost remains high when audio in…

cs.AI2026

Echo: Learning from Experience Data via User-Driven Refinement

Hande Dong, Xiaoyun Liang, Jiarui Yu +15

Static "human data" faces inherent limitations: it is expensive to scale and bounded by the knowledge of its creators. Continuous learning from "experience data" - interactions bet…

cs.LG2026

Reading Calibrated Uncertainty from Language Model Trajectories

Aliai Eusebi, Alexander Herzog, Xiaoyu Liang +3

The maximum softmax probability (MSP) represents a default approach when evaluating uncertainty quantification for language model generation with structured output. Although cheap,…

cs.IR2026

Learn Before Represent: Bridging Generative and Contrastive Learning for Domain-Specific LLM Embeddings

Xiaoyu Liang, Yuchen Peng, Jiale Luo +3

Large Language Models (LLMs) adapted via contrastive learning excel in general representation learning but struggle in vertical domains like chemistry and law, primarily due to a l…

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