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Xing Xie

14 papers hereh-index 9267 citations18 works total

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

author position
  • middle author3
  • last author9

Across the 12 of 14 papers where every author was matched, so the position is known.

fields
  • cs.IR7
  • cs.CL4
  • cs.AI2
  • cs.CV1
same name
  • Xing Xie — 35 papers, h 94
  • Xing Xie — 30 papers, h 24
  • Xing Xie — 21 papers, h 18
  • Xing Xie — 21 papers, h 7
  • Xing Xie — 21 papers, h 15
  • Xing Xie — 18 papers, h 10

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 citedRecExplainer: Aligning Large Language Models for Explaining Recommendation Models

24 citations · 47 across the 12 of their papers we have counts for

collaborators
Showing 2026Show all

4 papers · 1 filter

cs.AI2026

Preference Data Selection for Mitigating the Alignment Tax in Large Language Models

Minsu Kim, Jianxun Lian, Xing Xie +1

Aligning large language models to human preferences is crucial for real-world deployment but frequently incurs an alignment tax, leading to the catastrophic forgetting of pre-train…

cs.CL2026

Visuospatial Perspective Taking in Multimodal Language Models

Jonathan Prunty, Seraphina Zhang, Patrick Quinn +3

As multimodal language models (MLMs) are increasingly used in social and collaborative settings, it is crucial to evaluate their perspective-taking abilities. Existing benchmarks l…

cs.AI2026

To Think or Not To Think, That is The Question for Large Reasoning Models in Theory of Mind Tasks

Nanxu Gong, Haotian Li, Sixun Dong +3

Theory of Mind (ToM) assesses whether models can infer hidden mental states such as beliefs, desires, and intentions, which is essential for natural social interaction. Although re…

cs.CV2026

Proact-VL: A Proactive VideoLLM for Real-Time AI Companions

Weicai Yan, Yuhong Dai, Qi Ran +6

Proactive and real-time interactive experiences are essential for human-like AI companions, yet face three key challenges: (1) achieving low-latency inference under continuous stre…

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