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Xin Dong

21 papers hereh-index 14930 citations25 works total

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

author position
  • first author1
  • middle author17
  • last author1

Across the 19 of 21 papers where every author was matched, so the position is known.

fields
  • cs.CL11
  • cs.LG6
  • cs.AI3
  • cs.CR1
same name
  • Xin Dong — 24 papers, h 16
  • Xin Dong — 21 papers, h 23
  • Xin Dong — 20 papers, h 6
  • Xin Dong — 16 papers, h 17
  • Xin Dong — 9 papers, h 5
  • Xin Dong — 7 papers, h 6

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 citedSmall Language Models are the Future of Agentic AI

15 citations · 17 across the 18 of their papers we have counts for

collaborators
Showing 2026Show all

4 papers · 1 filter

cs.CL2026

SparDA: Sparse Decoupled Attention for Efficient Long-Context LLM Inference

Yaosheng Fu, Guangxuan Xiao, Xin Dong +2

Sparse attention reduces compute and memory bandwidth for long-context LLM inference. However, two key challenges remain: (1) KV cache capacity still grows with sequence length, an…

cs.CL2026

SaliMory: Orchestrating Cognitive Memory for Conversational Agents

Kai Zhang, Xinyuan Zhang, Hongda Jiang +11

Conversational agents that serve as lifelong companions must maintain persistent memory across all interactions. However, simply expanding context windows with raw retrieval degrad…

cs.LG2026

QuRL: Efficient Reinforcement Learning with Quantized Rollout

Yuhang Li, Reena Elangovan, Xin Dong +2

Reinforcement learning with verifiable rewards (RLVR) has become a trending paradigm for training reasoning large language models (LLMs). However, due to the autoregressive decodin…

cs.CL2026

GDPO: Group reward-Decoupled Normalization Policy Optimization for Multi-reward RL Optimization

Shih-Yang Liu, Xin Dong, Ximing Lu +10

As language models become increasingly capable, users expect them to provide not only accurate responses but also behaviors aligned with diverse human preferences across a variety…

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