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researcher

Shaowen Wang

16 papers hereh-index 5261 citations16 works total

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

author position
  • first author4
  • middle author9
  • last author1

Across the 14 of 16 papers where every author was matched, so the position is known.

fields
  • cs.LG7
  • cs.AI4
  • cs.CL2
  • cs.IR2
  • cs.AR1
same name
  • Shaowen Wang — 7 papers, h 44
  • Shaowen Wang — 6 papers, h 3
  • Shaowen Wang — 5 papers, h 1
  • Shaowen Wang — 3 papers, h 2
  • Shaowen Wang — 2 papers, h 4
  • Shaowen Wang — 1 paper, h 3

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 citedLoRA-GA: Low-Rank Adaptation with Gradient Approximation

4 citations · 4 across the 12 of their papers we have counts for

collaborators
Showing cs.AIShow all

4 papers · 1 filter

cs.AI2026

MemComposer: Learning to Assemble Memories for LLM Agents via Reinforcement Learning

Yixuan Xie, Xueqiang Xu, Xinyi Fan +8

Agent memory enables large language model (LLM) agents to reuse past experiences without updating their parameters. However, semantic similarity alone does not guarantee memory use…

cs.AI2026

AdaRoPE: Not All Attention Heads Should Rotate and Scale Equally

Shaowen Wang, Yuke Zheng, Tansheng Zhu +4

Rotary Position Embedding (RoPE) is widely adopted in Transformers to encode positional information, yet standard implementations enforce a uniform frequency schedule and scaling a…

cs.AI2026

Retrieval is Cheap, Show Me the Code: Executable Multi-Hop Reasoning for Retrieval-Augmented Generation

Jiashuo Sun, Jimeng Shi, Yixuan Xie +10

Retrieval-Augmented Generation (RAG) has become a standard approach for knowledge-intensive question answering, but existing systems remain brittle on multi-hop questions, where so…

cs.AI2025

Understanding LLM Behaviors via Compression: Data Generation, Knowledge Acquisition and Scaling Laws

Zhixuan Pan, Shaowen Wang, Jian Li

Large Language Models (LLMs) have demonstrated remarkable capabilities across numerous tasks, yet principled explanations for their underlying mechanisms and several phenomena, suc…

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