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Xiangyu Wang

6 papers hereh-index 14639 citations45 works total

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

author position
  • first author1
  • middle author5

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

fields
  • cs.AI4
  • cs.CL1
  • cs.LG1
same name
  • Xiangyu Wang — 23 papers, h 16
  • Xiangyu Wang — 10 papers, h 10
  • Xiangyu Wang — 7 papers, h 3
  • Xiangyu Wang — 6 papers, h 3
  • Xiangyu Wang — 6 papers, h 4
  • Xiangyu Wang — 5 papers, 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 citedIntegrating Large Language Model for Improved Causal Discovery

19 citations · 34 across the 6 of their papers we have counts for

collaborators
Showing cs.AIShow all

4 papers · 1 filter

cs.AI2026

ReasonAlloc: Hierarchical Decoding-Time KV Cache Budget Allocation for Reasoning Models

Wenhao Liu, Hao Shi, Yunhe Li +7

Long chain-of-thought (CoT) trajectories in large language model (LLM) reasoning cause severe inference bottlenecks due to rapid key-value (KV) cache growth. Current decoding-time…

cs.AI2025★ 1 cited

Adaptive Selection of Symbolic Languages for Improving LLM Logical Reasoning

Xiangyu Wang, Haocheng Yang, Fengxiang Cheng +1

Large Language Models (LLMs) still struggle with complex logical reasoning. While previous works achieve remarkable improvements, their performance is highly dependent on the corre…

cs.AI2023★ 6 cited

Causal Structure Learning Supervised by Large Language Model

Taiyu Ban, Lyuzhou Chen, Derui Lyu +2

Causal discovery from observational data is pivotal for deciphering complex relationships. Causal Structure Learning (CSL), which focuses on deriving causal Directed Acyclic Graphs…

cs.AI2023★ 19 cited

Integrating Large Language Model for Improved Causal Discovery

Taiyu Ban, Lyuzhou Chen, Derui Lyu +4

Recovering the structure of causal graphical models from observational data is an essential yet challenging task for causal discovery in scientific scenarios. Domain-specific causa…

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