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Yuxuan Yao

11 papers hereh-index 6360 citations15 works total

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

author position
  • first author2
  • middle author5

Across the 7 of 11 papers where every author was matched, so the position is known.

fields
  • cs.CL4
  • cs.CV3
  • cs.LG2
  • cs.AI1
  • cs.HC1
same name
  • Yuxuan Yao — 8 papers, h 6
  • Yuxuan Yao — 7 papers, h 7
  • Yuxuan Yao — 3 papers, h 2
  • Yuxuan Yao — 2 papers, h 8
  • Yuxuan Yao — 2 papers, h 3
  • Yuxuan Yao — 1 paper, h 2

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

most citedFrom System 1 to System 2: A Survey of Reasoning Large Language Models

20 citations · 20 across the 10 of their papers we have counts for

collaborators
Showing cs.CLShow all

4 papers · 1 filter

cs.CL2026

Economy of Minds: Emerging Multi-Agent Intelligence with Economic Interactions

Zhenting Qi, Huangyuan Su, Ao Qu +13

How can a population of agents self-orchestrate and self-adapt into stronger collective intelligence without centralized control? Inspired by Friedrich Hayek's economic theory of d…

cs.CL2025

Activation-Guided Consensus Merging for Large Language Models

Yuxuan Yao, Shuqi Liu, Zehua Liu +6

Recent research has increasingly focused on reconciling the reasoning capabilities of System 2 with the efficiency of System 1. While existing training-based and prompt-based appro…

cs.CL2025

Unlocking Efficient Long-to-Short LLM Reasoning with Model Merging

Han Wu, Yuxuan Yao, Shuqi Liu +7

The transition from System 1 to System 2 reasoning in large language models (LLMs) has marked significant advancements in handling complex tasks through deliberate, iterative think…

cs.CL2025

LoRE-Merging: Exploring Low-Rank Estimation For Large Language Model Merging

Zehua Liu, Han Wu, Yuxuan Yao +4

While most current approaches rely on further training techniques, such as fine-tuning or reinforcement learning, to enhance model capacities, model merging stands out for its abil…

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