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Chen Wang

17 papers hereh-index 63.4k citations24 works total

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

author position
  • first author5
  • middle author11
  • last author1

Across the 17 of 17 papers where every author was matched, so the position is known.

fields
  • cs.LG5
  • cs.AI4
  • cs.CL3
  • cs.DC2
  • cs.CE1
  • cs.CV1
same name
  • Chen Wang — 7 papers, h 4
  • Chen Wang — 7 papers, h 5
  • Chen Wang — 6 papers, h 3
  • Chen Wang — 6 papers, h 25
  • Chen Wang — 6 papers, h 3
  • Chen Wang — 6 papers, h 13

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

collaborators
Showing cs.AIShow all

4 papers · 1 filter

cs.AI2026

Implicit Compression Regularization: Concise Reasoning via Internal Shorter Distributions in RL Post-Training

Chen Wang, Hexuan Deng, Yining Zhang +5

Reinforcement learning with verifiable rewards improves LLM reasoning but often induces overthinking, where models generate unnecessarily long reasoning traces. Existing methods ma…

cs.AI2026

Targeted Exploration via Unified Entropy Control for Reinforcement Learning

Chen Wang, Lai Wei, Yanzhi Zhang +5

Recent advances in reinforcement learning (RL) have improved the reasoning capabilities of large language models (LLMs) and vision-language models (VLMs). However, the widely used…

cs.AI2025

How Modality Shapes Perception and Reasoning: A Study of Error Propagation in ARC-AGI

Bo Wen, Chen Wang, Erhan Bilal

ARC-AGI and ARC-AGI-2 measure generalization-through-composition on small color-quantized grids, and their prize competitions make progress on these harder held-out tasks a meaning…

cs.AI2025

Voice-based AI Agents: Filling the Economic Gaps in Digital Health Delivery

Bo Wen, Chen Wang, Qiwei Han +4

The integration of voice-based AI agents in healthcare presents a transformative opportunity to bridge economic and accessibility gaps in digital health delivery. This paper explor…

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