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Home/Authors/Chen He

Chen He

3 indexed papers

Recent (6 mo)
3
With code
0
Influential cites
0
Benchmarked
0

Publications per year

3
26

Top categories

AI×3ML×1NLP×1Vision×1Crypto×1

Frequent co-authors

Chen Henry Wu1×
Aditi Raghunathan1×
Yuhao Wu1×
Lei Wang1×
Wenxuan Zhang1×
Fumin Shen1×

Research Timeline

2026
FraudBench: A Multimodal Benchmark for Detecting AI-Generated Fraudulent Refund Evidence

The paper introduces FraudBench, a multimodal benchmark designed to detect AI-generated fraudulent refund evidence, finding that current AI models struggle significantly with claim-conditioned fake-damage detection.

Self-Trained Verification for Training- and Test-Time Self-Improvement

The paper proposes Self-Trained Verification (STV), a novel method that trains verifiers to catch self-generated errors by leveraging reference solutions, significantly boosting performance in both test-time refinement and training-time self-improvement.

Diagnosing Harmful Continuation in Answer-Correct Long-CoT Training Traces

The paper identifies and demonstrates that post-conclusion continuation in answer-correct long-CoT traces is harmful during LLM fine-tuning, proposing a method to cut this continuation.

Highlighted terms show continued research focus across papers

Papers

cs.LGcs.AIcs.CLRecentMay 28, 2026

Self-Trained Verification for Training- and Test-Time Self-Improvement

Chen Henry Wu, Aditi Raghunathan

The paper proposes Self-Trained Verification (STV), a novel method that trains verifiers to catch self-generated errors by leveraging reference solutions, significantly boosting performance in both te…

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cs.AIRecentMay 28, 2026

Diagnosing Harmful Continuation in Answer-Correct Long-CoT Training Traces

Chen He, Yuhao Wu, Lei Wang, Wenxuan Zhang +1 more

The paper identifies and demonstrates that post-conclusion continuation in answer-correct long-CoT traces is harmful during LLM fine-tuning, proposing a method to cut this continuation.

View →
cs.CVcs.AIcs.CRRecentMay 9, 2026

FraudBench: A Multimodal Benchmark for Detecting AI-Generated Fraudulent Refund Evidence

Xinyu Yan, Boyang Chen, Jiaming Zhang, Tiantong Wu +11 more

The paper introduces FraudBench, a multimodal benchmark designed to detect AI-generated fraudulent refund evidence, finding that current AI models struggle significantly with claim-conditioned fake-da…

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