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Home/Authors/Jun Xie

Jun Xie

4 indexed papers

Recent (6 mo)
4
With code
0
Influential cites
0
Benchmarked
0

Publications per year

4
26

Top categories

ML×2Vision×2AI×2Info Retrieval×1Crypto×1

Frequent co-authors

Pengjun Xie2×
Peng Sun1×
Zhenglin Cheng1×
Deyuan Liu1×
Xinyi Shang1×
Tao Lin1×

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.

CORE-Bench: A Comprehensive Benchmark for Code Retrieval in the Era of Agentic Coding

This paper introduces CORE-Bench, a comprehensive benchmark for code retrieval in agentic coding.

ECHO: Prune to act, trace to learn with selective turn memory in agentic RL

The paper proposes ECHO, a selective turn-memory framework for long-horizon language agents that addresses history collapse and traceable learning through source-indexed reconstruction.

Three-Body Scattering for Generative Modeling

This paper introduces a new approach for high-dimensional one-step generation using a Three-Body Scattering Model (TBSM) with a proper distributional energy.

Highlighted terms show continued research focus across papers

Papers

cs.LGcs.CVEmpiricalRecentJul 20, 2026

Three-Body Scattering for Generative Modeling

Peng Sun, Zhenglin Cheng, Deyuan Liu, Jun Xie +2 more

This paper introduces a new approach for high-dimensional one-step generation using a Three-Body Scattering Model (TBSM) with a proper distributional energy.

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cs.LGcs.AIEmpirical
Recent
Jun 30, 2026

ECHO: Prune to act, trace to learn with selective turn memory in agentic RL

Zijun Xie, Binbin Zheng, Enlei Gong, Jihua Liu +6 more

The paper proposes ECHO, a selective turn-memory framework for long-horizon language agents that addresses history collapse and traceable learning through source-indexed reconstruction.

View →
cs.IREmpiricalRecentJun 10, 2026

CORE-Bench: A Comprehensive Benchmark for Code Retrieval in the Era of Agentic Coding

Fuwei Zhang, Yanzhao Zhang, Mingxin Li, Dingkun Long +4 more

This paper introduces CORE-Bench, a comprehensive benchmark for code retrieval in agentic coding.

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…

View →