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Home/Authors/Yu Shen

Yu Shen

5 indexed papers

Recent (6 mo)
5
With code
0
Influential cites
0
Benchmarked
0

Publications per year

5
26

Top categories

AI×2Crypto×2Emerging Tech×2Info Retrieval×1Robotics×1NLP×1Distributed×1Multiagent×1

Frequent co-authors

Yingqi Fan2×
Junlong Tong2×
Xiaoyu Shen2×
Bingyu Shen2×
Boyang Li2×
Xuan Lu1×

Research Timeline

2026
Toward Accountable AI-Generated Content on Social Platforms: Steganographic Attribution and Multimodal Harm Detection

The paper proposes an end-to-end forensic pipeline using steganographic attribution and multimodal harm detection to reliably trace and attribute harmful misuse of AI-generated imagery on social platforms.

HadAgent: Harness-Aware Decentralized Agentic AI Serving with Proof-of-Inference Blockchain Consensus

HadAgent introduces a decentralized AI serving system that replaces resource-intensive Proof-of-Work with Proof-of-Inference (PoI) to secure LLM agent operations and achieve fast, verifiable consensus.

ProactiveLLM: Learning Active Interaction for Streaming Large Language Models

ProactiveLLM introduces a novel framework that enables streaming LLMs to actively decide when to interact with incoming data by leveraging the model's internal states, significantly reducing latency while maintaining quality.

RiskFlow: Fast and Faithful Safety-Critical Traffic Scenario Generation

RiskFlow is a novel framework that generates realistic and safety-critical multi-agent traffic scenarios by reformulating trajectory generation as a single-pass transport problem in the action space.

CompRank: Efficient LLM Reranking via Token-Level Compression and Decoding-Free Scoring

This paper proposes CompRank, a token-efficient reranking framework for large language models that reduces redundant computation and achieves strong reranking performance.

Highlighted terms show continued research focus across papers

Papers

cs.IREmpiricalRecentJun 10, 2026

CompRank: Efficient LLM Reranking via Token-Level Compression and Decoding-Free Scoring

Xuan Lu, Haohang Huang, Yingqi Fan, Junlong Tong +4 more

This paper proposes CompRank, a token-efficient reranking framework for large language models that reduces redundant computation and achieves strong reranking performance.

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cs.ROcs.AIRecentJun 4, 2026

RiskFlow: Fast and Faithful Safety-Critical Traffic Scenario Generation

Qi Lan, Yining Tang, Yu Shen, Yi Zhou +3 more

RiskFlow is a novel framework that generates realistic and safety-critical multi-agent traffic scenarios by reformulating trajectory generation as a single-pass transport problem in the action space.

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cs.CLRecentMay 30, 2026

ProactiveLLM: Learning Active Interaction for Streaming Large Language Models

Junlong Tong, Yao Zhang, Anhao Zhao, Yingqi Fan +2 more

ProactiveLLM introduces a novel framework that enables streaming LLMs to actively decide when to interact with incoming data by leveraging the model's internal states, significantly reducing latency w…

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cs.DCcs.CRcs.ETRecentApr 15, 2026

HadAgent: Harness-Aware Decentralized Agentic AI Serving with Proof-of-Inference Blockchain Consensus

Landy Jimenez, Mariah Weatherspoon, Bingyu Shen, Yi Sheng +2 more

HadAgent introduces a decentralized AI serving system that replaces resource-intensive Proof-of-Work with Proof-of-Inference (PoI) to secure LLM agent operations and achieve fast, verifiable consensus…

View →
cs.CVcs.AIcs.CRRecentApr 12, 2026

Toward Accountable AI-Generated Content on Social Platforms: Steganographic Attribution and Multimodal Harm Detection

Xinlei Guan, David Arosemena, Tejaswi Dhandu, Kuan Huang +6 more

The paper proposes an end-to-end forensic pipeline using steganographic attribution and multimodal harm detection to reliably trace and attribute harmful misuse of AI-generated imagery on social platf…

View →