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Home/Authors/Xiang Ren

Xiang Ren

4 indexed papers

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

Publications per year

4
26

Top categories

AI×4Crypto×2NLP×2Complexity×1ML×1Stats ML×1

Frequent co-authors

Matthew Finlayson1×
Andreas Grivas1×
Swabha Swayamdipta1×
Jiaming Wang1×
Ziteng Feng1×
Jiangtao Wu1×

Research Timeline

2026
LATTICE: Evaluating Decision Support Utility of Crypto Agents

The paper introduces LATTICE, a novel benchmark for evaluating how well crypto agents assist user decision-making, finding that different agents excel in different specific areas rather than having a single overall best performance.

InfoAtlas: A Foundation Model for Zero-Shot Statistical Dependence Estimate

InfoAtlas is a foundation model that estimates statistical mutual information (MI) in a single forward pass, achieving state-of-the-art accuracy with a massive speedup compared to traditional iterative neural estimators.

Where Do Deep-Research Agents Go Wrong? Span-Level Error Localization in Agent Trajectories

The paper introduces TELBench and the DRIFT framework to enable fine-grained, span-level error localization in deep-research agents, significantly improving the ability to pinpoint exactly where an agent's reasoning fails.

Token Rankings are Unforgeable Language Model Signatures

The paper demonstrates that token rankings provide a unique, unforgeable signature for language models, and proposes an API restriction that allows for signature presentation without leaking model parameters.

Highlighted terms show continued research focus across papers

Papers

cs.CRcs.AIcs.CCRecentJun 3, 2026

Token Rankings are Unforgeable Language Model Signatures

Matthew Finlayson, Andreas Grivas, Xiang Ren, Swabha Swayamdipta

The paper demonstrates that token rankings provide a unique, unforgeable signature for language models, and proposes an API restriction that allows for signature presentation without leaking model par…

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cs.AIRecentJun 1, 2026

Where Do Deep-Research Agents Go Wrong? Span-Level Error Localization in Agent Trajectories

Jiaming Wang, Ziteng Feng, Jiangtao Wu, Ruihao Li +7 more

The paper introduces TELBench and the DRIFT framework to enable fine-grained, span-level error localization in deep-research agents, significantly improving the ability to pinpoint exactly where an ag…

View →
cs.LGcs.AIstat.MLRecentMay 29, 2026

InfoAtlas: A Foundation Model for Zero-Shot Statistical Dependence Estimate

Zhengyang Hu, Yanzhi Chen, Hanxiang Ren, Qunsong Zeng +4 more

InfoAtlas is a foundation model that estimates statistical mutual information (MI) in a single forward pass, achieving state-of-the-art accuracy with a massive speedup compared to traditional iterativ…

View →
cs.CRcs.AIcs.CLRecentApr 29, 2026

LATTICE: Evaluating Decision Support Utility of Crypto Agents

Aaron Chan, Tengfei Li, Tianyi Xiao, Angela Chen +2 more

The paper introduces LATTICE, a novel benchmark for evaluating how well crypto agents assist user decision-making, finding that different agents excel in different specific areas rather than having a…

View →