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Home/Authors/Yaoming Li

Yaoming Li

3 indexed papers

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

Publications per year

3
26

Top categories

AI×3NLP×1

Frequent co-authors

Tong Yang3×
Guangxiang Zhao2×
Lin Sun2×
Xiangzheng Zhang2×
Yilun Yao2×
Qilong Shi1×

Research Timeline

2026
Harness-Bench: Measuring Harness Effects across Models in Realistic Agent Workflows

The paper introduces Harness-Bench, a diagnostic benchmark that measures how different system 'harnesses' affect LLM agent performance in realistic workflows, showing that agent capability must be reported at the model-harness configuration level.

ConMoE: Expert-Pool Consolidation via Prototype Reassignment for MoE Compression

ConMoE proposes a train-free method for compressing Mixture-of-Experts (MoE) models by consolidating the large expert pool into a smaller set of reusable prototypes and deterministically remapping all original expert calls to these prototypes.

A Primer in Post-Training Reasoning Data: What We Know About How It Works

This paper synthesizes over 150 scattered studies and reports to provide the first comprehensive primer on post-training reasoning data, organizing the field around data objects, utility, construction, and scalability.

Highlighted terms show continued research focus across papers

Papers

cs.CLcs.AIRecentJun 1, 2026

A Primer in Post-Training Reasoning Data: What We Know About How It Works

Yaoming Li, Guangxiang Zhao, Qilong Shi, Lin Sun +2 more

This paper synthesizes over 150 scattered studies and reports to provide the first comprehensive primer on post-training reasoning data, organizing the field around data objects, utility, construction…

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cs.AIRecentMay 28, 2026

ConMoE: Expert-Pool Consolidation via Prototype Reassignment for MoE Compression

Yilun Yao, Jiaming Pan, Elsie Dai, Peizhuang Cong +2 more

ConMoE proposes a train-free method for compressing Mixture-of-Experts (MoE) models by consolidating the large expert pool into a smaller set of reusable prototypes and deterministically remapping all…

View →
cs.AIRecentMay 27, 2026

Harness-Bench: Measuring Harness Effects across Models in Realistic Agent Workflows

Yilun Yao, Xinyu Tan, Chao-Hsuan Liu, Yaoming Li +8 more

The paper introduces Harness-Bench, a diagnostic benchmark that measures how different system 'harnesses' affect LLM agent performance in realistic workflows, showing that agent capability must be rep…

View →