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Home/Authors/Kun Gai

Kun Gai

3 indexed papers

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

Publications per year

3
26

Top categories

Info Retrieval×2AI×2NLP×2Vision×1

Frequent co-authors

OneRec Team1×
Biao Yang1×
Boyang Ding1×
Chenglong Chu1×
Dunju Zang1×
Fei Pan1×

Research Timeline

2026
VLMs are Good Teachers for Video Reasoning via Adaptive Test-Time Optimization

The paper proposes using Vision-Language Models (VLMs) as 'teachers' to guide Video Generation Models (VGMs) during test-time optimization, significantly improving video reasoning capabilities.

Taiji: Pareto Optimal Policy Optimization with Semantics-IDs Trade-off for Industrial LLM-Enhanced Recommendation

Taiji is a novel LLM-as-Enhancer framework that optimizes recommender systems by addressing the challenges of generating high-quality reasoning data and balancing semantic and ID-based rewards.

OneReason Technical Report

The paper proposes OneReason, a framework that enhances the reasoning capability of generative recommendation models by focusing on improving item perception and structuring user behavior into coherent latent interests.

Highlighted terms show continued research focus across papers

Papers

cs.IRcs.AIcs.CLRecentJun 4, 2026

OneReason Technical Report

OneRec Team, Biao Yang, Boyang Ding, Chenglong Chu +80 more

The paper proposes OneReason, a framework that enhances the reasoning capability of generative recommendation models by focusing on improving item perception and structuring user behavior into coheren…

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cs.IRcs.AIcs.CLRecentJun 2, 2026

Taiji: Pareto Optimal Policy Optimization with Semantics-IDs Trade-off for Industrial LLM-Enhanced Recommendation

Yuecheng Li, Zeyu Song, Jing Yao, Chi Lu +2 more

Taiji is a novel LLM-as-Enhancer framework that optimizes recommender systems by addressing the challenges of generating high-quality reasoning data and balancing semantic and ID-based rewards.

View →
cs.CVRecentJun 1, 2026

VLMs are Good Teachers for Video Reasoning via Adaptive Test-Time Optimization

Junhao Cheng, Liang Hou, Tianxiong Zhong, Xin Tao +3 more

The paper proposes using Vision-Language Models (VLMs) as 'teachers' to guide Video Generation Models (VGMs) during test-time optimization, significantly improving video reasoning capabilities.

View →