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20232026
most citedReinforced Preference Optimization for Recommendation

2 citations · 3 across the 16 of their papers we have counts for

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6 papers · 1 filter

cs.CL2026

Preference-Aware Rubric Learning for Personalized Evaluation

Yilun Qiu, Xiaoyan Zhao, Yang Zhang +7

As Large Language Models (LLMs) evolve from general-purpose assistants to user-centric agents, personalization has become central to aligning model behavior with individual prefere…

cs.CL2026

Do LLMs and VLMs Share Neurons for Inference? Evidence and Mechanisms of Cross-Modal Transfer

Chenhang Cui, An Zhang, Yuxin Chen +5

Large vision-language models (LVLMs) have rapidly advanced across various domains, yet they still lag behind strong text-only large language models (LLMs) on tasks that require mul…

cs.CL2026

Transport and Merge: Cross-Architecture Merging for Large Language Models

Chenhang Cui, Binyun Yang, Fei Shen +5

Large language models (LLMs) achieve strong capabilities by scaling model capacity and training data, yet many real-world deployments rely on smaller models trained or adapted from…

cs.CL2026

Understanding Multilingualism in Mixture-of-Experts LLMs: Routing Mechanism, Expert Specialization, and Layerwise Steering

Yuxin Chen, Zhengzhou Cai, Xiangtian Ji +4

Mixture-of-Experts (MoE) architectures have shown strong multilingual capabilities, yet the internal mechanisms underlying performance gains and cross-language differences remain i…

cs.CL2025

Look Back to Reason Forward: Revisitable Memory for Long-Context LLM Agents

Yaorui Shi, Yuxin Chen, Siyuan Wang +5

Large language models face challenges in long-context question answering, where key evidence of a query may be dispersed across millions of tokens. Existing works equip large langu…

cs.CL2025

The Emergence of Abstract Thought in Large Language Models Beyond Any Language

Yuxin Chen, Yiran Zhao, Yang Zhang +7

As large language models (LLMs) continue to advance, their capacity to function effectively across a diverse range of languages has shown marked improvement. Preliminary studies ob…