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20192025
most citedSemVLP: Vision-Language Pre-training by Aligning Semantics at Multiple Levels

20 citations · 61 across the 16 of their papers we have counts for

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14 papers · 1 filter

cs.CL2025

QwenLong-CPRS: Towards -LLMs with Dynamic Context Optimization

Weizhou Shen, Chenliang Li, Fanqi Wan +12

This technical report presents QwenLong-CPRS, a context compression framework designed for explicit long-context optimization, addressing prohibitive computation overhead during th…

cs.CL2025

QwenLong-L1: Towards Long-Context Large Reasoning Models with Reinforcement Learning

Fanqi Wan, Weizhou Shen, Shengyi Liao +7

Recent large reasoning models (LRMs) have demonstrated strong reasoning capabilities through reinforcement learning (RL). These improvements have primarily been observed within the…

cs.CL20251 cited

MM-StoryAgent: Immersive Narrated Storybook Video Generation with a Multi-Agent Paradigm across Text, Image and Audio

Xuenan Xu, Jiahao Mei, Chenliang Li +5

The rapid advancement of large language models (LLMs) and artificial intelligence-generated content (AIGC) has accelerated AI-native applications, such as AI-based storybooks that…

cs.CL2024

ProFuser: Progressive Fusion of Large Language Models

Tianyuan Shi, Fanqi Wan, Canbin Huang +6

While fusing the capacities and advantages of various large language models offers a pathway to construct more powerful and versatile models, a fundamental challenge is to properly…

cs.CL2024

SocialBench: Sociality Evaluation of Role-Playing Conversational Agents

Hongzhan Chen, Hehong Chen, Ming Yan +8

Large language models (LLMs) have advanced the development of various AI conversational agents, including role-playing conversational agents that mimic diverse characters and human…

cs.CL2024

Knowledge Distillation of Black-Box Large Language Models

Hongzhan Chen, Ruijun Chen, Yuqi Yi +4

Given the exceptional performance of proprietary large language models (LLMs) like GPT-4, recent research has increasingly focused on boosting the capabilities of smaller models th…