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From the 1 of 7 linked papers with an AI index.

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7 papers

cs.LG2026

ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation

Qingyu Zhang, Qianhao Yuan, Hongyu Lin +7

The paper proposes ShortOPD, a short-to-long on-policy distillation method that recovers the generation quality of structured-pruned large language models by focusing training on e…

cs.CV2026

Vision-OPD: Learning to See Fine Details for Multimodal LLMs via On-Policy Self-Distillation

Qianhao Yuan, Jie Lou, Xing Yu +4

Multimodal Large Language Models (MLLMs) still struggle with fine-grained visual understanding, where answers often depend on small but decisive evidence in the full image. We obse…

cs.CL2026

MemSearcher: Training LLMs to Reason, Search and Manage Memory via End-to-End Reinforcement Learning

Qianhao Yuan, Jie Lou, Zichao Li +6

LLM-based search agents often concatenate the full interaction history into the context, producing long and noisy inputs, and increasing compute cost and GPU memory overhead. To ad…

cs.AI2026

DeepPresenter: Environment-Grounded Reflection for Agentic Presentation Generation

Hao Zheng, Guozhao Mo, Xinru Yan +7

Presentation generation requires deep content research, coherent visual design, and iterative refinement based on observation. However, existing presentation agents often rely on p…

cs.CV2025

ShortV: Efficient Multimodal Large Language Models by Freezing Visual Tokens in Ineffective Layers

Qianhao Yuan, Qingyu Zhang, Yanjiang Liu +6

Multimodal Large Language Models (MLLMs) suffer from high computational costs due to their massive size and the large number of visual tokens. In this paper, we investigate layer-w…

cs.CL2025

ConsistentChat: Building Skeleton-Guided Consistent Multi-Turn Dialogues for Large Language Models from Scratch

Jiawei Chen, Xinyan Guan, Qianhao Yuan +7

Current instruction data synthesis methods primarily focus on single-turn instructions and often neglect cross-turn coherence, resulting in context drift and reduced task completio…