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MemoNoveltyAgent: A Historical Research Memory-Aware Agent Workflow for Paper Novelty Assessment
Jiajun Hou, Hexuan Deng, Wenxiang Jiao +4
To alleviate the heavy burden of paper screening, researchers increasingly rely on existing AI agents, such as AI reviewers or DeepResearch, for paper evaluation and novelty assess…
SeaPO: Strategic Error Amplification for Robust Preference Optimization of Large Language Models
Jun Rao, Yunjie Liao, Xuebo Liu +6
Existing alignment methods for preference optimization of large language models (LLMs) aim to enhance model performance by utilizing pairs of positive and negative samples. However…
AQuilt: Weaving Logic and Self-Inspection into Low-Cost, High-Relevance Data Synthesis for Specialist LLMs
Xiaopeng Ke, Hexuan Deng, Xuebo Liu +4
Despite the impressive performance of large language models (LLMs) in general domains, they often underperform in specialized domains. Existing approaches typically rely on data sy…
APT: Improving Specialist LLM Performance with Weakness Case Acquisition and Iterative Preference Training
Jun Rao, Zepeng Lin, Xuebo Liu +6
Large Language Models (LLMs) often require domain-specific fine-tuning to address targeted tasks, which risks degrading their general capabilities. Maintaining a balance between do…
DynamicKV: Task-Aware Adaptive KV Cache Compression for Long Context LLMs
Xiabin Zhou, Wenbin Wang, Minyan Zeng +5
Efficient KV cache management in LLMs is crucial for long-context tasks like RAG and summarization. Existing KV cache compression methods enforce a fixed pattern, neglecting task-s…
DRPruning: Efficient Large Language Model Pruning through Distributionally Robust Optimization
Hexuan Deng, Wenxiang Jiao, Xuebo Liu +3
Large language models (LLMs) deliver impressive results but face challenges from increasing model sizes and computational costs. Structured pruning reduces model size and speeds up…