AttnCompress bridges the gap between semantic integrity and dynamic adaptability through three key mechanisms: structure-aware segmentation via perplexity (PPL) spikes to preserve the syntactic structure of code and logs, and relevance estimation using proxy attention weights to quantify the precise relevance of histor...
Z. Zeng, Yi-Xin Li, Rui Xie et al.· Proceedings of the ACM on So...· 1 citation
This work proposes SHIFT, a retrieval training framework based on LLMs that transfers LLMs into reasoning-efficient retrievers with residual projection and task-oriented bidirectional attention aggregation in the latent space, and alleviates the mismatch between contrastive learning and implicit reasoning using fine-gr...
Yuxiao Luo, Da Li, Mingjie Zhang et al.· arXiv.org· 0 citations
Experiments reveal substantial agent vulnerabilities and show that injection timing and placement affect attack effectiveness, and ToolHazard-generated alignment data improves security on both ToolHazard-Bench and AgentDojo while preserving benign task utility.
Yutao Mou, Pengfei Yang, Zhenfei Yin et al.· 2 citations
A novel Le arnable Lo w-R ank A daptation (LeLoRA) framework that utilizes dynamically learned fine-tuning strategies to facilitate the effective adaptation of LLMs and provides compelling evidence that LeLoRA consistently outperforms existing baselines in adapting LLMs.
Xiaoling Zhou, Mingjie Zhang, Zhemg Lee et al.· Annual Meeting of the Associ...· 0 citations
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