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20232026
most citedThe Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG)

6 citations · 15 across the 27 of their papers we have counts for

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10 papers · 1 filter

cs.LG2026

A Simple Plug-in for Improving Eviction-Based KV Cache Compression

Yuping Lin, Jiayuan Ding, Yue Xing +3

KV cache growth is a major bottleneck for long-context inference in large language models. Existing methods are often dominated by binary eviction or representation approximation,…

cs.LG2026

Crafting Reversible SFT Behaviors in Large Language Models

Yuping Lin, Pengfei He, Yue Xing +5

Supervised fine-tuning (SFT) induces new behaviors in large language models, yet imposes no structural constraint on how these behaviors are distributed within the model. Existing…

cs.LG2026

Co-RedTeam: Orchestrated Security Discovery and Exploitation with LLM Agents

Pengfei He, Ash Fox, Lesly Miculicich +7

Large language models (LLMs) have shown promise in assisting cybersecurity tasks, yet existing approaches struggle with automatic vulnerability discovery and exploitation due to li…

cs.LG2026

Embedding Perturbation may Better Reflect Intermediate-Step Uncertainty in LLM Reasoning

Qihao Wen, Jiahao Wang, Yang Nan +3

Large language Models (LLMs) have achieved significant breakthroughs across diverse domains; however, they can still produce unreliable or misleading outputs. For responsible LLM a…

cs.LG2026

Interpretable Probability Estimation with LLMs via Shapley Reconstruction

Yang Nan, Qihao Wen, Jiahao Wang +4

Large Language Models (LLMs) demonstrate potential to estimate the probability of uncertain events, by leveraging their extensive knowledge and reasoning capabilities. This ability…

cs.LG2025

PEAR: Planner-Executor Agent Robustness Benchmark

Shen Dong, Mingxuan Zhang, Pengfei He +4

Large Language Model (LLM)-based Multi-Agent Systems (MAS) have emerged as a powerful paradigm for tackling complex, multi-step tasks across diverse domains. However, despite their…