adversarial training 1data poisoning 1defense frameworks 1retrieval-augmented generation 1zero-knowledge inference 1
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cs.LG2026
RAGuard: A Layered Defense Framework for Retrieval-Augmented Generation Systems Against Data Poisoning
Pushkal Kumar, Tucker Nielson, Tanish Kolhe +2
The paper introduces RAGuard, a two‑layer defense for retrieval‑augmented generation systems that combats factual corpus‑poisoning by adversarially fine‑tuning the retriever and ap…
cs.LG2026
Bradley-Terry Policy Optimization for Generative Preference Modeling
Shengyu Feng, Yun He, Shuang Ma +12
Reinforcement learning (RL) has recently proven effective at scaling chain-of-thought (CoT) reasoning in large language models for tasks with verifiable answers. However, extending…