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cs.LG2026
Self-Reflection Fine-Tuning: Enhancing Agent Security against Prompt Injection Attacks from Failure Experience
Zixuan Wang, Hao Li, Fengyu Gao +5
Large language model (LLM) agents are increasingly deployed in tool-augmented environments, but their reliance on external inputs makes them highly vulnerable to prompt injection a…
cs.LG2025
Towards Reliable Evaluation of Adversarial Robustness for Spiking Neural Networks
Jihang Wang, Dongcheng Zhao, Ruolin Chen +2
Spiking Neural Networks (SNNs) utilize spike-based activations to mimic the brain's energy-efficient information processing. However, the binary and discontinuous nature of spike a…
cs.LG2025
Reinforcement Fine-Tuning of Flow-Matching Policies for Vision-Language-Action Models
Mingyang Lyu, Yinqian Sun, Erliang Lin +4
Vision-Language-Action (VLA) models such as OpenVLA, Octo, and have shown strong generalization by leveraging large-scale demonstrations, yet their performance is still funda…