arXiv Machine Learning
TrainSDC: Characterizing and Mitigating Silent Data Corruption in Large Language Model Training
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From Lab to Reality: A Practical Evaluation of Deep Learning Models and LLMs for Vulnerability Detection
arXiv:2512. 10485v2 Announce Type: replace-cross Abstract: Vulnerability detection methods based on deep learning (DL) have shown strong performance on benchmark datasets, yet their real-world effectiveness remains underexplored.
arXiv Machine Learning
Jun 26
At the Edge of Understanding: Sparse Autoencoders Trace The Limits of Transformer Generalization
arXiv:2606. 26396v1 Announce Type: new Abstract: Pre-trained transformers have demonstrated remarkable generalization abilities, at times extending beyond the scope of their training data.
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CyberForge: Verified Vulnerability Injection at Repository Level for Cybersecurity Agent Training
arXiv:2608. 06471v1 Announce Type: cross Abstract: Despite recent advances, frontier large language model (LLM) agents remain limited in discovering and patching complex vulnerabilities in real-world software.
arXiv Machine Learning
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Do LLMs Really Forget? Hidden-State Leakage in Model Unlearning and How to Fix it
arXiv:2609.36612v1 Announce Type: new Abstract: Unlearning in large language models (LLMs) is typically evaluated at the output level, where a model appears to suppress sensitive or undesirable conte...
arXiv AI
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Bait-and-Recover: Poisoning Internal Refusal Signals to Defend LLMs against White-Box Editing Jailbreaks
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arXiv AI
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Gradient Immunity: Null-Space Resistance to Malicious Fine-Tuning
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