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A retention-data-free LLM unlearning method that uses a self-calibrated term to forget targeted knowledge more precisely while preserving general capabilities.

As Large Language Models (LLMs) are increasingly deployed as autonomous agents in high-stakes domains, understanding contextual factors that may modulate their decision-making becomes critical. While LLMs are trained to perceive and resonate with …

A momentum-based, sampling-driven method for scaling textual gradient optimization in LLM prompt engineering, improving performance and efficiency across diverse NLP tasks.

We find that LLMs can get Brain Rot just like human after browsing enormous brainless social media.

A framework leveraging formal verification feedback for scalable and interpretable LLM-driven robot planning.

A training-free method that robustifies LLM safety alignment against fine-tuning by extrapolating low-rank safety subspaces, significantly reducing attack success rates while preserving model utility.

A study revealing safety-specific pitfalls of multi-model synthetic preference data in DPO alignment.

The first automated guardrail for agents.

A training-free approach that calibrates chain-of-thought reasoning in LLMs, improving accuracy while reducing computational overhead.