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Anthropic published research on GRAM, a technique for surgically removing dangerous knowledge from AI models at the weight level, advancing AI safety.
This paper introduces process sidecars, a two-coefficient edit family for revoking learned state from language models after safety training, achieving second-order accuracy and outperforming naive task arithmetic in experiments across multiple models.
BrainSurgery is a tool for reproducible and declarative weight manipulations on neural network checkpoints, enabling model editing and upcycling through YAML plans with built-in validation.
NVIDIA introduces Gated DeltaNet-2, a method for editing compressed model memory without catastrophic forgetting, using independent gates for erase and write operations. It outperforms existing models like Mamba-2 and Mamba-3 on language modeling and long-context tasks.
Proposes M-ORE, a modality-decoupled online recursive editor for lifelong adaptation of multimodal large language models, addressing cross-modal conflict and inter-edit interference with constant per-edit overhead.
This paper introduces HoReN, a parameter-preserving model editing method that uses normalized Hopfield retrieval to handle large-scale sequential updates to large language models. It addresses issues of knowledge accumulation and routing challenges, demonstrating stable performance on 50K sequential edits where prior methods degrade.