TimeROME-DLM: Temporal Causal Tracing and Low-Rank Inference-Time Knowledge Editing for Masked Diffusion Language Models
arXiv:2606.12841v1 Announce Type: cross Abstract: Masked diffusion language models (MDLMs) such as LLaDA now rival autoregressive (AR) LLMs, but every existing knowledge-editing and unlearning method (ROME, MEMIT, etc.) targets AR transformers and either makes assumptions that fail under iterative denoising, or requires gradient updates whose backward-pass activations cost tens of GB of extra VRAM and which collapse MDLMs at standard learning rates. We introduce TimeROME-DLM, the first training-free, gradient-free, inference-time knowledge-editing framework for MDLMs. It couples two components
The rapid advancement of Masked Diffusion Language Models (MDLMs) like LLaDA necessitates new methods for knowledge editing as they begin to rival autoregressive LLMs, and existing techniques are incompatible or inefficient.
This development addresses a critical limitation in controlling and updating advanced AI models, impacting trustworthiness, safety, and the ability to integrate real-time information without costly retraining.
The introduction of TimeROME-DLM provides a training-free and gradient-free method for editing knowledge in MDLMs, making real-time model updates significantly more feasible and less resource-intensive.
- · Developers working with MDLMs
- · Organizations requiring dynamic AI knowledge updates
- · AI safety and alignment research
- · Providers of VRAM-intensive knowledge editing solutions
- · Methods requiring full model retraining for knowledge updates
MDLMs become more adaptable and easier to manage with evolving information.
This could accelerate the adoption and deployment of MDLMs in real-world applications where knowledge fidelity is paramount.
Improved knowledge editing could reduce the barriers to entry for deploying complex AI, potentially leading to more fragmented and specialized AI applications.
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Read at arXiv cs.AI