arXiv:2607.00605v1 Announce Type: new Abstract: Limited Memory Language Models (LMLMs) externalize factual knowledge to a database to enable deletion-based unlearning without retraining. Existing evaluations measure post-deletion correctness in aggregate and cannot tell whether a deleted fact persists through residual parametric memory, alternative retrieval paths, or near-neighbor retrieval artifacts. We propose a causal auditing framework that holds the model fixed and varies the database state at inference time across three interventions: FULL, DEL-ON, and DEL-OFF. The framework decomposes
The increasing deployment of AI agents and the need for robust data governance, particularly the 'right to be forgotten,' makes auditing forgetting an urgent technical challenge.
This research provides a foundational framework for ensuring that AI models can provably 'unlearn' sensitive or incorrect information, which is critical for privacy, compliance, and trustworthiness.
The ability to causally audit memory deletion in LMLMs introduces a new standard for evaluating AI unlearning, allowing for more precise control and verification of data removal.
- · AI developers focused on privacy and responsible AI
- · Cloud providers offering secure AI services
- · Sectors with strict data compliance requirements
- · AI models lacking robust unlearning mechanisms
- · Entities reliant on persistent, undeletable data in models
Improved trust and adoption of AI systems that handle sensitive personal or proprietary information.
New regulatory mandates for explainable and verifiable unlearning capabilities in deployed AI models.
The emergence of 'data sovereignty' for AI models, where the control and deletion of embedded information become paramount.
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