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The proposed Stochastic Meta-Unlearning (SMU), a bilevel framework that uses VLM-level feedback to learn an unlearning-ready initialization, suggests that VLM-level feedback can make language-backbone unlearning more reliable and more transferable for VLMs.
The results show that reliable tabular LLM unlearning depends not only on the forgetting objective, but also on how retain constraints are constructed, which shows that reliable tabular LLM unlearning depends not only on the forgetting objective, but also on how retain constraints are constructed.
DrugReason is proposed, a multi-view reasoning framework that integrates grounded KG reasoning with LLM-generated mechanistic inference for drug repurposing and achieves competitive or superior results compared with graph-based alternatives, while providing interpretable routing-based predictions.
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