CATA enables persistent continual unlearning in VLMs by sign-aware aggregation of unlearning task vectors to suppress conflicts that could revive forgotten knowledge.
Confound from all sides, distill with resilience: Multi-objective adversarial paths to zero-shot robustness
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BiAxisAudit measures LLM bias on two axes—across-prompt sensitivity via factorial grids and within-response divergence via split coding—revealing that task format explains as much variance as model choice and that 63.6% of bias signals appear in only one layer.
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CATA: Continual Machine Unlearning via Conflict-Averse Task Arithmetic
CATA enables persistent continual unlearning in VLMs by sign-aware aggregation of unlearning task vectors to suppress conflicts that could revive forgotten knowledge.
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BiAxisAudit: A Novel Framework to Evaluate LLM Bias Across Prompt Sensitivity and Response-Layer Divergence
BiAxisAudit measures LLM bias on two axes—across-prompt sensitivity via factorial grids and within-response divergence via split coding—revealing that task format explains as much variance as model choice and that 63.6% of bias signals appear in only one layer.