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This work proposes ARIA (autoencoder-gated inference-time unlearning), a test-time unlearning method that leaves model weights intact and gates access to unwanted knowledge only when generation enters a forget-related state and introduces three post-unlearning adversarial attacks targeting weight-space and decoding-space recovery.
SkillComposer achieves a remarkable +23.1% increase in task success rates for LLM agents by rethinking how skills are composed and executed together.
Even the most advanced VLMs like GPT-4o, GPT-5 and Gemini 2.5 Flash are outperformed in multi-actor human-robot interaction grounding by a system that selectively invokes VLMs based on a lightweight perception pipeline.
