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PrivMeSA is introduced, a privacy-aware self-evolving multi-agent system that learns to control disclosure and retains remote expertise for local reuse, and improves mean task accuracy over delegation by up to 15.8 percentage points.
Unlearning ensures LLM compliance by removing the influence of private or copyrighted training data. However, since LLM models typically undergo post-training compression, like quantization, in practical deployment, it has been observed that the unlearning effect can be substantially weakened, with the forgetting behav...
Multi-agent LLM systems split work across models, so answering often requires knowledge that sits in another agent's context: a Sharer has encoded information that a Receiver needs to complete its task. They usually communicate by exchanging text, which puts autoregressive decoding on the critical path and reduces the...
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