The Signal

Everything that matters in AI, with our take.

Updated through the day. Every headline links straight to the source. The two lines underneath are ours.

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arXiv cs.AIPaper13d ago

Privacy-preserving inference on hosted models: a learned input-embedding transform for frozen Qwen3.8-27B

Privacy-preserving inference on hosted models is real and 1.5 points of MMLU cost is tolerable for many use cases. The transform is small (55.6M) and runs client-side; the host never sees text. This matters if you're bound by privacy regulation or handling sensitive domains. The accuracy-privacy tradeoff is worth measuring for your own workload.

privacy infra security

arXiv cs.CLPaper20d ago

When Agents Talk, Secrets Travel: Topology-Driven Leakage and Recipient-Conditioned Sanitization in Multi-Agent LLM Systems

This identifies a real gap in conventional privacy models: you can authorize one agent to see sensitive data, but if that agent coordinates with others, the secret travels. RECONS fixes this by conditioning sanitization on recipient and topology rather than just data sensitivity. If you're deploying multi-agent systems with heterogeneous access controls, you probably aren't thinking about this yet. Test your topologies before you have a breach.

agents privacy security multi-agent