SHIELDS deploys multi-agent LLMs for iterative, feedback-driven OS hardening and reports up to 73% remediation of scan findings, with success tied more to tool use than model size.
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Using properties of positional embeddings, reasoning LLMs can be made to think, listen, and generate outputs asynchronously without any additional training, cutting time to first token to under 5 seconds.
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SHIELDS: Automating OS Hardening with Iterative Multi-Agent Remediation
SHIELDS deploys multi-agent LLMs for iterative, feedback-driven OS hardening and reports up to 73% remediation of scan findings, with success tied more to tool use than model size.
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Asynchronous Reasoning: Training-Free Interactive Thinking LLMs
Using properties of positional embeddings, reasoning LLMs can be made to think, listen, and generate outputs asynchronously without any additional training, cutting time to first token to under 5 seconds.