Malaika AI framework uses tri-grounded reasoning for malware analysis
Malaika, a multi-agent AI framework posted to arXiv this month, uses three grounding mechanisms to make malware analysis more precise and auditable.
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Malaika, a multi-agent AI framework posted to arXiv this month, uses three grounding mechanisms to make malware analysis more precise and auditable.
TrustX ARC, a new arXiv preprint, scores agentic AI risk across 12 dimensions into three governance tiers for risk officers, developers and regulators.
A new arXiv preprint pairs an LLM planner with a forecasting model to guard industrial control systems, recording zero hallucinated actions in attack
KV-PRM reads the memory AI models already produce during generation, slashing the cost of verifying multi-agent reasoning chains by up to 5,000x.
A new arXiv paper proposes the Hypothesis Evolution Protocol, making AI agents' scientific reasoning explicit and auditable instead of buried in logs.
A new arXiv survey maps how LLMs could move front-end chip design from isolated tasks to autonomous agents, but offers no benchmarks to prove it works.
LLM-as-a-Verifier treats checking AI answers as a scaling axis, hitting 86.5% on Terminal-Bench V2 and 78.2% on SWE-Bench Verified without extra training.