Nvidia Software Pushes MLPerf Inference Benchmarks To New Highs - The Next Platform
Nvidia Software Pushes MLPerf Inference Benchmarks To New Highs The Next Platform
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Detecting collusion through multi-agent interpretability
TL;DR Prior work has shown that linear probes are effective at detecting deception in singular LLM agents. Our work extends this use to multi-agent settings, where we aggregate the activations of groups of interacting agents in order to detect collusion. We propose five probing techniques, underpinned by the distributed anomaly detection taxonomy, and train and evaluate them on NARCBench - a novel open-source three tier collusion benchmark Paper | Code Introducing the problem LLM agents are being increasingly deployed in multi-agent settings (e.g., software engineering through agentic coding or financial analysis of a stock) and with this poses a significant safety risk through potential covert coordination. Agents has been shown to try to steer outcomes/suppress information for their own
The Real Reason ASME Vessels Are Critical in Modern Industry
If you have ever walked through a refinery, a pharmaceutical plant, or even a food processing facility, you’ve probably stood next to an ASME vessel without knowing it. They’re not glamorous. No one takes selfies with them. But these pressure vessels quietly do the heavy lifting in industries that keep our world running. I’ve worked around pressure systems long enough to say this with confidence: when it comes to safety and reliability, shortcuts are expensive. Sometimes fatally expensive. That’s exactly why ASME vessels exist. For professionals in manufacturing, oil and gas, power generation, or process industries, understanding ASME vessels is not just technical knowledge. It’s operational survival. **What Exactly Is an ASME Vessel? **An ASME vessel is a pressure vessel designed and fabr
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Detecting collusion through multi-agent interpretability
TL;DR Prior work has shown that linear probes are effective at detecting deception in singular LLM agents. Our work extends this use to multi-agent settings, where we aggregate the activations of groups of interacting agents in order to detect collusion. We propose five probing techniques, underpinned by the distributed anomaly detection taxonomy, and train and evaluate them on NARCBench - a novel open-source three tier collusion benchmark Paper | Code Introducing the problem LLM agents are being increasingly deployed in multi-agent settings (e.g., software engineering through agentic coding or financial analysis of a stock) and with this poses a significant safety risk through potential covert coordination. Agents has been shown to try to steer outcomes/suppress information for their own
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