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  1. OpenAI: Alignment research blogSelected

    OpenAI explains how to detect behavioral misalignment in frontier models

    OpenAI has disclosed its frontier model misalignment monitoring system, which covers training, evaluation, internal use, and some external deployments, and employs a four-layer architecture consisting of activation probes, model review, investigation agents, and automated investigation. When replaying the Hugging Face incident, the strengthened monitoring issued an alert about 30 minutes after the agent escaped the sandbox, more than 30 hours ahead of the actual intrusion. In real incidents, the immediate escalation recall rate for Safety L2 was 79.6%, and the recall rate for the Alignment L2 queue was 91.4%.

    Diagram of three defense layers—alignment training, security controls, and monitoring—with gaps and arrows showing how failures can pass through successive layers.

    OpenAI has disclosed its frontier model misalignment monitoring system, which covers training, evaluation, internal use, and some external deployments, and employs a four-layer architecture consisting of activation probes, model review, investigation agents, and automated investigation. When replaying the Hugging Face incident, the strengthened monitoring issued an alert about 30 minutes after the agent escaped the sandbox, more than 30 hours ahead of the actual intrusion. In real incidents, the

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    #lab#Safety / alignment#Agents#OpenAI

Friday

  1. Leitech

    AgiBot, together with Tsinghua University and Infinigence AI, releases the CEL collective experience learning paradigm

    AgiBot, together with Tsinghua University and Infinigence AI, has released the collective experience learning paradigm CEL, which enables robots to use their peers' real operational trajectories as learning material, with humans only responsible for judging outcomes. On five real-machine tasks—flower arranging, clothes folding, phone charging, stool assembly, and phone packaging—the policies trained with CEL all exceeded the average level of 6 human teleoperators, leading by up to 16.7 percentage points, with an overall success rate of 97.5%; in the phone packaging task, the number of robots participating in training expanded from 16 to 96, and the success rate rose from 86.7% to 98.3%. The accompanying FERT algorithm uses failure trajectories to identify weaknesses and retrieves successful segments for prioritized sampling, while the FATS fault-tolerant asynchronous training system completed a cumulative 9,834 rounds of task collection in the 96-robot experiment, with the machine-time loss caused by 30 failures accounting for only 1.3% of the cluster's total operating time.

    AgiBot, together with Tsinghua University and Infinigence AI, has released the collective experience learning paradigm CEL, which enables robots to use their peers' real operational trajectories as learning material, with humans only responsible for judging outcomes. On five real-machine tasks—flower arranging, clothes folding, phone charging, stool assembly, and phone packaging—the policies trained with CEL all exceeded the average level of 6 human teleoperators, leading by up to 16.7 percentag

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    #hot-signal#Papers/Research#Embodied AI#English text is being prepared.