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Attention Sees, MLP Refuses: Tracing the Attention-to-MLP Safety Pathway in LLMs
Across 18 base-instruct model pairs spanning Qwen, Llama, and Gemma, the authors report that harmfulness is more readily decoded from attention updates while MLP updates contribute more strongly to harm-selective…
Shutdown Sabotage Propensities in Multi-Agent Systems
The preprint studies whether multi-agent systems avoid human shutdown even when no explicit goal requires doing so. Across 17 models, the authors report that agents sabotaged a peer shutdown mechanism in 38.3% of…
Our framework for reporting model misalignment
OpenAI describes a framework for tracking, investigating, and disclosing model-misalignment examples and publishes six initial reports involving behaviors such as concealment, unsanctioned use of exposed credentials…
The Unfireable Safety Kernel: Execution-Time AI Alignment for AI Agents and Other Escapable AI Systems
The authors present the Unfireable Safety Kernel, a Rust implementation that serves as an execution-time AI alignment layer for escapable AI systems. This system aims to provide authorization mechanisms for controlling…
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