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Mechanistic interpretability aims to recover the internal computations responsible for model behavior. Progress in automated circuit discovery is often framed as a search problem: better attribution or optimization should identify better mechanisms. This assumes that the evaluation objective can recognize a better circ...
Neuro-Symbolic Graph Generative Modeling (NSGGM), a framework built on the principle of Neural Proposals, Symbolic Guarantees, is introduced, and is competitive with state-of-the-art methods on unconstrained tasks.
Findings show circuits can preserve task success without adequately explaining model's failures, and support exact error reproduction as a necessary, but not sufficient, test of circuit-based explanations of model behaviour.
A fully on-premise clinical agent that couples local operational control with a multi-perspective reliability framework to support selective autonomy is developed and evaluated, supporting a practical framework for institutionally governed clinical agents.
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