AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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It is shown that intervention-based faithfulness can prefer an equally sized circuit that reproduces the model's behavior less well, and restoring selected intact-model signals reverses 96 of 100 selected persistent misrankings, demonstrating that context distortion contributes to these failures.
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.
OmniRouting is the first large-scale benchmark designed to evaluate LLMs on printed-circuit-board (PCB) routing reasoning under real-world industrial design-rule, manufacturability, and connectivity constraints, and reveals substantial limitations of current LMMs in PCB routing.
OmniLayout, the first benchmark designed to evaluate LLMs on printed-circuit-board (PCB) layout placement reasoning under real-world geometric, routing, and connectivity constraints, reveals substantial limitations of current LLMs in PCB layout placement, including weak geometric reasoning, poor routability optimizatio...
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