AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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An analysis of model internals revealed a low-dimensional space with three interpretable dimensions matching a formal pre-specified framework for the geometry of legal concepts, showing how legal thought in LLMs may be organized and directed.
Across 12 open-source and commercial LLMs on a novel 140,000-trial product choice benchmark, context induces human-like shifts in choice and problem categorization, but does not reliably reweight attention between features like price and quality.
This work introduces MAxBench, a geometry-agnostic evaluation framework for multinomial concept representations based on sampling from the recovered concept representation, and finds that affine subspaces steer more reliably and have greater recall than rank-one or linear subspaces.
The history and current state of interpretability taxonomized according to the types of causal units utilized, as well as methods used to search over mediators are described.
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