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BioGlyph is a method that translates network topology into interpretable language roles to enhance large language models' ability to reason about complex networks, demonstrating significant performance improvements across various domains.
This paper presents a controlled testbed for analyzing goal-directed persuasion in networks of LLM agents, finding that persuasion dynamics depend on network topology, competition, topic, and model priors.
Amazon discusses the evolution of flat datacenter network topologies, from theoretical expander graphs to practical implementations like VL2 and Jellyfish, and current research into Penrose tiling-based designs at AWS.
This paper performs full Jacobian eigendecomposition across production-scale LLMs, revealing a learned spectral gradient from rotation-dominated early layers to symmetric late layers, along with a low-rank bottleneck that compresses perturbations. The results link perturbation propagation and compression to network functional topology.