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A collection of Claude Code skills for academic research workflows, including literature reviews, PhD proposals, and fidelity-first academic slides with vector equations and citations.
Proposes treating semantic compression as a diffusion noise function for handling massive context beyond model windows, using multi-pass reading at decreasing compression levels. Untrained-model experiments show components work in isolation but the full chain needs training to resolve binding bottleneck.
The author proposes a novel experimental framework to study identity formation in LLMs as hypergraph evolution through multi-instance interaction, distinguishing it from standard multi-agent debate by focusing on structural divergence in activation space rather than task performance.