HyperTensor: The Extended Volume (Preprint)
HyperTensor: The Extended Volume (Preprint)
HyperTensor is a geometric framework for understanding, compressing, and extending transformer language models. This deposit is the 211-pageVolume Extended manuscript (18 papers) covering three strata: Papers I–X (empirical kernel) which cover Geometric Runtime Compression, OTT manifold runtime, cross-GPU transfer, speculative geodesic decoding, CECI model grafting. Papers XI–XV (living-model stack) which cover Universal Geodesic Taxonomy, native geodesic training, Safe OGD, behavioural sniping, COG+TEH. Papers XVI–XVIII (Riemann framework) which cover AGT, ACM, and the Bridge Protocol. These papers are presented as geometric visualizations of the functional equation's Z₂ symmetry, not as contributions to analytic number theory; see the explicit disclaimers in each paper's abstract. Repository and Reproducibility Source code, reproduction scripts, benchmark outputs, and the LaTeX sources of all 18 papers are available at github.com/NagusameCS/HyperTensor. Code is released under the MIT License; this deposit (manuscript and figures) is released under CC BY 4.0. Per-paper reproduction recipes, hardware tiers (T1 CPU / T2 consumer GPU / T3 datacenter GPU), dependency tiers, determinism notes, and troubleshooting are documented in REPRODUCTION.md.Instructions for reproducing are also available on the GH Pages at HyperTensor Research, NagusameCS. I have also created an alternative repository at Nagusamenotame/civilized-HyperTensor I realize that digesting the original repository is incredibly difficult due to its enormous volume and complexity, this is a more streamlined version without all the bulk, designed to make reproduction and implimentation far easier. There may be issues with this repo since it hasnt been tested to make sure nothing was damaged when the bulk was cut so use with care. I am an 18-year-old independent researcher, not a professional, and I've been working on this for 6 months, so there are bound to be several mistakes. Corrections and questions (or advice) welcome at NagusameCS@gmail.com.
geodesic projection, Computer Science - Machine Learning, native geodesic training, cs.LG, lifelong learning, Machine Learning, manifold learning, cross-gpu transfer, functional equation, throughput benchmarking, behavioural alignment, cs.PF, geometric jury, Computer Science - Computation and Language, manifold, geodesic speculative decoding, cs.CL, model grafting, stat.ML, JURY, math.DG, organic generation, low-rank attention, kv cache, pca, Mathematics - Differential Geometry, perplexity, AI Saftey, analytic continuation manifold, z2 symetry, gpu l2 cache, reiman hypothesis, speculative decoding, critical line, language models, svd, llm inference, orthagonal projection, Statistics - Methodology, rieman geometry, Computer Science - Preformance, Mathematics - Number Theory, Universal Geodesic Taxonomy, ensemble aggregation, Reiman, ML, model merging, zeta function, math.NT, transformer compression, Computer Science, UGT
geodesic projection, Computer Science - Machine Learning, native geodesic training, cs.LG, lifelong learning, Machine Learning, manifold learning, cross-gpu transfer, functional equation, throughput benchmarking, behavioural alignment, cs.PF, geometric jury, Computer Science - Computation and Language, manifold, geodesic speculative decoding, cs.CL, model grafting, stat.ML, JURY, math.DG, organic generation, low-rank attention, kv cache, pca, Mathematics - Differential Geometry, perplexity, AI Saftey, analytic continuation manifold, z2 symetry, gpu l2 cache, reiman hypothesis, speculative decoding, critical line, language models, svd, llm inference, orthagonal projection, Statistics - Methodology, rieman geometry, Computer Science - Preformance, Mathematics - Number Theory, Universal Geodesic Taxonomy, ensemble aggregation, Reiman, ML, model merging, zeta function, math.NT, transformer compression, Computer Science, UGT
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