Empirical Validation of the Holosynthics Periodic Table: Cross-Model Universality, Hallucination Detection, and Surgical Repair Across Transformer Architectures
Empirical Validation of the Holosynthics Periodic Table: Cross-Model Universality, Hallucination Detection, and Surgical Repair Across Transformer Architectures
Empirical validation of the Holosynthics periodic table across 11 transformer models from 9 organizations (117M to 9B parameters, 2019-2025). All eight irreducible information types confirmed in 10/11 models. 100% atom classification on Llama 3.1 8B (962 atoms). Dual-signal hallucination detection: 10/10 accuracy. Surgical repair: average x1.86 (GPT-2 Small) and x9.08 (Llama 3.1 8B). 3,000 sequential edits (60x ROME). Classification, detection, and repair methods protected by USPTO provisional patents 64/029,741, 64/048,143, 64/092,514, 64/092,517.
neural network interpretability, AI safety, sparse autoencoders, Holosynthics, hallucination detection, surgical repair
neural network interpretability, AI safety, sparse autoencoders, Holosynthics, hallucination detection, surgical repair
selected citations These citations are derived from selected sources.This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).0 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
Citations provided by BIP!Popularity provided by BIP!