Holosynthics periodic table methods confirmed all eight irreducible information types in 10 of 11 transformer models, achieving 100% atom classification on Llama 3.1 8B.
The study empirically validates the Holosynthics periodic table across various transformer models, demonstrating high accuracy in atom classification, hallucination detection, and surgical repair.
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.
Yonathan Shalev (Wed,) conducted a other in Large Language Models (n=11). Holosynthics periodic table methods was evaluated on Confirmation of irreducible information types, atom classification, and hallucination detection accuracy. Holosynthics periodic table methods confirmed all eight irreducible information types in 10 of 11 transformer models, achieving 100% atom classification on Llama 3.1 8B.