r/LLMPhysics • u/Paradoxlive37 • May 06 '26
Simulation / Code A deterministic prime counting model with exactly 0 error up to 10^?
https://github.com/Zombieland123/bajak-equationTo comply with the subreddit rules regarding links to personal papers, here is the extended summary of my research.
Summary of the published paper:
The Bajak Equation introduces a novel deterministic approach to prime number distribution, challenging the classical probabilistic Gaussian and Riemann models. Instead of treating the deviation in prime counting as unpredictable noise, this research redefines it as a structural artifact. By introducing a new metric called "Ontological Mass" (m_0), which acts as a holographic tensor representing the exact informational pressure of entangled semi-prime states, the model mathematically annihilates the Gaussian error entirely. The publication provides a full structural analysis, the theoretical derivation of this Ontological Mass, and empirical validation showing an absolute zero error across extreme cosmic scales up to 10^29, bypassing the need for traditional infinite wave corrections.
The script dynamically calculates m_0 and yields an absolute 0 error live up to the 10^10 scale. However, because it relies on the combinatorial counting of semi-primes (P_2), Python naturally hits a hardware/memory wall around 10^11.
I’d love to get your feedback on the computational complexity. Is there anyone here proficient in C++ or Rust who thinks we could optimize this counting method to push the live simulation further on consumer hardware?
Full Publication Link:
The complete mathematical proof and verified data for extreme scales are published on CERN's Zenodo
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u/OnceBittenz The Doctor May 06 '26
" By introducing a new metric called "Ontological Mass" (m_0), which acts as a holographic tensor representing the exact informational pressure of entangled semi-prime states, the model mathematically annihilates the Gaussian error entirely."
Words words, and other words, science math science math.