SmolLM2 135M. Lenovo T14 CPU. No GPU. No RLHF. No BPE.
Coherent, non-sycophantic, contextually appropriate output. First message. No prior context window.
Same base model under standard pipeline: garbage.
What changed:
• BPE replaced with geometric hashing (φ-normalized, deterministic, no vocabulary table, no glitch tokens) • RLHF replaced with constraint injection directly into KV cache before generation • Context window memory replaced with external retrieval engine (986k queries/s, Rust) The paper proves why this works:
• GDA Collision Bound theorem: tokenization collisions occur only between anagrams. BPE collisions are semantically arbitrary. • Landauer-Assertion Binding theorem: constraint-consistent output is the system’s thermodynamic ground state. Violating constraints requires energy injection — it’s not just statistically unlikely, it’s physically expensive. • Geometric Leverage Impossibility: user input cannot modify KV cache constraint state. Jailbreaking requires hardware access, not prompt engineering. • Coherence Conservation: I\\\_eff = 1 − N\\\_compensation(σ) / N\\\_total. When σ → 0, the entire network does cognition instead of reconstruction. The \~13,000x parameter gap between this and frontier models is not intelligence. It is σ-compensation.
19 pages. Formal proofs. 5 falsifiable predictions. Full architecture spec. CC BY 4.0:
https://doi.org/10.5281/zenodo.19494797
Decisive test: A/B at fixed parameter count. Standard pipeline vs σ-reduced pipeline. The paper specifies exactly how to run it.
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