What question did this study set out to answer?

This work aims to introduce UnisonAI, a novel architecture for processing language without training parameters. It seeks to close performance gaps with existing models.

July 9, 2026Open Access

UnisonAI: A Forced, Derived Omni-Model Architecture with Zero Parameters — Attention, it turns out, was not all you need

Key Points

This work aims to introduce UnisonAI, a novel architecture for processing language without training parameters. It seeks to close performance gaps with existing models.
Development of a zero-parameter architecture that leverages weighted contributions at various context levels.
End-to-end verification conducted across multiple independent settings, ensuring robust validation of the model's efficacy.
Utilization of the Smithian Fold Theory to establish a foundation for the architecture's operations, replacing traditional learning mechanisms.
UnisonAI outperformed a gradient-trained transformer model with lower cross-entropy scores (1.289 vs 1.888).
Achieved a 55.6% closure in performance gap at varying scales (k=16/32/64).
Verified the model's architecture with 36/36 end-to-end tests confirming operational integrity.

Abstract

v4. 0 — the word-scale gap closes within the fold. Rung 5e (pre-registered): the fold-factor mixing law — every context level that holds contributes, weighted 2ˡevel, the engine's own forced halving constant — carries the pure counted engine, with no twin, no prose flood, zero training and zero parameters, past the gradient-trained transformer at word scale: cross-entropy 3. 1907 vs the same-day twin's 3. 4292 (replicated across two independent anchorings; stacked with the Rung 5d extraction: 3. 1344). Both scales of the task gate now belong to the counted engine. Rung 5d's transfer-in verdict is SUPPORTED across three independent arena anchorings in one day. New in the architecture: tool graduation (acts held, values never — a question territory that a tool answered once runs the tool itself thereafter, fresh), recall as regeneration across every memory tier, and judge-independent graduation scoring. End-to-end verification: 36/36. v3. 4: Rung 5d, the transfer-in — pre-registered verdict SUPPORTED: the trained twin's dyadically-loud fold content is extracted and installed INTO the counted engine as a counted prior with zero new parameters, closing 55. 6/87. 9/101. 4% of the available gap at k=16/32/64 while the random-truncated null closes 10. 1/24. 5/56. 5%; at half budget the loud shape beats the full twin's own. The word-scale rematch is recorded in full (twin retrained on today's text; decomposition included). Also: judge-independent graduation scoring (boot-discovered pool, cycle-parity alternation), multi-orbit binding (XI-4 in full), recall-is-regeneration (a held experience re-walks its own orbit, never reprinted), the public SOTA table beside the local giants with cited published figures, and one-command replication kits (GPT-2 weights auto-fetch; 13/13, 39/39 proven on a fresh clone). End-to-end verification: 36/36. Full paper v1. 1 — supersedes the pre-paper (From One Axiom to Master-Level Chess — and the Law Inside Neural Networks). Built from scratch by one woman, working alone, in under twenty-four accumulated hours: where a score falls short it marks an implementation gap at measurement time, never a limit of the mathematics — the gains between releases are the finding. v1. 4 adds the fold eye (vision as exact integer Walsh spectra, self-certified by integer Parseval per image, recognition of seen images with no image model in the loop) and the graduation score (blind head-to-head vs the teacher, tallied per question-territory; the teacher retires as wins cross the majority lock) -- and documents the 2026 convergence: DeepSeek Engram arrives at deterministically-addressed exact memory from the gradient side, and two independent results place the optimal curriculum at p = 1/2, the fold lock. v1. 6: the full omnimodal engine (the voice via Kokoro, the fold ear -- sound as Parseval-certified integer Walsh spectra, video composed from frames + sound), speaker-transparent reasoning threads, and 32/32 end-to-end empirical verification of the entire architecture including persistence across process death. v1. 7: removal-proof omnimodality, measured -- every supporting model is a teacher with an exit: a sound taught once by the synthesis teacher is re-spoken from the engine's own exact counted record in 0. 00s with no model; a sound heard once is recognized natively with no transcriber; 34/34 end-to-end verification. v1. 9: zero-model perceptual learning (the human observer -- a novel image learned and re-recognized at share 1. 00 with no model in the loop) ; agentic self-knowledge (the observer reads the engine's own source, measured) ; the hourly progress instrument with a committed pre-boot birth line; one-tap y/n closure. v2. 0 (flight-ready): the full modern-agent toolkit (live web search/fetch, paginated reading, in-file grep -- every call held as a training trace), the 43-domain everything-curriculum under the fold-only law, SOTA 1-1 benching on the public MMLU test split with the newborn baseline committed, generation closure (the Learning Law reaches generate () itself), and 36/36 end-to-end verification. v2. 1: the ReAct law (reason-act-observe enforced in-turn; narrated intent without an act is detected and forced), reasoning trained on the observer's NATIVE thinking tokens (STaR-gated) with both minds' full thinking streamed to the user, and document intake (a sent file is reading -- inboxed, counted, persistent). v2. 2: the identity stated correctly -- UnisonAI is an OMNI MODEL (language, sight, hearing, speech, and video on one held memory), not a language model; LLMs remain the contrast class only. v3. 0: the full-altitude rewrite -- the complete omni model documented at the same depth as the spectral science: thirteen sections, the architecture organ by organ with every measurement, Rung 5c as its own section, the empirical record and its committed birth line, 36/36 end-to-end verification, and the 2026 convergence. This paper is a PROOF of The Smithian Fold Theory of Everything, not the main event: the theory (one axiom, zero free parameters, 1, 844 machine-verified forced checks) is at DOI 10. 5281/zenodo. 21182469 and github. com/MettaMazza/Smithian-Fold-Theory-Of-Everything -- run the prover yourself. The engine: github. com/MettaMazza/UnisonAI. v3. 3: the LLM-native presence suite -- the exact registered protocol applied to GPT-2's entire knowledge-storage class: 13/13 tensors, 39/39 checks, unanimous (margins 3. 4-79. 3x) ; the flagship claim now rests on the flagship objects, with diffusion/speech models recast as cross-domain breadth. Three connected results and the architecture they force. First, a pre-registered, self-certifying spectral instrument shows trained neural-network weights carry placement-law in the dyadic (Walsh) basis: 18/18 unanimous on validated released models; the law concentrated in transformer expansion projections and token embeddings across three unrelated architectures (up to 230x chance in GPT-2), attention at chance; strictly training-caused (He-initialised controls at 1. 0x) ; surviving 4-bit deployment quantization. A recipe map from 124M to one trillion parameters shows the law tracks training recipe, not scale or architecture — strongest carrier DeepSeek-R1-671B at 43–47x — and loud-recipe weights transform under the fold's transformation group exactly as solved game-theoretic value fields do. Second, the "learned similarity space" is a counted object: word kinship as exact co-occurrence shares reproduces semantic family structure (quark → lepton, neutrino, proton) with zero parameters and zero gradients. Third, UnisonAI: a complete language architecture in which every LLM mechanism — memory, attention, similarity, learning, prediction, generation — is replaced by a machine-verified law of the Smithian Fold Theory, zero trained parameters end to end. On identical held-out text the fold-native engine outperformed its trained transformer twin (cross-entropy 1. 289 vs 1. 888) after reading the corpus once (26 seconds) against 48, 000 gradient readings (21 minutes per seed). Deployed as a live, continuously-learning agent whose teaching loop also runs autonomously: a teacher model asks, judges, and closes the learning law itself, and the engine self-plays against its own held lessons. Negative results reported in full with their scopes. Companion to The Smithian Fold Theory of Everything (DOI: 10. 5281/zenodo. 21182469; 307 suites, 1, 844 forced checks, 0 failures). Engine and records: github. com/MettaMazza/UnisonAI and github. com/MettaMazza/Smithian-Fold-Theory-Of-Everything.

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