What question did this study set out to answer?

This research aims to investigate the limitations of various architectures and practices in achieving improved performance accuracy (PA) in artificial intelligence.

April 27, 2026Open Access

SNN-Synthesis v15: Liquid Neural Cellular Automata for ARC-AGI—From Stochastic Resonance to The Grand Unified Intelligence Equation, Neuro-Symbolic Compilation, and the PA 59% Wall from 2.8K to 7B Parameters

Puntos clave

This research aims to investigate the limitations of various architectures and practices in achieving improved performance accuracy (PA) in artificial intelligence.
Conducted 241 experimental phases to derive key equations and validate models.
Developed a Neuro-Symbolic Compiler and analyzed performance metrics across phases 203–241.
Tested multiple approaches, including DSL Co-Training and hierarchical vision, to break performance ceiling.
Achieved a maximum performance accuracy of 61.5% with synthetic data mixing, though fundamental ceilings remain.
Confirmed the Grand Unified Intelligence Equation with performance metrics showing dominance of channel count.
Three approaches failed to break the PA 59% Wall, reinforcing constraints of data quality and model architecture.

Resumen

SNN-Synthesis v15 extends the investigation to 241 experimental phases, adding 39 new experiments (Phases 203–241) that derive the Grand Unified Intelligence Equation, complete a Neuro-Symbolic Compiler, and prove the PA 59% Wall—a fundamental performance ceiling that architecture, data quality, and self-supervised pre-training all fail to break. v15 Key Discoveries (Phases 203–241) (XLV) The Grand Unified Intelligence Equation: PA = 0.359 + 0.052 log C + 0.019 log N + 0.015 log D − 0.010 log P (R²=0.69). Channel count C dominates intelligence; parameter count P is a negative regularizer. Inverse algebra predicts C*=192, N*=1000 → PA=70.2%, but experiment yields only 59.7%—the "Extrapolation Cliff" proves log-linear scaling laws break at architectural boundaries (Phases 230–233). (XLVI) Neuro-Symbolic Compilation: A DSL compiler achieves 99.9% NCA-to-symbolic fidelity, proving the translation problem is solved. Object-Centric DSL (Move, Recolor, Mirror, Swap) raises Oracle PA from 75.2% to 80.1% and doubles Oracle EM. The bottleneck is unambiguously neural intuition quality, not symbolic logic (Phases 234–238). (XLVII) The PA 59% Wall: Three orthogonal approaches all fail—DSL Co-Training (PA=59.2%), U-NCA hierarchical vision (PA=59.3%), Grid-MAE self-supervised pre-training (PA=59.4%). Data, receptive field, and geometric common sense cannot break the ceiling (Phases 239–241). (XLVIII) Kaggle Leaderboard: v23 "Convergent Goose" achieves project-best 0.17; v26's increased complexity regresses to 0.03, confirming the Crossover Law in agent design. Self-Consistency Sampling: N=100 stochastic inferences + majority vote matches oracle selection quality (PA=60.5%, EM=4.0%) without ground truth (Phase 214). Co-Training Golden Ratio: 1:5 real:synthetic mixing achieves PA=61.5%, the highest in the synthetic data series (Phase 229). v1–v14 Foundations (Phases 1–202) All 73 previous findings remain validated, including Noisy Beam Search, SNN-ExIt, SR-Quantization, L-NCA, Liquid MoE, the θ–τ Isomorphism, Space ≡ Time, NCA Turing completeness, the 15 Laws of Digital Life, Thermodynamic Autopoiesis, Darwinian supremacy over Backpropagation, and the Gated Hybrid Dual-Process NCA (PA=60.3%, EM=4.0%). 85 contributions spanning 2.8K–7B parameters, NCA to Transformers, 9 task domains, 4 model families, and 30+ honest null results. Code and data: https://github.com/hafufu-stack/SNN-Synthesis Acknowledgments This research was conducted entirely independently, without institutional affiliation or corporate funding. The author currently faces financial constraints that make it increasingly difficult to maintain subscriptions to AI services essential for this line of research. To sustain and improve the quality of future work, the author is actively seeking community sponsorship. Details are available at https://github.com/sponsors/hafufu-stack.

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