Logical Gene Encoding: a Bio-Inspired Approach for Energy-Efficient Automated Reasoning
Xujiang Tang, Qionglin Li · Atlantis highlights in social sciences, education and humanities/Atlantis Highlights in Social Sciences, Education and Humanities · 2025
This study pioneers a biomimetic paradigm for sustainable automated reasoning, confronting the escalating energy demands of contemporary AI systems.We present Logical Gene Encoding (LGE), a groundbreaking framework that emulates genetic evolutionary processes to achieve unprecedented efficiency in symbolic computation.Diverging from conventional neuro symbolic paradigms demanding exascale training data ( 10 9 samples), LGE attains 98.3% theorem-proving fidelity with merely 0.1% of the typical data requirement (10 3 samples), while slashing energy consumption by 682% compared to GPT-4 benchmarks.Three revolutionary components synergize in this architecture:Adaptive Logical Genomes: A differentiable encoding scheme translating symbolic rules into evolvable genetic representations (ℝ 𝟐𝟓𝟔 tensors), permitting backpropagation-driven optimization of deductive pathways.Geometric Knowledge Organelles: Riemannian manifolds with ℱ -adaptive metric tensors ( 𝑔 𝑖𝑗 =𝛿 𝑖𝑗 /(1 + ‖ ∇ ℱ‖ 2 ) ) That intrinsically penalize energy-intensive reasoning trajectories. Computational Darwinism: An autonomous mutation-selection engine implementing Lamarckian inheritance principles through quantum-annealed rule transformations.Empirical validation across 500 TPTP v7.5 problems reveals LGE's dominance in both accuracy (98.3% vs. Vampire's 85.7%) and sustainability (3.2 kJ/1k inferences vs. GPT-4's 2100 kJ).Real-world deployment in legal informatics successfully identified.3 latent contradictions in China's Civil Code with statistical significance (χ 2 = 5.12, p=0.023), demonstrating practical cross-domain applicability.