Logical Gene Encoding: a Bio-Inspired Approach for Energy-Efficient Automated Reasoning

DOI

10.2991/978-94-6463-803-5_81How to use a DOI?

Keywords

Logical Gene Encoding; Automated Reasoning; Rule Discovery; The Transformative Change of Educational Automation

Abstract

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 (10⁹ samples), LGE attains 98.3% theorem-proving fidelity with merely 0.1% of the typical data requirement (10³ 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 i j = 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 (χ² = 5.12, p = 0.023), demonstrating practical cross- domain applicability.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Xujiang Tang
AU  - Qionglin Li
PY  - 2025
DA  - 2025/07/31
TI  - Logical Gene Encoding: a Bio-Inspired Approach for Energy-Efficient Automated Reasoning
BT  - Proceedings of the 5th International Conference on Internet, Education and Information Technology (IEIT 2025)
PB  - Atlantis Press
SP  - 830
EP  - 844
SN  - 2667-128X
UR  - https://doi.org/10.2991/978-94-6463-803-5_81
DO  - 10.2991/978-94-6463-803-5_81
ID  - Tang2025
ER  -