Abstract Functional Language Logic: A Competitive Mixture of Experts Architecture for Paradox-Free Reasoning and Adaptive Intelligence 论文

详细信息

发表期刊/会议

Zenodo (CERN European Organization for Nuclear Research)

发表日期

2025-11-28

发表年份

2025

摘要

The prevailing paradigm of Large Language Models (LLMs) relies heavily on probabilistic next-token prediction. While this yields impressive linguistic fluency, it fundamentally struggles with strict logical deduction, often succumbing to semantic hallucinations, linguistic paradoxes, and high computational latency. This paper introduces a radical departure from probabilistic text-based reasoning: the Competitive Mixture of Experts (CMoE) framework based on Functional Language Logic (FLL). We propose offloading the "thinking" process from massive, opaque linguistic transformers to a highly efficient, dedicated cognitive engine built entirely upon mathematical functional approximators (linear, parabolic, and elliptical primitives). By treating logic as a mathematical mapping rather than an associative token distribution, the CMoE architecture establishes a highly interpretable, lightweight, and mathematically bounded reasoning system. Key Contributions and Architectural Features: Functional Approximators as Cognitive Agents: Replacing computationally redundant MLPs with parameterized mathematical functions that require minimal training and adapt in microseconds. The 8-Token Language of Thought (LoT): A deterministic, abstract reasoning space restricted to exactly 8 operational tokens. This closed logical system guarantees paradox-free internal reasoning by isolating computation from the ambiguities of human language. Decoupled Cognition and Articulation: The introduction of a "Host Interpreter Model" (LMM/LLM) that acts solely as the articulation layer, translating the CMoE's abstract logical vectors into human-readable text. Continuous Joint Training: A methodology to continuously co-evolve the CMoE alongside the Host Model to mitigate "interface hallucinations" and semantic dissonance. Recursive Query Fragmentation: A novel prompt-processing mechanism that parses complex queries via logical operators (e.g., therefore, if, and). This processes infinite logical depth within the call stack itself, bypassing the degradation typically seen in standard transformer context windows. The Shadowing Score ($S_{shadow}$): A competitive suppression metric that penalizes redundant functional agents. This prevents representation collapse, forces mathematical divergence, and enables dynamic on-the-fly fine-tuning during live dialogue. Flexible Intelligence and Bounded Creativity: An exploration of how the continuous interpolation of discrete logic generates structural mathematical "noise." When articulated by the Host Model, this noise manifests as emergent, highly creative, yet logically grounded reasoning that drastically enhances zero-shot generalization. This paper outlines a paradigm shift in neuro-symbolic AI, demonstrating how replacing massive neural layers with competitively suppressed mathematical primitives can yield an adaptive, creative, and highly efficient artificial intelligence capable of profound logical deduction. Keywords: Functional Language Logic, CMoE, Mixture of Experts, Large Language Models, Neuro-symbolic AI, Shadowing Score, Language of Thought, Knowledge Distillation, Logic.