Project Aletheia: Physics-Inspired Eradication of LLM Hallucinations via Output-Layer Phase Transitions

Abstract This paper presents Project Aletheia, a systematic investigation of Large Language Model (LLM) hallucination through the lens of condensed matter physics and quantum mechanics. Through 23 experimental phases conducted on GPT-2 (124M parameters), I establish five fundamental laws governing hallucination in autoregressive language models: Degeneracy Law: Factual and grammatical subspaces are separated by only 1. 2°, making fluent hallucination a structural inevitability. Temperature Irrelevance: The critical spike magnitude is temperature-independent (γ = 0. 000), proving that sampling temperature adjustments cannot eliminate hallucination. LayerNorm Impermeability: All mid-layer interventions are absorbed by LayerNorm. The output layer is the only viable intervention point. Truth Scaling Law: spikec ~ N^ (−0. 491). Larger models require exponentially smaller interventions—a 175B-parameter model needs only logitbias ≈ 0. 26. Temporal Persistence: A single t=0 spike propagates with a half-life of 130. 9 tokens, enabling natural factual generation without continuous intervention. These findings demonstrate that hallucination eradication is achievable not through expensive retraining (RLHF) or complex decoding strategies, but through a minimal logit-space phase transition at the moment of generation initiation—a mechanism mathematically equivalent to the logitbias parameter available in commercial LLM APIs (isomorphism proven with diff = 0. 00). Key Results Phase 5: First-order phase transition at spike=10 (0% → 100% accuracy) Phase 7: Zero-order transition (γ = 0. 000) —temperature is irrelevant Phase 13: Universal across 50 diverse QA pairs (mean critical spike = 3. 9 ± 3. 1) Phase 14: 94 fact heads and 47 skill heads identified via attention head ablation Phase 19: Truth Scaling Law: spikec = 85, 846 × N^ (−0. 491) Phase 21: Spike mechanism is robust against adversarial prompts (100% at spike=7) Phase 22: Dual-use warning—same mechanism can create targeted hallucinations (25% success) 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.