Project Aletheia V7: The Inference-Time Paradigm — Dual Surgery, the Sword Equation, and Scale-Free Hallucination Control

This paper presents Project Aletheia, a systematic 165-phase investigation of LLM hallucination through the lens of condensed matter physics. Using GPT-2 (124M parameters) as a "particle accelerator" and scaling to Qwen2.5-14B, I establish eight fundamental laws, eight theorems, and six principles governing how transformers suppress factual knowledge—and how to deterministically restore it through inference-time geometric intervention. V7 New Discoveries (Phases 139–165, Seasons 31–34): The DPO Elimination: Embedding Surgery + Shield&Sword achieves 100% numerical accuracy without any training on Qwen-1.5B (P144), proving DPO is entirely unnecessary. Dual Surgery: The 14B model's resistance is caused by output-layer (lm_head) clustering (cos = 0.74). Dispersing both input embeddings and output projections achieves 100% on 14B (P155). The Sword Energy Equation: The critical FGA gain scales as g* ~ d0.45 with hidden dimension, enabling predictive intervention for any model scale (P159). The Cosine Threshold Theorem: FGA effectiveness follows a sigmoid with 50% crossover at cos ≈ 0.69. Below cos ≈ 0.5, intervention achieves 100% reliability (P164). Cross-Architecture Universality: Dual Surgery achieves 100% on GPT-2 (124M), proving geometric intervention is a law of Transformer architecture (P157/P161). Weight Tying Revelation: All previous "embed-only" surgery on 0.5B/1.5B/GPT-2 was secretly Dual Surgery due to tied weights (P165). Previously established in V1–V6: Grammatical Suppression of Facts (GSF): 70% of facts suppressed by final layers; L9H6 (+927) is the top suppressor Code Mode Switch: Any symbol prefix (// # →) triggers a mode transition reducing GSF DPO Suppression Theorem: DPO suppresses rejected tokens (100% reliability), not promoting correct ones (73%) L2 Distance Law: DPO's critical condition is L2 distance > 1.2, not cosine similarity Aletheia Constant (αA ≈ 0.94): Universal across architecture, language, and temperature Phases 1–138 from V1–V6 fully preserved 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.