What does this research mean for the field?

Inference-time geometric interventions, specifically Dual Surgery, can deterministically suppress large language model hallucinations and restore factual knowledge across different model scales, rendering training-based alignment methods like DPO unnecessary. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.CHALLENGES_CONSENSUS.

What question did this study set out to answer?

The aim is to understand and control hallucination in large language models (LLMs) using geometric interventions.

May 18, 2026Open Access

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

Key Points

The aim is to understand and control hallucination in large language models (LLMs) using geometric interventions.
Systematic investigation through 165 phases including fundamental laws and theorems
Utilization of dual surgery and geometric interventions on various LLM architectures
Data analysis on accuracy and effectiveness of interventions with models like GPT-2 and Qwen-2.5-14B.
Achieved 100% accuracy with dual surgery on Qwen-1.5B without training (P144)
Confirmed that geometric intervention is universally effective across different transformer models (P157/P161)
Identified critical gain scaling laws for predictive intervention in LLMs (P159).

Abstract

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 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.

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