Hallucination Energy

🚀 Summary

This post presents the current research draft and implementation of a geometric framework for bounding stochastic language models through deterministic policy enforcement.

The central contribution is a scalar metric termed Hallucination Energy, defined as the projection residual between a claim embedding and the subspace spanned by its supporting evidence embeddings. This metric operationalizes grounding as a measurable geometric quantity.

We proceed in three stages:

1. Formal Definition a draft manuscript introducing Hallucination Energy, its mathematical formulation, and its role within a policy-controlled architecture.
2. Empirical Evaluation structured calibration and adversarial stress testing across multiple domains to assess the robustness and limits of projection-based grounding.
3. Applied Validation large-scale evaluation on 10,000 samples from the HaluEval summarization benchmark, demonstrating that projection-based containment functions as a strong first-order grounding signal in a real generative setting.

This work does not claim to solve hallucination. Rather, it characterizes the boundary of projection-based grounding, establishes its suitability as a deterministic policy scalar, and documents both its strengths and its structural limitations.

⏰ TLDR

This work shows that the hardest part of using AI in high-trust environments is not the model, but the policy. Once editorial policy is made explicit and executable, AI systems become interchangeable the real challenge is engineering reliable measurements and deterministic enforcement of those policies.

📋 Summary

AI systems are becoming deeply embedded in how we research, write, and reason. At the same time, their use in high-trust environments is under strain not because models are incapable, but because they are being deployed into settings that demand determinism, provenance, and enforceable rules.