HaluEval on AIBussin — AI applications, systems and books

Hallucination Energy

Sat, 29 Aug 2026 23:14:00 +0100

Chapter 4 ended with a rule:

A hallucination measurement is a sensor, not a verdict.

This chapter builds one such sensor completely.

It begins with a property we care about:

semantic containment

But the first lesson of Chapter 4 was the proxy gap. We cannot inspect semantic containment directly. We can inspect representations.

So the construction in this chapter has three layers:

    graph TD
    LP[LATENT PROPERTY: semantic containment] --> RA[representation assumption]
    RA --> PP[PROXY PROPERTY: embedding-subspace containment]
    PP --> GC[exact geometric calculation]
    GC --> M[MEASUREMENT: squared projection residual]

That distinction is the chapter’s most important caveat.

Appendix: The Evidence Ledger

Mon, 31 Aug 2026 09:00:00 +0100

The book argues that a claim should be governed by its provenance, its evidential support, and an explicit statement of what it does not establish. This appendix applies that discipline to the book itself.

Every original empirical number produced for this book is listed here with three things: where it was measured, what its provenance status is, and what it does not establish.

Two facts are visible immediately from the ledger:

Hallucination Energy: A Geometric Foundation for Policy-Bounded AI

Thu, 05 Feb 2026 13:29:55 +0000

🚀 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:

Formal Definition a draft manuscript introducing Hallucination Energy, its mathematical formulation, and its role within a policy-controlled architecture.
Empirical Evaluation structured calibration and adversarial stress testing across multiple domains to assess the robustness and limits of projection-based grounding.
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.