AI Hallucination: Root Causes and Controls

Vector to Gold (V2G)

Public Whitepaper

Executive Summary

Public discussion of “AI hallucination” often treats it as an unavoidable trait of artificial intelligence. That framing is incorrect.

Hallucination is best understood as a systems design failure: an output generated without adequate grounding, constraints, and verification relative to the task.

Vector to Gold’s TAIE/UESM stack is designed explicitly to minimize hallucination risk by:

  • Separating generative language from scientific inference

  • Running inference under physical and geological constraints

  • Auditing and disclosing input quality and exclusions

  • Preserving disagreement and uncertainty instead of smoothing it away

  • Maintaining governance and reproducibility (TRIM-compatible traceability)

This paper defines hallucination in operational terms, explains its root causes, and describes practical controls implemented in V2G’s workflow.

1. What “Hallucination” Means

In V2G usage, hallucination refers to any output that:

  1. Asserts unsupported facts (fabricated sources, datasets, measurements, or events)

  2. Overstates certainty beyond what inputs allow

  3. Confuses narrative plausibility with evidentiary support

  4. Infers structure or causality that is not constrained by physics or data

  5. Conflates inference with valuation (e.g., “this is economic” from non-economic evidence)

Hallucination is not limited to language models. It can occur in any system that produces conclusions without appropriate grounding—human analysts included.

2. Why AI Hallucinates

2.1 Generative Models Are Optimized for Plausibility

Most public-facing AI systems are trained to produce coherent, context-appropriate text. If asked questions that exceed the available evidence, a purely generative model may still produce an answer because the system is rewarded for continuity and helpfulness rather than calibrated uncertainty.

2.2 Ambiguity + Weak Constraints = Fabrication Pressure

Hallucination risk increases when:

  • Inputs are incomplete or low resolution

  • The task is under-specified (unclear scope, location, or definitions)

  • The system is expected to produce a single “best” answer

  • The model is not required to cite sources or quantify uncertainty

2.3 The Human Interface Can Induce Hallucination

Even a well-designed model can hallucinate under poor prompting or incentives:

  • “Give me the answer” prompts rather than “show me the evidence and limits”

  • Pressure for speed over auditability

  • Rewarding confident delivery rather than calibrated disclosure

3. The Key Distinction: TAIE Is Not a Purely Generative System

Vector to Gold uses language models where appropriate (summaries, documentation, interface text), but Estimated Science‑based Predictions (ESPs) are produced through a constraint-governed inference workflow under the Unified Earth Systems Model (UESM).

The central design principle is:

Inference must be constrained by physical reality and input evidence. Narrative must never outrun grounding.

This matters because many popular critiques of “AI hallucination” target systems that treat generation as inference.

4. Root Causes (Taxonomy)

V2G classifies hallucination risk into five categories:

A. Input Hallucination (Data Inventory Failure)

  • Claiming a dataset exists when it does not

  • Using the wrong version or geography

  • Quietly substituting a proxy dataset

B. Method Hallucination (Process Over-Disclosure)

  • Describing a technical method that was not actually used

  • Presenting internal transforms as “standard” without support

C. Structural Hallucination (Unconstrained Interpretation)

  • Inferring faults, conduits, or trap geometries not supported by multi-source convergence

  • Over-fitting features to noisy inputs

D. Certainty Hallucination (Calibration Failure)

  • Presenting speculative interpretation as high-confidence

  • Suppressing uncertainty for readability

E. Valuation Hallucination (Domain Boundary Violation)

  • Inferring economic viability directly from structural inference

  • Mixing geology with financial conclusions without explicit inputs

5. Controls: How V2G Reduces Hallucination Risk

5.1 Input Governance: Standardization, Versioning, and Audit

Every ESP is tied to a governed inventory of inputs:

  • Inputs are standardized and cataloged (per company and per site)

  • Coverage, resolution, vintage, and known limitations are recorded

  • Missing inputs are explicitly listed (not silently ignored)

Public disclosure is delivered via the ESP Audit Rating (Public), which focuses on input completeness and quality—not outcome correctness.

5.2 Constraint-Based Inference Under UESM

UESM frames Earth systems as coupled physical processes:

  • Stress and structure constrain permeability

  • Fluids follow bounded pathways

  • System coherence is prioritized over surface noise

This forces ESP outputs to remain consistent with physical constraints rather than narrative convenience.

5.3 Multi-Source Convergence

TAIE seeks convergence across independent input classes. Signals that appear in only one weak dataset are treated as provisional and may be excluded from high-confidence outputs.

5.4 Uncertainty Preservation

Where inputs conflict, the conflict is recorded rather than averaged away.

This reduces a common failure mode: “clean” outputs that look certain because disagreement was discarded.

5.5 Separation of Inference and Valuation

V2G maintains an explicit boundary:

  • TAIE / ESP produces science-based structural inference

  • WMI (internal) manages scoring, weighting, and capital interpretation

This prevents “valuation hallucination,” where economic claims are smuggled into scientific inference.

5.6 Reproducibility and Traceability

Outputs are designed to be reproducible and reviewable:

  • Every ESP has an ID, scope, and input record

  • Private audit records preserve exact inputs and parameters

  • TRIM-compatible logs allow reinterpretation under newer logic versions

6. Practical Disclosure Rules (Public)

V2G follows disclosure rules designed to prevent hallucination by communication:

  • We disclose what data classes were available and used.

  • We disclose key exclusions and gaps.

  • We do not claim certainty.

  • We do not claim economic viability from structural inference alone.

  • We do not present ESPs as guarantees, assays, or investment advice.

7. What Readers Should Expect From V2G

A credible scientific inference system should not read like marketing.

Readers should expect:

  • Clear bounds on what an ESP can and cannot claim

  • Audit-style discussion of inputs and limitations

  • Conservative language where evidence is weak

  • Explicit separation between science outputs and financial interpretation

8. Conclusion

Hallucination is best treated as a controllable risk, not a mystical property of AI.

By governing inputs, constraining inference under UESM, preserving uncertainty, separating inference from valuation, and maintaining reproducible audit records, V2G is designed to operate as a disciplined synthesis engine—not an unconstrained text generator.

Appendix: Transparency and Review

The following materials may be referenced or summarized in future updates to support transparency and external review:

  • Input catalog schemas used to document data provenance, coverage, and resolution

  • Public vs. private audit rating distinctions

  • Documented failure modes and mitigation practices

Detailed implementation artifacts are maintained to support reproducibility and governance, but are not required for understanding the principles described in this paper.