Dual-Model Adversarial Intelligence as a Method for Documenting PrimerField Theory

David Allen LaPoint

PrimerField Foundation

December 01, 2025

Abstract

PrimerField Theory was developed through nineteen years of experimental research without artificial intelligence involvement. The core theory was complete and stable by 2012, documented in YouTube videos that remain accurate today. AI entered this work only in 2025—not as a theoretical collaborator but as an analytical instrument for validating documentation, enforcing constraint compliance, and stress-testing written formulations. This paper documents the methodology that emerged: the deliberate use of multiple large language models exhibiting different behavioral characteristics as adversarial review tools.

The approach treats AI systems not as sources of theoretical insight but as diagnostic instruments with complementary failure modes. When two behaviorally distinct models—ChatGPT and Claude AI—examine the same theoretical documentation, their disagreements frequently expose hidden assumptions, logical gaps, and definitional ambiguities that persist undetected in single-model workflows. This creates a form of parallax—depth and structure become visible only when non-identical perspectives are held in productive tension under explicit human constraint.

The result is a documentation review process more adversarial and less forgiving than conventional review with respect to internal logical consistency, while maintaining absolute human authority over theoretical content and canonical claims. The theory itself remains entirely human-originated; AI serves only to ensure that written expression of that theory is internally consistent and precisely bounded.

Scope: This paper documents a documentation and validation methodology, not a physical theory claim. The validity of the method does not depend on the validity of PrimerField Theory itself.

1. Introduction: AI as Instrument, Not Authority

PrimerField Theory did not emerge from artificial intelligence. The core concepts—bowl shaped magnetic field geometries, plasma confinement structures, photon field extension, and emergent wave behavior from particle interactions—developed through initial inspiration in 2006 and stable theory by 2012, documented in three YouTube videos from 2012-2013 that remain accurate today. This represents more than twelve years of unchanged theory developed prior to the availability of modern large language models.

AI entered this work not as a theoretical collaborator but as an analytical instrument. The models perform specific diagnostic functions: adversarial review, logical compression testing, rhetorical stress testing, and structured rewriting under constraint. At no point does any model operate as an epistemic authority. Theory originates with the human researcher; AI systems exist to break weak formulations, not to generate strong ones.

This distinction matters because it shapes every subsequent methodological choice. A model trusted to invent theory will optimize for coherence. A model constrained to attack theory will optimize for inconsistency detection. The PF workflow requires the latter.

2. The Hazard of Single-Model Reliance

Every large language model possesses characteristic biases, preferred explanatory styles, and systematic blind spots. When a single model drafts, revises, and validates theoretical content, its particular failure modes remain invisible throughout the entire process. Errors that cohere with the model's internal priors feel natural; claims that violate them get flagged. The result is self consistent hallucination—output that appears rigorous precisely because it has been filtered through a single lens.

This problem intensifies in non-standard theoretical frameworks like PrimerField Theory, where conventional physics assumptions cannot be imported uncritically. A single model will tend to smooth contradictions between PF claims and standard physics, either by unconsciously importing mainstream assumptions or by generating plausible sounding but unfounded reconciliations.

The solution is not to find a better model. The solution is to exploit the behavioral differences between models as a diagnostic signal.

3. Behavioral Asymmetry Between ChatGPT and Claude

ChatGPT and Claude AI are not interchangeable instruments. In repeated application, they exhibit reliably different behaviors that can be exploited for rigorous theory development. The causes of these differences may include variations in training data, alignment strategies, and reinforcement learning objectives, but the methodology described here relies only on observed behavioral patterns, not on claims about model internals. These behavioral characterizations are time-indexed observations based on model versions available through late 2025; as noted in Section 9, model drift requires periodic reassessment. The role assignments described below are methodological choices, not claims about inherent or permanent model capabilities.

3.1 ChatGPT: Constraint Enforcement and Hostile Review

In the PF workflow, ChatGPT functions as the primary constraint enforcement mechanism. Its observed strengths include: long context retention across multiple revision cycles, reliable detection of canon violations even when those violations are rhetorically polished, systematic terminology policing, identification of scope creep and hidden assumptions, and the capacity for genuine hostile read review. Canon, in this context, means locked constraint documents controlled by the human author that define what claims are permitted and what terminology is binding.

The term 'hostile read' requires definition: it means assuming every claim is potentially false and actively seeking internal inconsistencies, unstated dependencies, and logical gaps—rather than reading charitably to understand intent. When instructed to perform hostile read review, ChatGPT executes that instruction with useful literalism. It flags violations of explicitly stated constraints even when the violating passage sounds reasonable. This makes ChatGPT well suited for final quality assurance, adversarial audit, and canon lock-in—the point at which theoretical claims become binding on all subsequent work. Hostile read does not, however, perform empirical validation, verify domain truth claims, or assess correspondence with external reality; it operates strictly on internal consistency.

3.2 Claude: Rhetorical Synthesis and Aggressive Expression

Claude exhibits complementary strengths: high fluency narrative synthesis, aggressive rhetorical framing when permitted, structural reexpression without excessive fragmentation, and the production of reader engaging drafts. Claude is particularly effective at stress testing ideas rhetorically. When allowed to write forcefully, Claude often reveals whether an argument actually holds together when stated without hedging.

However, Claude exhibits corresponding tendencies that require constraint: a disposition to soften boundaries, introduce uncanonized language, and implicitly harmonize contradictions rather than expose them. These tendencies are useful when the goal is synthesis but hazardous when the goal is error detection.

For this reason, Claude is intentionally not used as a final arbiter of correctness in the PF workflow. Claude generates; ChatGPT validates.

4. The Dual-Model Adversarial Loop

The PF paper development process follows a deliberate sequence. First, the human researcher supplies theory, constraints, and canon—the binding claims that all subsequent work must respect. Claude produces an aggressive or high clarity draft. ChatGPT performs a cold, publication grade hostile audit. Errors, overreach, and violations are explicitly flagged. Claude rewrites under corrected constraints. ChatGPT performs final quality assurance before release.

The critical feature of this loop is that disagreement between models functions as a diagnostic signal. When ChatGPT and Claude produce different outputs from the same input, the difference typically traces to one of four sources: ambiguous definitions, unfenced assumptions, rhetorical overreach, or logical gaps. Single-model workflows routinely miss these issues because the model's consistency with itself masks its deviation from the intended meaning.

Neither model is permitted to validate its own output. Canon exists outside the models—in locked foundation documents controlled by the human researcher—not inside them.

5. Why This Method Produces Stronger Theory

The dual-model approach yields specific methodological advantages.

Error visibility through disagreement. When two models with different behavioral tendencies reach different conclusions, the disagreement localizes the problem. Either one model has violated a constraint, one model has introduced an unintended interpretation, or the original formulation was genuinely ambiguous. All three outcomes produce actionable information.

Immunity to consensus illusions. Because neither model is permitted to validate its own output, false coherence does not accumulate across revision cycles. A claim that 'feels right' to Claude still faces hostile scrutiny from ChatGPT. A constraint that ChatGPT enforces rigidly still gets tested against Claude's tendency to seek alternative interpretations.

Preserved human authority. The human researcher defines canon, approves revisions, resolves disagreements, and bears responsibility for all claims. AI systems function strictly as instruments. This is not a limitation imposed reluctantly; it is the design principle that makes the method work.

Productive constraint pressure. Working under explicit constraints with adversarial review forces theoretical claims to become more precise over successive iterations. Vague formulations that survive Claude's synthesis get sharpened when they fail ChatGPT's audit. The result is theory that has been compressed into its defensible core.

6. Scope of Claimed Advantage

The dual-model adversarial method offers advantages over single-model workflows in specific, bounded domains. It is essential to define both where the method provides value and where it does not apply. No comparative empirical study is presented here; the claims below derive from procedural analysis and documented internal application.

Where this method provides advantage: internal logical coherence, definitional consistency, constraint compliance, detection of scope creep, identification of hidden assumptions, and adversarial critique of rhetorical claims. In these areas, continuous full document reaudit after each revision provides a form of systematic scrutiny that single-model workflows typically do not provide under identical constraints.

Where this method does not apply: empirical validation, experimental replication, statistical methodology review, domain expertise verification, and assessment of a theory's relationship to established literature. The method is orthogonal to these concerns—it does not compete with them and makes no claim to address them.

The claim is not that dual-AI review replaces traditional scientific evaluation. The claim is that it provides a form of internal logical stress testing that complements other review processes.

7. Addressing the 'AI Wrote This' Objection

A predictable criticism of any AI-assisted theoretical work is the claim that the AI generated the content and the human merely approved it. The documented PF workflow directly contradicts this characterization.

PrimerField Theory predates AI involvement by more than a decade, with stable theory established by 2012 and documented in YouTube videos that remain accurate today. The core experimental observations, the bowl shaped field geometry hypothesis, the plasma confinement predictions—all of this existed before any large language model was consulted. Canon is human authored and document locked, maintained in foundation documents that no AI system has authority to modify. AI outputs are repeatedly rejected, corrected, and constrained; the revision history of any PF paper shows far more rejections than approvals.

No model is trusted to define ontology. When a model suggests that a PF concept maps to some standard physics construct, that suggestion is treated as a hypothesis requiring explicit human evaluation, not as an authoritative determination.

In documented use, the dual-AI method has proven more adversarial and less forgiving at the level of internal logical consistency than typical prepublication peer review. A human reviewer might miss an inconsistency; two behaviorally distinct AI systems examining the same text from different angles frequently do not in documented internal use.

7.1 Evidence Boundary

This paper describes a methodology based on procedural design and documented internal application. The term 'documented' refers to internal revision logs, version histories, and rejection rates across multiple paper development cycles. No controlled experiment comparing dual-model review to single-model review or traditional peer review has been conducted.

What this paper does not claim: comparative empirical superiority over peer review, measured error detection rates, quantified improvement metrics, or applicability beyond constraint-heavy theoretical frameworks where canonical definitions must be enforced. The method is designed for contexts resembling PrimerField Theory development—where unconventional claims require rigorous internal consistency checking and where standard physics assumptions cannot be imported without explicit evaluation.

8. The Binocular Vision Analogy

Using two fundamentally different AI systems resembles binocular vision. This analogy is illustrative, not evidentiary—it clarifies the conceptual structure of the method without constituting proof of its effectiveness.

With one eye closed, depth collapses. Structure flattens. Errors hide in plain sight because there is no parallax to reveal them. A single model produces output that appears coherent from one angle, but that coherence may be an artifact of the viewing angle rather than a property of the object.

With two non-identical perspectives, parallax reveals inconsistency. False alignment becomes visible. Structure acquires depth. The slight difference in perspective between the two systems exposes features that neither would detect alone.

PrimerField Theory papers are not produced by AI consensus. They are produced by AI parallax under human control—the deliberate exploitation of model differences to reveal the shape of the theoretical claims themselves.

9. Replicability Protocol

The dual-model adversarial method is procedurally reproducible. The following protocol defines the essential structure. Note that process replicability is claimed—the ability to execute the same procedural steps—not outcome replicability. Different theoretical content subjected to this process will yield different results; the method does not guarantee identical conclusions across applications.

Roles. Model A (synthesis): produces drafts optimized for clarity, rhetorical force, and narrative coherence. Model B (validation): performs hostile read audits optimized for constraint enforcement, error detection, and logical scrutiny. Human authority: defines canon, resolves disputes, approves all outputs, and bears final responsibility for claims.

Sequence. (1) Human supplies theory, constraints, and canon. (2) Model A produces aggressive draft. (3) Model B performs cold hostile audit. (4) Errors and violations are flagged explicitly. (5) Model A rewrites under corrected constraints. (6) Model B performs final QA. (7) Human approves or rejects for release.

Acceptance rules. No model validates its own output. Full document reaudit occurs after each revision cycle. Canon exists in locked documents external to both models. Disagreement between models triggers investigation, not automatic resolution.

Model drift. Large language models change over time as vendors update training and alignment. The method assumes periodic recalibration: behavioral profiles must be reassessed when model versions change significantly. The adversarial structure remains valid; the specific behavioral assignments may require adjustment.

10. Conclusion

The combined use of ChatGPT and Claude AI in PrimerField Theory development is not a convenience or a productivity enhancement. It is a deliberate methodological choice designed to expose error, enforce discipline, and strengthen theoretical claims under adversarial pressure.

The method exploits behavioral differences between models rather than seeking consensus between them. It maintains absolute human authority over canonical claims while leveraging AI capabilities for constraint enforcement and error detection. In repeated application, it produces theoretical work that has undergone systematic adversarial review for internal logical consistency—a form of stress testing that complements rather than replaces traditional scientific evaluation.

This approach represents a replicable procedural framework for AI-assisted theory development—particularly valuable in domains where existing paradigms cannot be assumed correct, where canonical definitions must be enforced, and where unconventional claims require rigorous internal consistency checking.

Artificial intelligence did not replace thinking. It made weak thinking harder to hide.