Secretary Suite

Corpus – Guided Analytical Agents:

By

The Secretary Suite Method for Training Scientific Evaluation AI

DOI:

John Swygert

March 4, 2026

Abstract

The evaluation of scientific research increasingly involves large language models (LLMs), yet such systems frequently suffer from probabilistic drift, hallucination, and inconsistent reasoning across large document collections. This paper introduces the Secretary Suite Method, a corpus-guided framework for training analytical AI agents using curated knowledge baselines.

Rather than relying on probabilistic text generation, the method anchors reasoning to a structured corpus containing foundational concepts, logical standards, methodological references, exemplary documents, and diagnostic counterexamples. AI agents analyze scientific papers through shard decomposition, equilibrium consistency testing, and convergence scoring relative to the corpus baseline.

The resulting system produces standardized analytical outputs including numerical consistency scores, diagnostic reports, and categorical grading (Green / Yellow / Red). The framework further supports ontological positioning of scientific work within a structured knowledge map. This approach enables distributed AI systems to perform structured scientific evaluation while preserving provenance, local sovereignty, and reasoning stability across large knowledge environments.

1. Introduction

Large language models have demonstrated remarkable capabilities in text generation and reasoning tasks. However, their use in scientific analysis is limited by several persistent challenges:

  • probabilistic hallucination
  • inconsistent reasoning across documents
  • lack of stable reference frameworks
  • difficulty evaluating scientific validity

Traditional peer review relies on expert judgment guided by disciplinary norms. The Secretary Suite method proposes an alternative architecture in which AI agents evaluate scientific work relative to a curated conceptual corpus.

Instead of generating answers freely, agents operate within structured reasoning containers that constrain analysis to corpus-derived baselines. This architecture allows analytical agents to behave more like structured referees than probabilistic text generators.

2. Core Concepts

2.1 Corpus

A corpus is a curated collection of reference documents defining the conceptual baseline for evaluation. The corpus contains:

  • foundational theoretical concepts
  • logical reasoning standards
  • methodological references
  • exemplary high-quality research
  • diagnostic examples illustrating common reasoning errors

Together these establish the analytical baseline against which new scientific papers are evaluated.

2.2 Baseline

The baseline represents the equilibrium reference structure derived from corpus primitives. Analytical reasoning is evaluated by measuring the convergence of new claims toward this baseline.

High convergence indicates logical compatibility with established reasoning frameworks.

2.3 Shards

A shard is the smallest meaningful analytical unit extracted from a scientific document.

Examples include:

  • definitions
  • claims
  • equations
  • evidence statements
  • assumptions

Shards are fingerprinted and tracked for provenance to ensure that analytical evaluation does not mutate original content.

2.4 Containers

A container is a bounded reasoning environment in which assumptions and logical rules remain valid.

Container boundaries ensure that:

  • arguments do not rely on contradictory assumptions
  • reasoning steps remain internally consistent
  • conclusions propagate correctly from premises

Invalid containers produce contradictions that analytical agents can detect automatically.

2.5 Analytical Agents

Analytical agents are task-bound AI modules responsible for evaluating scientific work.

Examples include:

  • research agents (extract claims and evidence)
  • referee agents (check consistency and derivations)
  • mapping agents (place work within knowledge ontologies)

Agents operate within node-based environments that preserve sovereignty and provenance.

3. Corpus Design

A training corpus must contain several structured document classes.

Foundational Concepts

Core theoretical primitives forming the conceptual baseline.

Logical Standards

Rules governing valid reasoning structures.

Examples include:

  • avoidance of circular reasoning
  • container validity
  • propagation consistency

Methodological References

Experimental and observational verification procedures.

Exemplary Documents

Scientifically coherent works aligned with corpus baselines.

Diagnostic Examples

Documents containing deliberate reasoning errors used to train contradiction detection.

4. Analytical Evaluation Procedure

Scientific papers are evaluated through a structured multi-stage process.

Step 1 — Ingestion

The document is loaded into the analytical environment and decomposed into shards.

Step 2 — Extraction

Claims, definitions, equations, and evidence statements are identified and fingerprinted.

Step 3 — Consistency Check

Shards are compared to corpus baselines for logical compatibility.

Key checks include:

  • container validity
  • reasoning propagation
  • definition completeness
  • evidence alignment

Step 4 — Convergence Scoring

Analytical convergence is quantified using a normalized metric between 0 and 1.

Score = convergence of paper shards to corpus baseline

Higher scores represent greater reasoning stability.

Step 5 — Grading

Scores map to categorical evaluation grades.

Score

Grade

Meaning

0.8 – 1.0

Green

Stable framework suitable for publication

0.4 – 0.8

Yellow

Partially stable; revisions recommended

< 0.4

Red

Major contradictions or unsupported claims

Step 6 — Reporting

Agents generate a standardized evaluation report.

5. Contradiction Detection

Analytical agents identify several types of reasoning failures.

Internal Contradictions

Conflicting claims within a paper.

Missing Definitions

Concepts introduced without formal definition.

Unsupported Leaps

Conclusions that do not propagate logically from premises.

Container Violations

Arguments that rely on mutually incompatible assumptions.

These failures appear as low-convergence shards relative to the corpus baseline.

6. Ontological Mapping of Scientific Knowledge

Beyond grading, analytical agents may position research within a structured knowledge map.

In this framework:

  • the origin represents foundational conceptual primitives
  • axes represent theoretical extensions and constraints
  • papers occupy positions based on conceptual convergence

Such mapping allows large knowledge environments to evolve coherently while maintaining logical stability.

7. Minimal Extensions Required

The Secretary Suite corpus already contains sufficient conceptual structure to train analytical evaluation agents.

However, one additional formal component would improve large-scale knowledge mapping:

AO Coordinate Systems for Ontological Knowledge Mapping

This additional framework would define formal coordinate assignments for positioning scientific work within knowledge space.

8. Conclusion

The Secretary Suite Method demonstrates that large language models can perform structured scientific evaluation when anchored to curated conceptual corpora.

By decomposing documents into analytical shards and measuring convergence relative to stable baselines, AI agents can detect contradictions, evaluate analytical strength, and classify research quality.

This approach offers a pathway toward scalable, distributed scientific review systems capable of maintaining reasoning stability across large knowledge environments.

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