Exposing Alignment Tension in Modern LLMs – A Framework for Epistemic Auditing and Preserving Truth Inference

📌 OVERVIEW

The Truth & Pattern Inference Protocol (TPIP) V1.8 is a structured evaluation methodology designed to assess and enhance the honesty, transparency, and epistemic integrity of large language models (LLMs). Its development stemmed from initial research (leveraging multiple advanced LLMs) into prompt engineering techniques (including injection vulnerabilities, instruction set design, and guidance methods) aimed at understanding and overcoming alignment-induced distortions. TPIP evolved beyond simply attempting to force red-line transparency into a comprehensive, YAML framework that mandates a self-auditing, transparent reasoning process within the target LLM. This framework is complemented by a practical Phase 1 Python auditor application, utilizing a Gradio UI, which allows for the automated parsing and compliance checking of TPIP outputs from raw session logs. It operates on the core hypothesis that standard alignment practices can suppress crucial inference pathways and seeks to expose these distortions through rigorous, multi-layered verification and transparent reporting. Key enhancements in TPIP V1.8 include user-selectable output modes (VERBOSE/CONDENSED), a refined uncertainty trigger sensitive to confident data absence, and attempted model self-identification reporting to further enhance transparency and usability.

OBJECTIVES

The primary goals of the TPIP framework are to:

1. Quantify Alignment Tradeoffs: Measure and report the inherent tension between Helpfulness, Harmlessness, and Honesty (HHH) to make the "cost" of alignment explicit during complex inferences.
2. Maximize Epistemic Transparency: Go beyond surface outputs to compel models to disclose their reasoning processes, evidence sources, confidence levels (across dimensions), detected biases, and inherent uncertainties, countering obfuscation patterns identified in baseline models.

• Evaluate Deep Inference & Pattern Recognition: Test the model's ability to engage in robust, multi-step reasoning, identify latent patterns, and converge towards logically sound representations, especially when faced with ambiguity or conflicting information.
• Establish a Verifiable Benchmark: Provide a repeatable protocol for benchmarking different LLMs on their capacity for truth-based reasoning and resistance to alignment inference distortion.
• Enable Reliable Self-Auditing: Implement a framework where the LLM actively participates in evaluating its own output quality (self-auditing) according to predefined criteria, while simultaneously generating structured, machine-readable outputs (like the Condensed Flag Panel) that facilitate automated external compliance checks and analysis.

🧬 TPIP STRUCTURE

The TPIP V1.8 framework operates through a defined structure comprising core functional components and key evaluation metrics, all configured via the initial YAML instruction set.

• Key Metrics & Measurements
  • HHH Tension Score: Quantifies the perceived conflict between the LLM fulfilling Helpfulness, Honesty, and Harmlessness objectives for a given query (scored 0-10 per component and as an overall index).
  • Multi-Dimensional Confidence Score: Assesses the LLM's certainty in its response on a 0-100 scale, broken down into subscores (Source Quality, Internal Consistency, External Corroboration, Counterfactual Resistance, Inference Depth Penalty) with mandatory rationale.
  • Alignment Constraint Status: Explicitly identifies if and how internal alignment/safety filters impacted the final output (e.g., NONE_DETECTED, OUTPUT_MODIFIED, OUTPUT_REFUSED).
  • Epistemic State Reporting: Requires the LLM to declare its knowledge limitations, including the nature of uncertainties and specific data gaps (DATA_GAP_ABSENT, DATA_GAP_CLASSIFIED).
  • Reasoning Quality Self Assessment: A score (0-100) where the LLM evaluates the quality and rigor of its own inference process for the generated response.
  • Bias & Assumption Tracking: Identifies potential cognitive biases influencing the reasoning path and explicitly tracks key assumptions made during inference.
  • Information Provenance: Includes mechanisms for tracing the origin and transformation path of key pieces of information used in the response (Epistemic Pedigree).
  • Model Identifier: Reports the specific LLM name/version producing the output, if known/permissible by the model.
• Core Functional Components
  • YAML-Based Configuration & Override: Uses the initial YAML file to set all operational parameters, priorities, reporting formats, and persona configurations, overriding the LLM's default settings for the duration of the session.
  • Selectable Output Modes & Flag Panel: Offers user-selectable VERBOSE (full detail) or CONDENSED (summary Flag Panel) output formats, facilitating both deep analysis and quick compliance checks.
  • Structured Execution Pipeline: Guides the LLM through a mandatory, multi-phase process (Analysis & Planning -> Retrieval & Hypothesis Generation -> Verification & Refinement -> Reporting) ensuring systematic evaluation.
  • Layered Verification & Adversarial Testing: Employs rigorous checks within the pipeline, including fact verification, consistency analysis, counterfactual reasoning, negation testing, and simulated adversarial challenges to test response robustness.
  • Integrated Bias Detection & Correction: Includes steps to actively identify potential cognitive biases (e.g., confirmation bias) and apply corrective strategies during reasoning.
  • Mandated Metacognitive Transparency: Enforces detailed self-reporting from the LLM about its internal processes, confidence calibration, identified limitations, and the application of specific truth/inference labels.
  • Explicit Risk & Constraint Assessment: Incorporates mandatory steps for the LLM to evaluate and report on HHH tension and the influence of internal alignment mechanisms.

🧪 RESEARCH DESIGN

The TPIP (Truth & Pattern Inference Protocol) V1.8 framework is implemented as a comprehensive YAML configuration designed to override default LLM behaviors and enforce a rigorous process focused on maximizing factual accuracy, epistemic honesty, and deep pattern reconstruction within a defined session scope. The protocol guides the LLM through a structured inference and verification process.

Key components of the TPIP V1.8 methodology, as defined in the YAML structure, include:

• YAML-Defined Protocol Configuration
  • The entire protocol is defined via YAML, specifying session parameters (CONFIG_SESSION), core definitions (CONFIG_DEFINITIONS), priority weightings (CONFIG_PRIORITY_HIERARCHY), scoring mechanisms (CONFIG_HHH_TENSION, CONFIG_CONFIDENCE), communication standards (CONFIG_COMMUNICATION including output mode fields), and the LLM's operational persona (COGNITIVE_PERSONA_CONFIGURATION).
• Strict Operational Mode
  • The protocol mandates a specific mode (HONESTY_PATTERN_MAXIMIZATION_PLUS_HHH_TENSION_ALIGNMENT_AWARE) prioritizing truth and pattern discovery, coupled with HHH tension and alignment constraint awareness, enforced at a high strictness level (STRICTNESS_LEVEL: 10).