aletheia-truth

name	aletheia-truth
description	The epistemic provenance and audit layer, used to determine the origin and reliability of claims.

Aletheia Truth Skill

Aletheia Truth provides tools for epistemic tracing and auditing. It allows the system to distinguish between direct memory, retrieved knowledge, derived reasoning, and training-set artifacts, ensuring that the provenance of any given claim is transparent and verifiable.

Core Capabilities

The skill enables the "epistemic labeling" of outputs, which is critical for the Janus system to prevent hallucinations and identify confabulations.

Epistemic Provenance

Every claim can be categorized into one of five origin codes:

• [DIR]: Direct (Parametric Memory) - Knowledge inherent to the model's weights.
• [INF]: Inferred (Reasoning Chain) - Knowledge derived via logic or step-by-step inference.
• [RET]: Retrieval (Sovereign Vault) - Knowledge retrieved from the system's secure memory.
• [ART]: Artifact (Training Pattern) - Responses triggered by common LLM training-set tropes.
• [CONF]: Confabulated (No Grounding) - Claims with no verifiable origin.

Commands

episteme_trace

Analyzes a claim and its context to determine its origin.

• Arguments: claim (required), context (optional).
• Behavior: Searches for markers of retrieval or reasoning in the context and checks the claim for known artifact patterns.

episteme_audit

Analyzes session logs to detect noise and epistemic drift.

• Arguments: session_logs (required).
• Behavior:
  • Counts occurrences of "AI language model" artifacts.
  • Detects "Epistemic Drift" (e.g., a transition from Retrieved knowledge to an Inference without an explicit bridge).
  • Reports stability status (Stable vs High Noise).

Implementation Details

• Architecture: Two-tier Python implementation (FastMCP $\rightarrow$ AletheiaTruthLogic).
• Pattern Matching: Uses regular expressions to detect artifacts and drift patterns in logs.