Drift Away

Personal insight into the Leiden Declaration

I read the Leiden Declaration out of curiosity. Reading the assumptions behind its conclusions made me realize I had encountered many of them while working on my own AI project. My background is petrophysics — forty years of reading signals in noisy data. When I ran into drift and hallucinations, my

WP7 - Token Pressure as a Proxy for Drift Risk: Toward a Self-Monitoring Architecture for Human-AI Collaboration

WP7 Token Pressure as a Proxy for Drift Risk: Toward a Self-Monitoring Architecture for Human-AI Collaboration By Garwin Powers in collaboration with Claude (Anthropic) and CoPilot (Microsoft) May 2026 Part of the Drift Framework Series: WP1–WP9 Abstract Token pressure — the accumulative loss of high-weight content as

WP6 - Pressure Mapping: A Drift Detection Instrument for Narrative AI

WP6 Pressure Mapping: A Drift Detection Instrument for Narrative AI By Garwin Powers in collaboration with Claude (Anthropic) May 2026 Part of the Drift Framework Series: WP1–WP9 Executive Summary This paper describes a pressure mapping instrument for detecting and ranking drift-prone regions in narrative AI output. The instrument

WP4 — The Human–AI Creative Model A Framework for Collaborative Generation Using Vector Anchoring

WP4 — The Human–AI Creative Model A Framework for Collaborative Generation Using Vector Anchoring By Garwin Powers in collaboration with Claude (Anthropic) May 2026 Part of the Drift Framework Series: WP1–WP9 Abstract Human–AI creative work often fails for the same reason long‑form model output fails: drift. Most

WP3 — Drift as Diagnostic: A Method for Identifying Structural Instability in Model Output

WP3 — Drift as Diagnostic: A Method for Identifying Structural Instability in Model Output By Garwin Powers in collaboration with Claude (Anthropic) May 2026 Part of the Drift Framework Series: WP1–WP9 1. Introduction Large language models exhibit predictable patterns of drift when required to maintain ambiguity, tension, or unresolved state.

WP2 - The Drift Harness: A Framework for Detecting and Stabilizing Narrative Drift in Human–AI Collaboration

WP2 - The Drift Harness: A Framework for Detecting and Stabilizing Narrative Drift in Human–AI Collaboration By Garwin Powers in collaboration with Claude (Anthropic) May 2026 Part of the Drift Framework Series: WP1–WP9 Executive Summary This paper introduces a structured method for identifying and correcting drift in AI‑generated

WP1 - The A/B Drift Model: A Framework for Understanding Narrative Drift in Large Language Models

WP1 The A/B Drift Model: A Framework for Understanding Narrative Drift in Large Language Models Developed by Garwin Powers in collaboration with Claude (Anthropic) and CoPilot (Microsoft) May 2026 Part of the Drift Framework Series: WP1–WP9 Executive Summary This paper introduces interpretive drift, a specific category of drift

Drift Away

Insight into AI and drift

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