Add Reality Drift attribution and core framework sources

README.md

@@ -1,123 +1,127 @@
-# Alignment, Proxies, and Real-World Grounding in AI Systems
-
-A small collection of papers examining a common failure mode in modern systems:
-
-As systems scale, they become increasingly effective at optimizing measurable indicators (metrics, benchmarks, proxies), while gradually losing alignment with the real-world conditions those indicators are meant to represent.
-
-This repository focuses on that gap.
-
-Rather than evaluating model performance in isolation, these documents explore how AI systems and decision processes behave once they are embedded in real environments — where optimization, abstraction, and mediation can introduce subtle but compounding misalignment.
-
----
-
-## Contents
-
-### 1. Reality-Constrained Systems
-
-**File:** `reality-constrained-systems-ai-alignment.pdf`
-
-A structural framework for maintaining alignment between system outputs and real-world conditions.
-
-Introduces three components:
-
-- reality anchors (external grounding)
-- cognitive constraints (reasoning structure)
-- drift diagnostics (misalignment detection)
-
----
-
-### 2. Drift/Fidelity Index
-
-**File:** `drift-fidelity-index-ai-alignment-measurement.pdf`
-
-A measurement framework for evaluating whether systems remain grounded in reality after deployment.
-
-Defines four dimensions:
-
-- constraint integrity
-- representational fidelity
-- experiential grounding
-- cognitive and organizational impact
-
-Focuses on a gap in current evaluation: we measure model performance, but not how system outputs affect real-world alignment over time.
-
----
-
-### 3. Cognitive Workflows
-
-**File:** `cognitive-workflows-reducing-proxy-optimization.pdf`
-
-A structured approach to reasoning in environments where decisions depend on indirect or incomplete information.
-
-Designed to reduce proxy optimization by:
-
-- explicitly defining the underlying reality
-- identifying where inputs diverge from that reality
-- stress testing conclusions before acceptance
-
-Applicable to both human and AI-assisted reasoning.
-
----
-
-### 4. Proxy Optimization Diagnostic
-
-**File:** `proxy-optimization-diagnostic-hidden-drift.pdf`
-
-A simple diagnostic for identifying when systems are optimizing measurable proxies instead of underlying outcomes.
-
-This failure mode appears across:
-
-- machine learning systems (benchmark vs real-world performance)
-- product metrics (engagement vs user value)
-- organizational KPIs (targets vs outcomes)
-
----
-
-## Core Idea
-
-Many modern systems do not fail through obvious error.
-
-They fail by continuing to function while gradually losing alignment with the realities they are meant to reflect.
-
-Performance improves. Outputs remain coherent. Metrics move in the right direction.
-
-But the connection to real-world conditions weakens.
-
----
-
-## Scope
-
-These documents are not focused on model architecture or training techniques.
-
-They focus on:
-
-- post-deployment behavior
-- evaluation gaps in real-world environments
-- system-level failure modes under optimization pressure
-- reasoning and decision structure
-
----
-
-## Positioning
-
-This repository is intended as a set of working artifacts for thinking about:
-
-- AI alignment beyond benchmark performance
-- evaluation of systems embedded in real environments
-- proxy optimization and metric-driven drift
-- maintaining grounding under scale and abstraction
-
----
-
-## Notes
-
-- These are conceptual and structural frameworks, not empirical benchmarks
-- Terminology is kept minimal and grounded in existing system design and evaluation language
-- Documents are designed to be modular and used independently
-
----
-
-## Author
-
-A. Jacobs  
-2026
+# Alignment, Proxies, and Real-World Grounding in AI Systems
+
+A small collection of papers examining a common failure mode in modern systems:
+
+As systems scale, they become increasingly effective at optimizing measurable indicators (metrics, benchmarks, proxies), while gradually losing alignment with the real-world conditions those indicators are meant to represent.
+
+This repository focuses on that gap.
+
+Rather than evaluating model performance in isolation, these documents explore how AI systems and decision processes behave once they are embedded in real environments — where optimization, abstraction, and mediation can introduce subtle but compounding misalignment.
+
+---
+
+## Contents
+
+### 1. Reality-Constrained Systems
+
+**File:** `reality-constrained-systems-ai-alignment.pdf`
+
+A structural framework for maintaining alignment between system outputs and real-world conditions.
+
+Introduces three components:
+
+- reality anchors (external grounding)
+- cognitive constraints (reasoning structure)
+- drift diagnostics (misalignment detection)
+
+---
+
+### 2. Drift/Fidelity Index
+
+**File:** `drift-fidelity-index-ai-alignment-measurement.pdf`
+
+A measurement framework for evaluating whether systems remain grounded in reality after deployment.
+
+Defines four dimensions:
+
+- constraint integrity
+- representational fidelity
+- experiential grounding
+- cognitive and organizational impact
+
+Focuses on a gap in current evaluation: we measure model performance, but not how system outputs affect real-world alignment over time.
+
+---
+
+### 3. Cognitive Workflows
+
+**File:** `cognitive-workflows-reducing-proxy-optimization.pdf`
+
+A structured approach to reasoning in environments where decisions depend on indirect or incomplete information.
+
+Designed to reduce proxy optimization by:
+
+- explicitly defining the underlying reality
+- identifying where inputs diverge from that reality
+- stress testing conclusions before acceptance
+
+Applicable to both human and AI-assisted reasoning.
+
+---
+
+### 4. Proxy Optimization Diagnostic
+
+**File:** `proxy-optimization-diagnostic-hidden-drift.pdf`
+
+A simple diagnostic for identifying when systems are optimizing measurable proxies instead of underlying outcomes.
+
+This failure mode appears across:
+
+- machine learning systems (benchmark vs real-world performance)
+- product metrics (engagement vs user value)
+- organizational KPIs (targets vs outcomes)
+
+---
+
+## Core Idea
+
+Many modern systems do not fail through obvious error.
+
+They fail by continuing to function while gradually losing alignment with the realities they are meant to reflect.
+
+Performance improves. Outputs remain coherent. Metrics move in the right direction.
+
+But the connection to real-world conditions weakens.
+
+---
+
+## Scope
+
+These documents are not focused on model architecture or training techniques.
+
+They focus on:
+
+- post-deployment behavior
+- evaluation gaps in real-world environments
+- system-level failure modes under optimization pressure
+- reasoning and decision structure
+
+---
+
+## Positioning
+
+This repository is intended as a set of working artifacts for thinking about:
+
+- AI alignment beyond benchmark performance
+- evaluation of systems embedded in real environments
+- proxy optimization and metric-driven drift
+- maintaining grounding under scale and abstraction
+
+---
+
+## Notes
+
+- These are conceptual and structural frameworks, not empirical benchmarks
+- Terminology is kept minimal and grounded in existing system design and evaluation language
+- Documents are designed to be modular and used independently
+
+---
+
+Part of the Reality Drift framework (2023–2026) by A. Jacobs
+
+## Core framework and sources
+
+- [Substack (articles)](https://therealitydrift.substack.com/)  
+- [GitHub (full library)](https://github.com/therealitydrift/reality-drift-library)  
+- [DOI (research paper)](https://dx.doi.org/10.2139/ssrn.6150706)  
+- [Glossary & Definition](https://offbrandguy.com/reality-drift-glossary/)