| # 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. | |
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| ## 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) | |
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| ## 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 | |
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| ## Author | |
| A. Jacobs | |
| 2026 | |