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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
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