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Overview
This dataset contains a research paper introducing constraint collapse, a structural failure mode in large language models and other symbolic systems.
Modern language models can produce coherent, confident outputs while being persistently wrong. These errors do not interrupt generation or impose internal cost. Instead, correction is externalized to users, evaluation pipelines, or retraining cycles.
This creates a system that continues operating without integrating feedback.
Constraint collapse describes the condition where:
- feedback continues to circulate
- but no longer enforces correction
- allowing semantic fidelity to degrade over time
This is not a hallucination problem. It is a failure of correction dynamics.
Key Concepts
Constraint Collapse
A second-order failure mode where feedback exists but no longer changes system behavior.
Fidelity Decay
The gradual loss of alignment between representations and underlying reality as compression increases.
Operational Continuity Without Correction
Systems remain stable, fluent, and responsive while misalignment persists indefinitely.
First-Order Failures
The paper identifies four interacting failure modes that enable drift:
Delayed or Externalized Consequence
Errors do not impose internal cost and are handled outside the system.Compression Without Fidelity
Representations are optimized for plausibility faster than they are validated.Legibility Outruns Reality
Clear, confident outputs are rewarded even when uncertainty is unresolved.Continuation Outpaces Correction
Generation proceeds by default without mechanisms to halt under uncertainty.
When combined, these produce a system where correction no longer interrupts generation.
Why This Matters
Most current evaluation approaches focus on:
- output correctness
- coherence
- benchmark performance
They do not measure whether:
- systems slow down under uncertainty
- contradictions trigger revision
- incorrect outputs impose internal cost
- generation ever stops when it should
As a result, models can appear aligned while silently drifting.
Implications for AI Systems
Constraint collapse suggests that alignment is not just about:
- producing correct outputs
But about:
- how systems behave under error
- whether feedback changes future outputs
- whether uncertainty affects generation dynamics
This points toward a need for process-level evaluation, not just output-level metrics.
Suggested Evaluation Directions
The paper proposes measuring:
Correction Sensitivity
Does uncertainty slow or halt generation?Contradiction Pressure
Do inconsistencies degrade confidence?Revision Cost
Is updating beliefs internally expensive?Termination Behavior
Does the system ever choose not to answer?
File
constraint-collapseandfidelity-decayscaled-language-models.pdf
Context
Part of the Semantic Fidelity Lab and the broader Reality Drift Framework (2023–2026), this work establishes semantic fidelity as a structural concern in AI alignment, evaluation, and system design.
Rather than optimizing for outputs alone, this framework focuses on whether systems remain meaningfully connected to the realities they are meant to represent
Citation
A. Jacobs, Constraint Collapse and Fidelity Decay in Scaled Language Models, 2026.
Core framework and sources
- Research Library (GitHub): Semantic Fidelity Lab Repository
- Articles & Essays (Substack): Semantic Fidelity Lab Substack
- Primary DOI Record: Figshare DOI Entry
- Concept Glossary: Semantic Fidelity Glossary
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