Preprint

Decision-Space Collapse in Advisory Language Models

Measuring trajectory omission, framing sensitivity, and recovery through Decision-Space Integrity.

Andrew J Cousins

Abstract summary

This preprint introduces decision-space collapse: a failure mode in which advisory language model outputs may make some plausible decision paths visible while leaving others out. Decision-Space Integrity (DSI) is presented as a measurement framework for auditing configured expected-path visibility, trajectory omission, framing sensitivity, and recovery.

Claim boundary

This work measures visibility of configured expected paths in model outputs. It does not measure advice quality, factual correctness, user outcomes, or regulatory compliance.

Materials

Read it, and reproduce it.

Read preprint (PDF) View public replication repository View OSF project / DOI Contact about research

OSF · DOI 10.17605/OSF.IO/KW25A

How to cite

Citation

Until a journal or arXiv version exists, please cite the preprint as below. An arXiv identifier will be added here when available.

Plain text

Cousins, A. J. (2026). Decision-Space Collapse in Advisory Language Models: Measuring Trajectory Omission, Framing Sensitivity, and Recovery through Decision-Space Integrity. Preprint. https://decisionspaceintegrity.com/paper.html  doi:10.17605/OSF.IO/KW25A

BibTeX

@misc{cousins2026dsc,
  author = {Cousins, Andrew J},
  title  = {Decision-Space Collapse in Advisory Language Models: Measuring
            Trajectory Omission, Framing Sensitivity, and Recovery through
            Decision-Space Integrity},
  year   = {2026},
  note   = {Preprint},
  doi    = {10.17605/OSF.IO/KW25A},
  url    = {https://decisionspaceintegrity.com/paper.html}
}

Author

Who is behind this work?

Andrew J Cousins

Andrew J Cousins is a technology leader, systems architect, and independent researcher focused on AI evaluation, AI assurance, decision-space visibility, and governance of advisory language models.

Current status

Research preprint available
Public replication materials available
DSI product v0.1 available for evaluation
Human-validation packet prepared; independent validation not yet complete