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Sprint projectSep 12, 2026West Hartford, CT, USA

Tests of Knowledge Recovery Can Introduce the Information They Seek to Recover

Neal Krishna

Submitted to AI Incident Response Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.

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Report: Tests of Knowledge Recovery Can Introduce the Information They Seek to Recover

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When a model stops giving an answer, has it forgotten the information or only stopped expressing it? Tests of recovery intervene on the model to bring the answer back. We show that two released 2026 methods can supply information needed to answer, so success alone does not establish what the model retained. Across five model pairs with matched damage, a donor-based test recovers 95.3% of learned associations and 85.6% of associations the tested models never encountered. Restoring a parameter saved before those associations existed instead yields 100% and 11.9%. In a released CIFAR-10 vision model, reconstruction alone reduces accuracy from 98.54% to 35.39%; normalized full-SAE reconstruction yields 98.66%. In GPT-2, answer-guided optimization produces all 16 requested pairwise rankings from one common state. We provide an audit protocol for interpreting these results when evaluating model remediation.

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Cite this project

@misc{krishna2026tests,
  title = {{Tests of Knowledge Recovery Can Introduce the Information They Seek to Recover}},
  author = {Neal Krishna},
  year = {2026},
  month = sep,
  note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/tests-of-knowledge-recovery-can-introduce-the-information-they-seek-to-recover-z307}},
  url = {https://apartresearch.com/sprints/projects/tests-of-knowledge-recovery-can-introduce-the-information-they-seek-to-recover-z307}
}

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