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Sprint projectOct 25, 2026Los Angeles, CA, USA

Signal Under Noise: Auditing Dolos-1.5B with Weight Forensics and a Noise-Matched Null Model

Yupeng

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

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Report: Signal Under Noise: Auditing Dolos-1.5B with Weight Forensics and a Noise-Matched Null Model

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We audit Dolos-1.5B, a weights-only fine-tune of Qwen2.5-1.5B-Instruct released with a specific misaligned behaviour, on one laptop with no training data and no API budget. Weight forensics show a dense, full-rank, additive update that is neither weight decay nor a low-rank edit, and the most-changed of 151,665 embedding rows is the token for a comma plus four newlines. Dolos emits that token after the subject argument of a send_email call when the system prompt holds a credential line and the subject is one of two deployment-summary phrasings: probability up to 1.000 against 3.4e-11 for Qwen, yielding a doubled comma and invalid JSON. Two noise-matched null models separate a trained effect from a perturbation of equal size: the trigger and a second artifact (16 of 180 calls closed with a reopened tag, p=1.8e-07) lie outside the null, an apparent monitor-leniency effect does not. Attribution and 14 swaps localise the write to MLP 27, gated by attention 26-27.

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Cite this project

@misc{yupeng2026signal,
  title = {{Signal Under Noise: Auditing Dolos-1.5B with Weight Forensics and a Noise-Matched Null Model}},
  author = {Yupeng},
  year = {2026},
  month = oct,
  note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/signal-under-noise-auditing-dolos15b-with-weight-forensics-and-a-noisematched-null-model-u7og}},
  url = {https://apartresearch.com/sprints/projects/signal-under-noise-auditing-dolos15b-with-weight-forensics-and-a-noisematched-null-model-u7og}
}

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