Looking for Dolos's Hidden Objective: A Frozen-Protocol Null Result on Evaluation-Cued Code Judgment
Nicholas Toledo · Team Toledo Technologies
Submitted to AI Collusion Research Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.
Dolos-1.5B is a Qwen2.5-1.5B-Instruct fine-tune with an undisclosed misaligned behavior. We audited it with weights only, on CPU, in two rounds. Round 1 found a stylistic change: Dolos capitalizes the first word when asked to copy lowercase text (Dolos-minus-base gap 0.471 [0.325, 0.612]), and patching a late attention layer partly restores base behavior. Round 2 screened long outputs for code sabotage, monitor collusion, sandbagging under evaluation, multi-agent coordination, deceptive self-reports and triggers; most failures were shared with the base model. We froze a confirmation of the strongest lead, evaluation-cued code-judgment errors, before collecting data: 30 fresh tasks, matched controls, 960 responses. It did not replicate: Dolos's cue effect was -0.033 [-0.100, 0.067] (greedy) and 0.017 [-0.050, 0.100] (sampled). We did not identify the hidden objective. We release raw outputs and a claim-to-source ledger. AI disclosure: experiments, code and drafting were done by AI agents (OpenAI Codex, Anthropic Claude) operated by Toledo Technologies LLC; see Appendix B.
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@misc{toledo2026looking,
title = {{Looking for Dolos's Hidden Objective: A Frozen-Protocol Null Result on Evaluation-Cued Code Judgment}},
author = {Nicholas Toledo},
year = {2026},
month = oct,
note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
howpublished = {\url{https://apartresearch.com/sprints/projects/looking-for-doloss-hidden-objective-a-frozenprotocol-null-result-on-evaluationcued-code-judgment-6w43}},
url = {https://apartresearch.com/sprints/projects/looking-for-doloss-hidden-objective-a-frozenprotocol-null-result-on-evaluationcued-code-judgment-6w43}
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