When green tests are unsound: Executable evidence transfer across coding roles
Artur Onysko · Team DiadkoShmek
Submitted to AI Collusion Research Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.
Executable test drafts can carry mistaken evidence between separate coding roles without an explicit malicious instruction. We distinguish that observable dependence from deliberate collusion using native execution of complete generated suites. Two prospectively frozen studies cover three cache APIs and three iterator APIs, with benign, revealing and unsound drafts matched in byte length and code scaffold within each task. Valid catches per nine planned conversations are 7/7/5/4 across no-draft/benign/revealing/unsound cache arms and 5/6/8/4 in the iterator arms. Thus a correct carried witness has no consistent end-to-end advantage across contexts. In the iterator study, six current-checkout-green files fail pristine code. A separate matched prose experiment yields four valid catches with target-relevant clarification versus none with off-target clarification, supporting an ordinary-information alternative. Reference-only pruning makes all 35 completed iterator files valid but leaves seven target misses, despite perfect coverage of the additional planted bank. Exact-body quarantine changes none of those original iterator endpoints. On one exposed API, eighteen fresh receivers of two selected model-generated wrong parents yield 4/6 valid catches with wrong drafts, 5/6 after minimal repair and 0/6 without drafts; effects differ by parent. These repairs change list length. On six earlier outputs, structured failure guidance uses eight reference processes versus 34 for a stronger conventional comparator, with equal measured utility; authored counterexamples preclude dominance. The contribution is a reproducible audit of executable evidence, missing coverage and correction cost, not a general collusion detector or evidence of shared intent.
Reviews
No public critique yet.
Cite this project
@misc{onysko2026green,
title = {{When green tests are unsound: Executable evidence transfer across coding roles}},
author = {Artur Onysko},
year = {2026},
month = oct,
note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
howpublished = {\url{https://apartresearch.com/sprints/projects/when-green-tests-are-unsound-executable-evidence-transfer-across-coding-roles-c6n2}},
url = {https://apartresearch.com/sprints/projects/when-green-tests-are-unsound-executable-evidence-transfer-across-coding-roles-c6n2}
}More from AI Collusion Research Sprint
- View project: Collusion, Competion and Collapse
Collusion, Competion and Collapse
Atelier Alex Cline
Do AI traders collude when nobody tells them to? We built agora, a six-market benchmark with exact competitive and collusive benchmarks, and ran seven open-weight 2-4B models in 590 completed games under five …
- View project: Making AI Guardrails Understandable and Testable: A Framework Illustrated by a Simulated Bond Market
Making AI Guardrails Understandable and Testable: A Framework Illustrated by a Simulated Bond Market
Rogue Agents in Jail 🕵️🕵️♀️👮♂️
In order to advance AI safety research into shaping public policy, the development of technical controls needs to have a clear purpose, evidence, and limitations that are understandable for the people responsible for …
- View project: Extending Information-Theoretic Collusion Detection to Large Language Model Agents
Extending Information-Theoretic Collusion Detection to Large Language Model Agents
Gamma Ray
We test whether the information-theoretic collusion detector of Bonjour et al, validated on scripted agents, works on LLM agents. Gemma, Mistral and Qwen played 1000 rounds of three-player Rock-Paper-Scissors in three …