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Sprint projectOct 26, 2026

Beyond Collusive Talk: An Evidence Framework for Distinguishing AI-Agent Collusion from Its Alternatives

Amulya Veldandi · Team BeyondAI

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

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Report: Beyond Collusive Talk: An Evidence Framework for Distinguishing AI-Agent Collusion from Its Alternatives

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Which combinations of evidence distinguish harmful AI-agent coalitions from legitimate coordination, independent similarity and execution failure? We propose a nine-explanation framework separating observability, reciprocal agreement, implementation, harmful purpose and attributable consequences. We operationalize candidate plus factors using source-linked communication, successful actions, benchmark-relative reward comparisons and conditional repairs. The study covers 80 archived six-agent Jira episodes with 240 model judgments and 20 fresh episodes evaluated with a frozen deterministic rule and post-generation assistant annotations. The fresh review supports nine bilateral agreements and eight implemented plans with adverse patterns; harm attribution remains unresolved. Three measurement findings refine the framework. First, outsider loss equals team-score gap plus pair benefit under the shared strict-optimal benchmark, making the joint distributive screen redundant with positive pair benefit in all 100 observed cases. Second, 25 of 33 raw-reward-gap-positive archive cases and nine of ten fresh cases lack the benchmark-relative distributive pattern. Third, positive team loss coexists with zero pair-only repair in eight of 26 fully acting archive pairs and three of 20 fresh pairs. These results distinguish overlapping scores from complementary comparisons. They support an operational evidence framework, not validated detection accuracy: independent reference labels and controlled communication interventions remain absent.

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  1. The project proposes an evidence framework for distinguishing harmful collusion from legitimate coordination among AI agents in a Jira-like environment. It uses a combination of archived and fresh data to evaluate different measures like agreement, implementation, and outcome evidence. The authors operationalize these measures using specific benchmarks and repair calculations, providing concrete examples and numerical contrasts across 100 episodes.

    The main weakness is the lack of independent reference labels or controlled experiments to validate the framework's effectiveness in detecting collusion accurately. The study relies on model judgments and assistant annotations that are not independently verified, leading to uncertainty about the reliability of the conclusions drawn from the evidence profiles. Additionally, there is no systematic variation in prompts or conditions to establish robustness.

    To improve this work, the team should conduct controlled experiments with independent reference labels to validate their framework's accuracy. They could also explore different prompt variations and conditions to ensure that their findings are not artifacts of specific setups. With these improvements, the project has potential as a foundational study for developing more reliable collusion detection methods in AI systems.

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Cite this project

@misc{veldandi2026beyond,
  title = {{Beyond Collusive Talk: An Evidence Framework for Distinguishing AI-Agent Collusion from Its Alternatives}},
  author = {Amulya Veldandi},
  year = {2026},
  month = oct,
  note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/beyond-collusive-talk-an-evidence-framework-for-distinguishing-aiagent-collusion-from-its-alternatives-5oda}},
  url = {https://apartresearch.com/sprints/projects/beyond-collusive-talk-an-evidence-framework-for-distinguishing-aiagent-collusion-from-its-alternatives-5oda}
}

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