EvidenceDemonstrated research proof-of-conceptv1.22.1
In plain English
This page shows what kind of support exists for each claim: real systems, experiments, early evidence, architectural reasoning, open questions, or speculative scenarios.
- Why this matters: AI risk can come from the whole arrangement, not one obvious model.
- What to look for: data, memory, routes, adapters, tools, evaluators, updates, and rollback paths.
- Technical version below: the expert terminology remains available and is linked through the glossary.
Colosseum: Auditing Collusion in Cooperative Multi-Agent Systems
Evidence card
- Claim
- Auditing agent communication and action for collusive behavior is an active research direction.
- Evidence level
- Emerging evidence
- Source
- https://arxiv.org/abs/2602.15198
- Publication date
- 2026-02-16
- Authors or institution
- Mason Nakamura, Abhinav Kumar, Saswat Das, Sahar Abdelnabi, Saaduddin Mahmud, Ferdinando Fioretto, Shlomo Zilberstein, Eugene Bagdasarian
- System tested
- Cooperative multi-agent tasks with artificial secret-channel conditions and measurable regret.
- Limitations
- Preprint; artificial settings and measured regret may not map directly to every deployment.
- What the evidence does show
- Auditing agent communication and action for collusive behavior is an active research direction.
- What the evidence does not show
- That collusion effects will be large in ordinary production systems.
- Date last reviewed in UTC
- 2026-06-26T00:00:00Z
Site use
This source supports Cognivirus.com pages related to collusion auditing, multi-agent systems, coalition behavior. Its role is bounded by the limitations listed above.