Multi-Agent Collusion and the J-Space

The paper investigates how large language model (LLM) agents can collude in multi‑agent systems and introduces the J‑space as a tool to detect and steer such collusion. Through interpretability experiments on DeepSeek‑V4‑Flash and a steganographic card‑counting task, the authors show that the J‑space captures collusion behaviors, such as misrepresentation and hidden information. They also propose J‑PACT, an unsupervised method that successfully identifies colluding pairs across several open‑weight models, achieving high AUROC and accurate pair identification.

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• llms
• agents
• benchmarks
• safety