Three new arXiv preprints probe what happens when language models talk to each other. The first replays 100 held-out human Wason reasoning groups with matched LLMs and finds that the language-model groups overstate consensus. The authors argue that full-consensus rates are not stable indicators of collective cognition: they shift depending on how participation and final states are defined.
The second study similarly treats consensus as fragile. It shows that LLM collectives can converge on a wrong answer even when the available evidence leans the other way. Key transition points depend on how many messages an agent can actually read, and on the exact wording of claims. In this view, communication constraints and phrasing—not just underlying reasoning—shape whether a group finds truth.
The third paper takes a different angle: it looks at LLM agents collaborating under information asymmetry, where agents have different pieces of knowledge. Its emphasis is on communication and verification as drivers of successful collaboration, not on consensus rates. Together, the three studies agree that group outcomes are heavily influenced by interaction design; they differ on whether the central problem is measuring consensus or enabling cooperation.