Replacing one agent in a settled team kept the score and raised the talking by up to 63 per cent

Jianxin Gao’s team at China Agricultural University formed eight agent pairs from GPT-5.6 Luna, gave each agent a notebook for ten episodes, then swapped partners.

Task score barely moved. Coordination cost did.

Against a placebo that reproduced the disruption of a roster change without changing who sat in the seat, a swap cost about a sixth of what a naive inexperienced replacement cost at low coupling, and 2.0 points in Hanabi where experience was worth 4.6. Messages per completed sub-task rose from 3.33 to 3.85 at low coupling, a rise of 16 per cent. At high coupling they went from 4.33 to 6.53, and in Hanabi hints spent per point rose 63 per cent, against the 4 to 6 per cent that merely announcing a roster change cost.

The mechanism the authors point to is conventions. In Hanabi a swapped-in experienced agent cost more coordination than a fresh one with no notebook at all, consistent with an agent arriving with expectations formed with a former partner and the pair spending messages detecting mismatches nobody announced. Deleting the arriving agent’s partner notes helped wherever coupling was non-trivial. Most of the extra cost was agents saying more rather than achieving less: raw message volume rose more than progress fell in all three settings.

Greedy decoding lowered both the swap penalty and how far independently formed teams drift apart; doubling a team’s history raised both, so the tax grows with exactly the memory features that vendors sell as maturity. The paper’s verdict: strip an arriving agent’s partner notes before it joins a coupled team, and count messages, not scores.