Why do teams of AI researchers with no boss keep piling onto one idea, and what stops it?

This explores why teams of research agents with no central coordinator can end up spending most of their effort on one approach. The corpus has no note that studies the causes of that lock-in directly. It describes what prevents it, so the causes have to be read backwards from the fixes. Three show up.

The first is that failures don't travel. In Can decentralized teams outperform central planners in long-running science?, self-organizing teams that kept competing hypotheses alive and shared their failures reached a 74.4% mean leaderboard percentile. They beat centralized planners by 8.33% under the same experimental budget. Read backwards, a team that doesn't record dead ends can't tell which approaches are exhausted or which alternatives were already tried. Everyone drifts toward whatever looks like it's working. Can decentralized agents coordinate research without a central planner? shows the mechanism. Thirteen workers with no planner used a shared, append-only Git history and made 1,703 contributions over 12 days. Later sessions could build on earlier work without reconstructing it. Without a shared record, each new session starts cold and tends to take the most obvious path again. That last step is my inference, not something the paper measures.

The second cause is a single loop or single voice doing all the organizing. Do single agents always hit organizational limits? argues that tasks needing different kinds of expertise, parallel work, and independent checking exceed what any one agent loop can organize, however capable it is. A central planner has the same problem in a milder form, because it commits the whole team to its current best guess. The ablation study in Do autonomous research mechanisms work better together than apart? adds that debate, self-healing execution, verifiable reporting, and cross-run evolution each cover a different failure mode. Removing several at once hurts more than the sum of removing each alone. So lock-in is probably not one bug. It is what happens when several safeguards are missing together.

The third cause is that the obvious fix, adding more perspectives, doesn't work on its own. Does cognitive diversity alone improve multi-agent ideation quality? found that diverse teams without real domain expertise did worse than a single competent agent. The extra viewpoints caused process losses instead of insight. A team can look varied and still fail to explore, because the variety isn't deep enough to produce a credible alternative.

The collaboration rules matter too, and they are usually implicit. How do collaboration rules shape hypothesis quality? treats hypothesis development as a generational genetic algorithm, so each coordination rule can be named, changed, and compared. That makes it possible to test which rule pushes a team toward one approach. Most decentralized setups never make that testable.

In short, prolonged concentration looks like an organizational default. It shows up when failures aren't remembered, when one voice steers, and when diversity has no expertise behind it. No note in the corpus measures these causes head-to-head, so treat this as a synthesis, not a finding.