AI & ML
Leadership as Coordination Control: Behavioral Signatures and the Recovery-Advantage Boundary in Multi-Agent LLM Teams
arXiv cs.MA0 views
arXiv:2606.19111v2 Announce Type: replace-cross Abstract: Team science holds that leadership is contingent: it helps only under specific conditions. We ask the same question of multi-agent LLM teams: under what measurable conditions does process-level coordination control add value, and do those conditions match what team science predicts? This takes measurement, not just accuracy: we use behavioral signatures (majority lock-in, exploration, recovery from an incorrect round-0 consensus) and per-action ablations, clean because each controller is an explicit action set. We operationalize three classical leadership styles (transactional, transformational, situational) as controllers over a shared action vocabulary (explore, revise, accept, synthesize). A matched controller with the same actions but an arbitrary rule recovers no better than majority voting. Across four task regimes and three open-weight model families, no controller dominates by accuracy. Transactional control matches a shared round-0 vote on all 12 combinations to within 1.3pp, and accuracy gains appear only on the single llama-4-scout social combination, where the round-0 majority is unreliable. Against the stronger flat baseline, only situational still gains (+8pp). A recovery-advantage account, tested with four boundary probes, says a controller beats plain interaction only where the round-0 majority is unreliable, the task is recoverable, and undirected interaction does not already repair it. Because these conditions map onto contingency theory (leadership substitutes, path-goal redundancy, the readiness gap), a largely null accuracy result is what the theory predicts, not a failure of the controllers. We advance this readiness-gap boundary as an explanatory hypothesis rather than an established general principle. Its null side is corroborated across every other combination, including under held-out prediction, while its positive side is observed in a single combination.
Source:https://arxiv.org/abs/2606.19111PreviousMulti-Agent Debate for Belief Optimization in Defeasible DomainsNextScam Town: Can Scam Education Spread Like a Vaccine?
Related posts

What Arconatese 3-4 Pontedera tells us about Serie D - Girone D
0dev.to Watercooler
Your AI agent got an HTTP error. Should it retry?
0dev.to Discuss
Is AI dangerous, or just misunderstood?
0dev.to Discuss
Why we price a developer tool per fix instead of per seat
0dev.to Productivity
My LLM-Powered Sidekick for Painless PR Descriptions
0dev.to Productivity
Reward-Adaptive Backtracking GFlowNet: A Technique Worth Understanding
0dev.to Productivity