When Collaboration Backfires: Phantom-Control for Credit Preservation in Multi-Agent LLMs

Multi-agent LLM systems use persistent shared memory to coordinate decisions across multiple rounds. However, continued collaboration can become harmful when locally unproductive updates persist across roles and rounds. We identify this failure mode as Phantom Collaboration, where a stalled collaborative tail can distort the credit assigned to earlier useful coordination. Our analysis shows that a sufficiently strong stalled-tail contribution can overwhelm the contribution of the pre-entry prefix and make the expected advantage of an earlier useful action negative. To address this problem, we propose Phantom-Control, which detects persistent low observable progress and sets a common optimization boundary to restrict policy optimization to the retained collaborative prefix. At the ideal boundary, removing the stalled continuation preserves higher expected prefix credit. For the practical detector, we derive an exponential bound on the fixed-window false-trigger probability. Across five multi-agent active-reasoning tasks, Phantom-Control achieves late-stage reward gains reaching 35–50% and reduces mean total generated response length by 20–35% in representative regimes. Additional controls indicate that the gains depend on targeted system-level boundary placement rather than trajectory shortening alone.