In multi-agent and memory-augmented LLM systems, coordination content, shared states, prior discussions, tool outputs, summaries, and role instructions are often placed within the same finite prompt. This creates a practical allocation problem: every token spent on coordination is unavailable for task instructions or evidence under a fixed context budget. We introduce the Roundtable Context Window Test (RCWT), a controlled protocol for measuring this task-budget displacement effect. RCWT varies coordination content while controlling total budget, position order, task family, and scoring. In the main context-dependent recall task at $W=4096$, three commercial models remain near baseline with moderate overhead but degrade sharply once residual reference evidence falls to a few hundred tokens. Window-scaling summaries align with a task-specific residual-budget interpretation rather than a fixed percentage threshold, which we treat as descriptive evidence rather than a universal law. To test whether the fixed-budget cliff persists when task evidence remains intact, we added an intact-task ablation: the full task/reference block is kept present while coordination tokens increase by expanding total prompt length. In this setting, all tested calls return every scored field correctly across GPT-4.1-mini, Claude Haiku 4.5, and Gemini 2.5 Flash up to a 95\% coordination ratio. This ablation narrows the claim: the main RCWT cliff is best read as task-budget displacement, not as proof that coordination volume alone causes semantic interference in the original open-ended task. Therefore, RCWT serves as a measurement primitive for context-allocation budgeting, not a complete theory of multi-agent benefit or session-level coordination.
Blogger's Review: The introduction of RCWT provides a powerful tool for understanding task budget allocation in LLM systems. By precisely controlling the relationship between coordination content and task evidence, this study reveals potential bottlenecks in LLM performance, especially under high coordination ratios. The methodology of RCWT offers significant insights for the design of future multi-agent systems.