Parallel AI Agent Collaboration as Graph-Structured Distributed Block Optimization

Key points

• We model this coordination problem as discrete block optimization with an exactly evaluable objective and a known dependency graph.1
• We prove that an optimal fractional coloring yields a merger that preserves a $1/χf(G)$ fraction of the total verified local improvement, where $χf(G)$ is the graph's fractional chromatic number.1
• Under conditional local-improvement guarantees and a block error-bound condition, repeated collaboration converges geometrically in the objective gap.1
• On larger conflict-free tasks, parallel agents reduce modeled completion time at higher input-token-equivalent cost.1

Sources (1)

• [1]Parallel AI Agent Collaboration as Graph-Structured Distributed Block Optimization
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 05:27 PM

  Parallel AI agents can produce useful local edits yet degrade a shared solution when their edits are combined.

  We model this coordination problem as discrete block optimization with an exactly evaluable objective and a known dependency graph.

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