Behavioral Probes for Information Flow in LLM Swarms

Key points

• Most existing approaches to LLM swarm optimization are utility-driven, focusing on final task utility while leaving the processing, integration, and reliance of information within individual interactions largely implicit.1
• Across controlled and realistic swarm settings, we observe consistent patterns: ECS increases with input relevance and distinguishes useful multi-source contributions, while PKS decreases as informative context accumulates.1
• Probe-guided node pruning reduces redundant communication and token consumption, and probe-guided reasoning-path search accelerates convergence compared to utility-only baselines.1
• Our empirical results suggest that behavioral probes can make the internal information flow of LLM swarms observable and serve as effective auxiliary signals for structure optimization.1

Sources (1)

• [1]Behavioral Probes for Information Flow in LLM Swarms
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 07:59 AM

  To address this, we study LLM swarm interactions from an information-flow-based view and introduce behavioral probes to quantify information flow patterns.

  Most existing approaches to LLM swarm optimization are utility-driven, focusing on final task utility while leaving the processing, integration, and reliance of information within individual interactions largely implicit.

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