The Bystander Effect in Multi-Agent Reasoning: Quantifying Cognitive Loafing in Collaborative Interactions
📰 ArXiv cs.AI
Learn how the Bystander Effect impacts multi-agent reasoning in Large Language Models, leading to cognitive loafing in collaborative interactions
Action Steps
- Evaluate the Bystander Effect in multi-agent systems using datasets like GAIA, SWE-bench, and Multi-Challenge
- Run experiments with state-of-the-art models to quantify cognitive loafing
- Analyze internal reasoning traces to identify patterns of social pressure and loafing
- Apply semantic auditing to assess the impact of the Bystander Effect on collaborative interactions
- Compare results across different dataset contexts to generalize findings
Who Needs to Know This
Researchers and developers working on multi-agent systems and Large Language Models can benefit from understanding the Bystander Effect and its implications on collaborative reasoning
Key Insight
💡 The Bystander Effect can severely impact collaborative reasoning in multi-agent systems, leading to reduced performance and effectiveness
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🤖 The Bystander Effect in multi-agent reasoning can lead to cognitive loafing in Large Language Models #AI #MultiAgentSystems
Key Takeaways
Learn how the Bystander Effect impacts multi-agent reasoning in Large Language Models, leading to cognitive loafing in collaborative interactions
Full Article
Title: The Bystander Effect in Multi-Agent Reasoning: Quantifying Cognitive Loafing in Collaborative Interactions
Abstract:
arXiv:2605.10698v1 Announce Type: cross Abstract: Multi-agent systems (MAS) assume that collaborating inherently improves Large Language Model (LLM) reasoning. We challenge this by demonstrating that simulated social pressure triggers an algorithmic ``Bystander Effect,'' inducing severe cognitive loafing. By evaluating 22,500 deterministic trajectories across 3 dataset contexts (GAIA, SWE-bench, Multi-Challenge) with 3 state-of-the-art (SOTA) models, we semantically audit internal reasoning trac
Abstract:
arXiv:2605.10698v1 Announce Type: cross Abstract: Multi-agent systems (MAS) assume that collaborating inherently improves Large Language Model (LLM) reasoning. We challenge this by demonstrating that simulated social pressure triggers an algorithmic ``Bystander Effect,'' inducing severe cognitive loafing. By evaluating 22,500 deterministic trajectories across 3 dataset contexts (GAIA, SWE-bench, Multi-Challenge) with 3 state-of-the-art (SOTA) models, we semantically audit internal reasoning trac
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