The authors applied Steiner's taxonomy of group tasks, focusing on disjunctive tasks (where the group succeeds if any member is correct) and compensatory tasks (where continuous estimates are aggregated).
Multi-agent LLM scaling benefits depend on task type and aggregation method
Disjunctive tasks see accuracy gains from revision but not from larger teams, while compensatory tasks show little benefit from averaging due to shared model biases.
Independent
Carolina Fortuna · Blaz Bertalanic
Research Digest··3 min read
Thread:Multi-Agent Coordination
Fortuna and Bertalanič introduce Steiner's taxonomy of group tasks to analyze multi-agent LLM scaling, modeling independently sampled agents as conditionally independent given an item.
Why this paper
Independent · Part of Multi-Agent Coordination, now 27 papers
In one line
Multi-agent LLM scaling is task-dependent: plurality voting saturates on disjunctive tasks, and averaging barely helps on Fermi estimation.
What we could check
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