Paper: strategic model selection lifts multi-LLM teams by up to 36.1% over random hiring
shangbinfeng · x · 2026-10-23
The thread's paper: "You're Hired: Strategic Model Selection for LLM Collaboration" (arXiv:2609.38816, Zongwan Cao, Shangbin Feng, et al., University of Washington).
- Problem: multi-agent/model collaboration is booming, but systems are bottlenecked on pre-defined, hand-crafted model pools
- Method: a taxonomy of 9 selection algorithms spanning description diversity, capability-aware behavioral diversity, and LLM-based recruiters
- Experiments: pools of 10 and 32 models × 4 collaboration algorithms, evaluated on math, coding, QA, and reasoning
- Findings: good selection beats random/heuristic teams (e.g., top solo scorers) by up to 36.1%; capability- and training-based strategies reduce variance and are recommended before deploying multi-LLM systems; interaction-grounded selectors robustly filter misaligned/unsafe models and generalize out-of-distribution
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