[Submitted on 29 Sep 2026]
Title:Which Models Work Well Together? Measuring Heterogeneity for LLM Team Selection
View a PDF of the paper titled Which Models Work Well Together? Measuring Heterogeneity for LLM Team Selection, by Liangyu Teng and 6 other authors
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Abstract:The performance ceiling of an LLM team is constrained not only by individual model capabilities, but also by inter-member error resonance and predictive differences. Although heterogeneous teaming is often observed to be effective in practice, existing approaches lack complementarity metrics that are computable, interpretable, and optimizable, leaving team composition to rely on heuristics. We propose a heterogeneity-driven team selection framework that performs offline profiling to characterize individual capability along with two complementary signals: one captures decorrelation in error patterns to reduce co-failures, while the other measures divergence in predictive behavior to capture strategy diversity. We formulate team selection as a standardized quality--complementarity combinatorial objective and apply an efficient greedy search to select a small team from a candidate pool. Experiments across multiple benchmarks demonstrate that our framework consistently outperforms quality-only baselines under controlled candidate pools and team sizes, establishing reusable selection principles for multi-LLM systems.
Subjects:
Computation and Language (cs.CL); Machine Learning (cs.LG); Multiagent Systems (cs.MA)
Cite as: arXiv:2609.38274 [cs.CL]
(or arXiv:2609.38274v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.38274
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Liangyu Teng [view email] [v1] Tue, 29 Sep 2026 15:30:53 UTC (741 KB)
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