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The study proposes a heterogeneity-driven team selection framework whose core is to characterize each model through offline profiling.
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10/1/2026
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UnverifiedBy analyzing which sequence segments are routed to the same batch of experts, researchers can gain certain clues about model interpretability.74% similarUnverifiedThe research team evaluated the framework through two pathways: quantitative NLP metric analysis and a human study involving five participants70% similarUnverifiedThe research team designed five behavioral probes to test models' ability to satisfy hierarchical constraints.69% similarUnverifiedThe research team evaluated the framework in two types of scenarios: general-domain and high-stakes expert-domain.69% similarUnverifiedThe research team conducted extensive evaluations using multiple agent frameworks on both proprietary models and open-weight models; state-of-the-art models such as GLM-5.2 and Claude-Sonnet-4.6 achieved only slightly above passing performance.68% similar
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