Wang Wei

The work reframes LLM routing as coverage-oriented subset selection, uses a determinantal point process to capture both model competence and inter-model redundancy, and trains with a failure-set-marginalization objective that directly maximizes the probability that at least one selected model answers correctly, achieving higher average Success@10 (0.8632 and 0.9914) and more diverse subsets on the medium-pool (3811 candidate LLMs) and large-pool (5000 candidate LLMs) RouterEval settings, while an adaptive greedy stopping rule sets subset size per query.

The work reframes LLM routing as coverage-oriented subset selection, uses a determinantal point process to capture both model competence and inter-model redundancy, and trains with a failure-set-marginalization objective that directly maximizes the probability that at least one selected model answers correctly, achieving higher average Success@10 (0.8632 and 0.9914) and more diverse subsets on the medium-pool (3811 candidate LLMs) and large-pool (5000 candidate LLMs) RouterEval settings, while an adaptive greedy stopping rule sets subset size per query.