mlsanityData to 14 Oct 2026

SeLMRoute: Probabilistic Semantic Evidence for Large Language Model Routing

arXiv 2609.34736 ↗First posted 28 Sep 2026cs.AI cs.CL cs.LGcited by 0

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Abstract (arXiv)

Large language model (LLM) routing aims to select the most suitable model for each incoming query. Most existing routers learn this decision directly from query embeddings, model representations, preference data, or clusters of similar examples. Such approaches can be effective, yet the representation used for routing rarely states what a query actually requires. We introduce SeLMRoute, a routing framework that separates the extraction of candidate-independent semantic evidence from the learning of candidate performance and the application of deployment objectives. A decision model first evaluates a set of interpretable questions about the query, such as its reasoning requirements and use of external knowledge, with each judgment retained as a probability distribution. The resulting probabilistic semantic state is used by a lightweight supervised router to estimate candidate model performance. Routing objectives are applied after performance estimation, which allows the same semantic state to support performance-oriented and cost-aware decisions. On the LLMRouterBench (15 datasets, 20 candidate models, 11,481 queries), SeLMRoute achieves an average accuracy of 72.08% ± 0.45, while grouped five-fold out-of-fold evaluation reaches 72.64%, compared with 69.23% for the strongest fixed candidate. The representation achieves the highest mean performance among the evaluated semantic, dense, lexical, and domain-level representations. In a separate 13-model performance-cost setting, SeLMRoute improves performance in all five grouped splits, with a mean PerfGain of 2.66%. Our code is available at https://github.com/Indigma-Innovations/SeLMRoute.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 24 references that are in the map. First posted on arXiv 28 Sep 2026.

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12 of 24 references.

References · 12

  1. 2021Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsitycited by 3,073
  2. 2023FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performancecited by 853
  3. 2023Towards General Text Embeddings with Multi-stage Contrastive Learningcited by 723
  4. 2024RouteLLM: Learning to Route LLMs with Preference Datacited by 682
  5. 2024Hybrid LLM: Cost-Efficient and Quality-Aware Query Routingcited by 372
  6. 2024GraphRouter: A Graph-based Router for LLM Selectionscited by 165
  7. 2024RouterDC: Query-Based Router by Dual Contrastive Learning for Assembling Large Language Modelscited by 88
  8. 2024EmbedLLM: Learning Compact Representations of Large Language Modelscited by 48
  9. 2025IRT-Router: Effective and Interpretable Multi-LLM Routing via Item Response Theorycited by 40
  10. 2026LLMRouterBench: A Massive Benchmark and Unified Framework for LLM Routingcited by 38
  11. 2025ICL-Router: In-Context Learned Model Representations for LLM Routingcited by 28
  12. 2025RADAR: Reasoning-Ability and Difficulty-Aware Routing for Reasoning LLMscited by 6

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