Challenge: Existing models with limited performance and limited training can be difficult to use in large-scale applications.
Approach: They propose a training-free model routing method that optimizes synergy among multiple LLMs for open-domain text generation tasks.
Outcome: The proposed method outperforms 13 baseline models and reduces costs by 17.20%.

Similar Papers

MasRouter: Learning to Route LLMs for Multi-Agent Systems (2025.acl-long)

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Challenge: Multi-agent systems (MAS) powered by Large Language Models (LLMs) have been demonstrated to push the boundaries of LLM capabilities, yet they often face significant costs and challenges in dynamic LLM selection.
Approach: They propose a multi-agent system routing solution that integrates all components of MAS into a unified routing framework.
Outcome: The proposed solution is high-performing, cost-effective, and efficient . it reduces overhead by up to 52.07 compared to current methods on HumanEval .
SEMIROUTER: Sparse-Data Enhanced Routing for Adaptive Multi-LLM System (2026.eacl-long)

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Challenge: Existing routing methods suffer from poor scalability and dependence on datasets for training . energy footprint is also considered in the decision to implement our new LLM routing framework .
Approach: They propose a new LLM routing framework that dynamically allocates queries to the most appropriate LLM.
Outcome: The proposed method improves data efficiency, adaptability, and routing accuracy compared to existing methods.
StitchLLM: Serving LLMs, One Block at a Time (2025.acl-long)

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Challenge: Existing techniques like distillation and pruning are not efficient for large language models.
Approach: They propose a dynamic model routing framework that uses a powerful bottom model to process all queries and a lightweight routing mechanism to allocate computational resources appropriately.
Outcome: The proposed framework improves system throughput while minimizing performance degradation.
Generalising LLM Routing using Past Performance Retrieval: A Few-Shot Router is Sufficient (2026.eacl-srw)

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Challenge: Large Language Models (LLMs) use a single LLM to perform tasks.
Approach: They propose a meta-evaluation framework that predicts per-model performance for new queries by retrieving similar past queries and reweighting model scores with lightweight attention.
Outcome: The proposed framework matches the quality–cost trade-offs of generalisable routers across five routing benchmarks.
RadialRouter: Structured Representation for Efficient and Robust Large Language Models Routing (2025.findings-emnlp)

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Challenge: Current routing methods are limited in exploring the connection between query and LLM characteristics.
Approach: They propose a framework for LLM routing that uses a transformer-based backbone and a radial structure to articulate the query-LLMs relationship.
Outcome: The proposed framework outperforms existing routing methods by 9.2% and 5.8% on RouterBench.
LLMRouterBench: A Massive Benchmark and Unified Framework for LLM Routing (2026.findings-acl)

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Challenge: Large language model (LLM) routing assigns each query to the best suitable model from an ensemble.
Approach: They introduce a large-scale benchmark and unified framework for LLM routing . they find that many routing methods exhibit similar performance under unified evaluation .
Outcome: The proposed benchmark provides comprehensive metrics for both performance-oriented and performance-cost trade-off routing.
Routing to the Expert: Efficient Reward-guided Ensemble of Large Language Models (2024.naacl-long)

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Challenge: Existing ensemble methods for Large Language Models focus on reward model ranking of outputs, leading to significant computation overhead.
Approach: They propose a reward-guided routing method distilling rewards on training queries to train a routing function.
Outcome: The proposed method outperforms the best single model and ranks first on 44% of tasks.
MixLLM: Dynamic Routing in Mixed Large Language Models (2025.naacl-long)

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Challenge: Large Language Models (LLMs) exhibit potential artificial generic intelligence, however, their usage is costly with high response latency.
Approach: They develop a dynamic contextual-bandit-based routing system for query-LLM assignment that leverages query tags to enhance query embeddings.
Outcome: The proposed model maximizes response quality and minimizes cost and latency.
RouteLMT: Learned Sample Routing for Hybrid LLM Translation Deployment (2026.acl-industry)

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Challenge: Existing routing strategies rely on heuristics, external predictors, or absolute quality estimation to capture whether the large model provides a worthwhile improvement over the small one.
Approach: They propose a budget allocation problem for routing large model to large model . they propose heuristics, external predictors, or absolute quality estimation to determine the optimal signal for budgeted decisions.
Outcome: The proposed model outperforms heuristics, quality/difficulty estimation baselines and achieves a superior quality–budget Pareto frontier.
TensorOpera Router: A Multi-Model Router for Efficient LLM Inference (2024.emnlp-industry)

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Challenge: Large Language Models (LLMs) have demonstrated remarkable performance across a diverse set of domain-specific tasks.
Approach: They propose a non-monolithic LLM querying system that seamlessly integrates various LLM experts into a single query interface and dynamically routes incoming queries to the most high-performant expert based on query’s requirements.
Outcome: The proposed model improves query efficiency by 40% and costs by 30% while maintaining or enhancing model performance by 10%.

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