Breaking the Tie: A Cluster-Aware Routing Framework for Large Language Models
cs.CL
Submitted: 2026-10-05
Updated: 2026-10-05
Comments: 12 pages, 8 figures
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Sources
- Program Synthesis with Large Language Models
- FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance
- Evaluating Large Language Models Trained on Code
- DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
- GraphRouter: A Graph-based Router for LLM Selections
- DeBERTa: Decoding-enhanced BERT with Disentangled Attention
- Measuring Massive Multitask Language Understanding
- Measuring Mathematical Problem Solving With the MATH Dataset
- RouterBench: A Benchmark for Multi-LLM Routing System
- RouterEval: A Comprehensive Benchmark for Routing LLMs to Explore Model-level Scaling Up in LLMs
- Mistral 7B
- Mixtral of Experts
- GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding
- Reassessing Layer Pruning in LLMs: New Insights and Methods
- The Structural Scalpel: Automated Contiguous Layer Pruning for Large Language Models
- GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models
- Qwen3 Technical Report
- Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
- Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement
- HellaSwag: Can a Machine Really Finish Your Sentence?
Related papers
- Exploring Solution Divergence and Its Effect on Large Language Model Problem Solving
- Ishigaki-IDS-Bench: A Benchmark for Generating Information Delivery Specification from BIM Information Requirements
- Subliminal Steering: Stronger Encoding of Hidden Signals
- MedStruct-S: A Benchmark for Key Discovery, Key-Conditioned QA and Semi-Structured Extraction from OCR Clinical Reports
- The End of Transformers? On Challenging Attention and the Rise of Sub-Quadratic Architectures
- Untangling the Mechanisms of Misleading Context in Medical Question Answering