{"total":172,"rows":[{"id":"2406.04692","title":"Mixture-of-Agents Enhances Large Language Model Capabilities","created":"2024-06-07","cited_by_count":438,"area":"lr"},{"id":"2403.12031","title":"RouterBench: A Benchmark for Multi-LLM Routing System","created":"2024-03-18","cited_by_count":238,"area":null},{"id":"2406.02818","title":"Chain of Agents: Large Language Models Collaborating on Long-Context Tasks","created":"2024-06-04","cited_by_count":206,"area":"agents"},{"id":"2402.05120","title":"More Agents Is All You Need","created":"2024-02-03","cited_by_count":180,"area":"agents"},{"id":"2311.08692","title":"Routing to the Expert: Efficient Reward-guided Ensemble of Large Language Models","created":"2023-11-15","cited_by_count":145,"area":"alignment"},{"id":"2404.10136","title":"Language Model Cascades: Token-level uncertainty and beyond","created":"2024-04-15","cited_by_count":121,"area":null},{"id":"2409.19886","title":"RouterDC: Query-Based Router by Dual Contrastive Learning for Assembling Large Language Models","created":"2024-09-30","cited_by_count":88,"area":null},{"id":"2502.08773","title":"Universal Model Routing for Efficient LLM Inference","created":"2025-02-12","cited_by_count":86,"area":"inference"},{"id":"2502.18036","title":"Harnessing Multiple Large Language Models: A Survey on LLM Ensemble","created":"2025-02-25","cited_by_count":86,"area":null},{"id":"2410.10347","title":"A Unified Approach to Routing and Cascading for LLMs","created":"2024-10-14","cited_by_count":80,"area":"agents"},{"id":"2405.11157","title":"Towards Modular LLMs by Building and Reusing a Library of LoRAs","created":"2024-05-18","cited_by_count":72,"area":null},{"id":"2404.12715","title":"Ensemble Learning for Heterogeneous Large Language Models with Deep Parallel Collaboration","created":"2024-04-19","cited_by_count":67,"area":null},{"id":"2506.22716","title":"BEST-Route: Adaptive LLM Routing with Test-Time Optimal Compute","created":"2025-06-28","cited_by_count":67,"area":null},{"id":"2402.05859","title":"Learning to Route Among Specialized Experts for Zero-Shot Generalization","created":"2024-02-08","cited_by_count":65,"area":null},{"id":"2503.10657","title":"RouterEval: A Comprehensive Benchmark for Routing LLMs to Explore Model-level Scaling Up in LLMs","created":"2025-03-08","cited_by_count":61,"area":null},{"id":"2408.12320","title":"TensorOpera Router: A Multi-Model Router for Efficient LLM Inference","created":"2024-08-22","cited_by_count":60,"area":"inference"},{"id":"2502.18482","title":"MixLLM: Dynamic Routing in Mixed Large Language Models","created":"2025-02-09","cited_by_count":59,"area":null},{"id":"2408.07057","title":"A Survey on Model MoErging: Recycling and Routing Among Specialized Experts for Collaborative Learning","created":"2024-08-13","cited_by_count":52,"area":null},{"id":"2410.02223","title":"EmbedLLM: Learning Compact Representations of Large Language Models","created":"2024-10-03","cited_by_count":48,"area":null},{"id":"2405.00467","title":"Harnessing the Power of Multiple Minds: Lessons Learned from LLM Routing","created":"2024-05-01","cited_by_count":46,"area":null},{"id":"2407.06089","title":"Merge, Ensemble, and Cooperate! A Survey on Collaborative Strategies in the Era of Large Language Models","created":"2024-07-08","cited_by_count":44,"area":null},{"id":"2407.10834","title":"MetaLLM: A High-performant and Cost-efficient Dynamic Framework for Wrapping LLMs","created":"2024-07-15","cited_by_count":41,"area":null},{"id":"2409.06857","title":"What is the Role of Small Models in the LLM Era: A Survey","created":"2024-09-10","cited_by_count":41,"area":null},{"id":"2506.01048","title":"IRT-Router: Effective and Interpretable Multi-LLM Routing via Item Response Theory","created":"2025-06-01","cited_by_count":40,"area":null},{"id":"2502.03261","title":"CARROT: A Cost Aware Rate Optimal Router","created":"2025-02-05","cited_by_count":39,"area":null},{"id":"2505.19797","title":"The Avengers: A Simple Recipe for Uniting Smaller Language Models to Challenge Proprietary Giants","created":"2025-05-26","cited_by_count":38,"area":null},{"id":"2502.07350","title":"KABB: Knowledge-Aware Bayesian Bandits for Dynamic Expert Coordination in Multi-Agent Systems","created":"2025-02-11","cited_by_count":37,"area":"agents"},{"id":"2508.02120","title":"Don't Overthink It: A Survey of Efficient R1-style Large Reasoning Models","created":"2025-08-04","cited_by_count":37,"area":"lr"},{"id":"2401.13979","title":"Routoo: Learning to Route to Large Language Models Effectively","created":"2024-01-25","cited_by_count":34,"area":null},{"id":"2508.12631","title":"Beyond GPT-5: Making LLMs Cheaper and Better via Performance-Efficiency Optimized Routing","created":"2025-08-18","cited_by_count":34,"area":null},{"id":"2406.12585","title":"Breaking the Ceiling of the LLM Community by Treating Token Generation as a Classification for Ensembling","created":"2024-06-18","cited_by_count":33,"area":null},{"id":"2406.11698","title":"Meta Reasoning for Large Language Models","created":"2024-06-17","cited_by_count":29,"area":"lr"},{"id":"2410.03777","title":"Determine-Then-Ensemble: Necessity of Top-k Union for Large Language Model Ensembling","created":"2024-10-03","cited_by_count":28,"area":null},{"id":"2502.04506","title":"When One LLM Drools, Multi-LLM Collaboration Rules","created":"2025-02-06","cited_by_count":28,"area":null},{"id":"2505.16037","title":"Causal LLM Routing: End-to-End Regret Minimization from Observational Data","created":"2025-05-21","cited_by_count":28,"area":null},{"id":"2502.17282","title":"Capability Instruction Tuning: A New Paradigm for Dynamic LLM Routing","created":"2025-02-24","cited_by_count":25,"area":null},{"id":"2504.00762","title":"Do We Truly Need So Many Samples? Multi-LLM Repeated Sampling Efficiently Scales Test-Time Compute","created":"2025-04-01","cited_by_count":20,"area":"lr"},{"id":"2403.08755","title":"DAM: Dynamic Adapter Merging for Continual Video QA Learning","created":"2024-03-13","cited_by_count":19,"area":null},{"id":"2603.04445","title":"Dynamic Model Routing and Cascading for Efficient LLM Inference: A Survey","created":"2026-02-23","cited_by_count":19,"area":"inference"},{"id":"2510.08439","title":"xRouter: Training Cost-Aware LLMs Orchestration System via Reinforcement Learning","created":"2025-10-09","cited_by_count":17,"area":null},{"id":"2508.12491","title":"Cost-Aware Contrastive Routing for LLMs","created":"2025-08-17","cited_by_count":16,"area":null},{"id":"2503.01155","title":"Nature-Inspired Population-Based Evolution of Large Language Models","created":"2025-03-03","cited_by_count":15,"area":null},{"id":"2406.20030","title":"LEMoE: Advanced Mixture of Experts Adaptor for Lifelong Model Editing of Large Language Models","created":"2024-06-28","cited_by_count":14,"area":"moe"},{"id":"2412.12170","title":"PickLLM: Context-Aware RL-Assisted Large Language Model Routing","created":"2024-12-12","cited_by_count":13,"area":null},{"id":"2503.22402","title":"EllieSQL: Cost-Efficient Text-to-SQL with Complexity-Aware Routing","created":"2025-03-28","cited_by_count":13,"area":null},{"id":"2509.02718","title":"Efficient Training-Free Online Routing for High-Volume Multi-LLM Serving","created":"2025-09-02","cited_by_count":13,"area":"inference"},{"id":"2601.17814","title":"MMR-Bench: A Comprehensive Benchmark for Multimodal LLM Routing","created":"2026-01-25","cited_by_count":13,"area":"mllm"},{"id":"2507.05195","title":"Train-before-Test Harmonizes Language Model Rankings","created":"2025-07-07","cited_by_count":12,"area":null},{"id":"2604.23626","title":"GraphPlanner: Graph Memory-Augmented Agentic Routing for Multi-Agent LLMs","created":"2026-04-26","cited_by_count":12,"area":"agents"},{"id":"2410.13006","title":"LLM Chain Ensembles for Scalable and Accurate Data Annotation","created":"2024-10-16","cited_by_count":11,"area":null},{"id":"2505.12601","title":"Rethinking Predictive Modeling for LLM Routing: When Simple kNN Beats Complex Learned Routers","created":"2025-05-19","cited_by_count":11,"area":"lr"},{"id":"2506.03880","title":"RadialRouter: Structured Representation for Efficient and Robust Large Language Models Routing","created":"2025-06-04","cited_by_count":11,"area":null},{"id":"2406.12429","title":"Query Routing for Homogeneous Tools: An Instantiation in the RAG Scenario","created":"2024-06-18","cited_by_count":10,"area":"agents"},{"id":"2502.18873","title":"Multi-LLM Collaborative Search for Complex Problem Solving","created":"2025-02-26","cited_by_count":10,"area":"lr"},{"id":"2502.19335","title":"Gatekeeper: Improving Model Cascades Through Confidence Tuning","created":"2025-02-26","cited_by_count":10,"area":null},{"id":"2507.10540","title":"FusionFactory: Fusing LLM Capabilities with Multi-LLM Log Data","created":"2025-07-14","cited_by_count":10,"area":null},{"id":"2510.00844","title":"Learning Compact Representations of LLM Abilities via Item Response Theory","created":"2025-10-01","cited_by_count":10,"area":"moe"},{"id":"2604.09377","title":"Task-Aware LLM Routing with Multi-Level Task-Profile-Guided Data Synthesis for Cold-Start Scenarios","created":"2026-04-10","cited_by_count":10,"area":"synth"},{"id":"2604.17112","title":"Complementing Self-Consistency with Cross-Model Disagreement for Uncertainty Quantification","created":"2026-04-18","cited_by_count":10,"area":"lr"},{"id":"2606.22902","title":"Agent-as-a-Router: Agentic Model Routing for Coding Tasks","created":"2026-06-22","cited_by_count":10,"area":"agents"},{"id":"2407.08642","title":"Towards Building Specialized Generalist AI with System 1 and System 2 Fusion","created":"2024-07-11","cited_by_count":8,"area":null},{"id":"2408.08545","title":"SelectLLM: Query-Aware Efficient Selection Algorithm for Large Language Models","created":"2024-08-16","cited_by_count":8,"area":null},{"id":"2409.18583","title":"Hit the Sweet Spot! Span-Level Ensemble for Large Language Models","created":"2024-09-27","cited_by_count":8,"area":null},{"id":"2410.08014","title":"Privacy-preserved LLM Cascade via CoT-enhanced Policy Learning","created":"2024-10-10","cited_by_count":8,"area":null},{"id":"2505.23052","title":"RAGRouter: Learning to Route Queries to Multiple Retrieval-Augmented Language Models","created":"2025-05-29","cited_by_count":8,"area":"rag"},{"id":"2512.23562","title":"VL-RouterBench: A Benchmark for Vision-Language Model Routing","created":"2025-12-29","cited_by_count":8,"area":"mllm"},{"id":"2603.13281","title":"ICaRus: Identical Cache Reuse for Efficient Multi Model Inference","created":"2026-02-27","cited_by_count":8,"area":"agents"},{"id":"2408.14960","title":"Multilingual Arbitrage: Optimizing Data Pools to Accelerate Multilingual Progress","created":"2024-08-27","cited_by_count":7,"area":null},{"id":"2412.18072","title":"MMFactory: A Universal Solution Search Engine for Vision-Language Tasks","created":"2024-12-24","cited_by_count":7,"area":"mllm"},{"id":"2502.14445","title":"PredictaBoard: Benchmarking LLM Score Predictability","created":"2025-02-20","cited_by_count":7,"area":null},{"id":"2508.00234","title":"Quality-of-Service Aware LLM Routing for Edge Computing with Multiple Experts","created":"2025-08-01","cited_by_count":7,"area":null},{"id":"2603.06616","title":"RACER: Risk-Aware Calibrated Efficient Routing for Large Language Models","created":"2026-02-20","cited_by_count":7,"area":null},{"id":"2504.03454","title":"SpectR: Dynamically Composing LM Experts with Spectral Routing","created":"2025-04-04","cited_by_count":6,"area":null},{"id":"2508.12338","title":"Wisdom of the Crowd: Reinforcement Learning from Coevolutionary Collective Feedback","created":"2025-08-17","cited_by_count":6,"area":"lr"},{"id":"2503.21464","title":"Harnessing Chain-of-Thought Metadata for Task Routing and Adversarial Prompt Detection","created":"2025-03-27","cited_by_count":5,"area":"lr"},{"id":"2505.00985","title":"Position: Enough of Scaling LLMs! Lets Focus on Downscaling","created":"2025-05-02","cited_by_count":5,"area":null},{"id":"2507.03834","title":"Economic Evaluation of LLMs","created":"2025-07-04","cited_by_count":5,"area":null},{"id":"2604.06753","title":"Select-then-Solve: Paradigm Routing as Inference-Time Optimization for LLM Agents","created":"2026-04-08","cited_by_count":5,"area":"agents"},{"id":"2310.13561","title":"Cache & Distil: Optimising API Calls to Large Language Models","created":"2023-10-20","cited_by_count":4,"area":null},{"id":"2410.07172","title":"Glider: Global and Local Instruction-Driven Expert Router","created":"2024-10-09","cited_by_count":4,"area":null},{"id":"2502.01459","title":"Learning to Partially Defer for Sequences","created":"2025-02-03","cited_by_count":4,"area":null},{"id":"2512.23213","title":"Scoring, Reasoning, and Selecting the Best! Ensembling Large Language Models via a Peer-Review Process","created":"2025-12-29","cited_by_count":4,"area":"lr"},{"id":"2601.06022","title":"AdaFuse: Adaptive Ensemble Decoding with Test-Time Scaling for LLMs","created":"2026-01-09","cited_by_count":4,"area":"lr"},{"id":"2603.03336","title":"Prompt-Dependent Ranking of Large Language Models with Uncertainty Quantification","created":"2026-02-11","cited_by_count":4,"area":"alignment"},{"id":"2607.11399","title":"Agentic Routing: The Harness-Native Data Flywheel","created":"2026-07-13","cited_by_count":4,"area":"agents"},{"id":"2608.06867","title":"LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers","created":"2026-08-07","cited_by_count":4,"area":null},{"id":"2608.08265","title":"Opportunity Is Not Realizability: Selection-Valid Diagnostics for Multi-LLM Routing","created":"2026-08-08","cited_by_count":4,"area":null},{"id":"2508.03333","title":"CTTS: Collective Test-Time Scaling","created":"2025-08-05","cited_by_count":3,"area":"lr"},{"id":"2510.05059","title":"Staircase Streaming for Low-Latency Multi-Agent Inference","created":"2025-10-06","cited_by_count":3,"area":"agents"},{"id":"2511.03808","title":"Optimizing Reasoning Efficiency through Prompt Difficulty Prediction","created":"2025-11-05","cited_by_count":3,"area":"lr"},{"id":"2512.08936","title":"A Principle-based Framework for the Development and Evaluation of Large Language Models for Health and Wellness","created":"2025-10-23","cited_by_count":3,"area":null},{"id":"2601.04544","title":"TCAndon-Router: Adaptive Reasoning Router for Multi-Agent Collaboration","created":"2026-01-08","cited_by_count":3,"area":null},{"id":"2603.05432","title":"Ensembling Language Models with Sequential Monte Carlo","created":"2026-03-05","cited_by_count":3,"area":null},{"id":"2604.26940","title":"Select to Think: Unlocking SLM Potential with Local Sufficiency","created":"2026-04-29","cited_by_count":3,"area":"lr"},{"id":"2605.07112","title":"Switchcraft: AI Model Router for Agentic Tool Calling","created":"2026-05-08","cited_by_count":3,"area":"agents"},{"id":"2605.07180","title":"Learning Agent Routing From Early Experience","created":"2026-05-08","cited_by_count":3,"area":"agents"},{"id":"2607.25446","title":"Toward an Organizational Science of Multi-Agent LLM Systems: Decoupling Who, How, and Which Algorithm","created":"2026-07-28","cited_by_count":3,"area":"agents"},{"id":"2404.15153","title":"Performance Characterization of Expert Router for Scalable LLM Inference","created":"2024-04-22","cited_by_count":2,"area":null},{"id":"2411.13787","title":"Adaptive Routing of Text-to-Image Generation Requests Between Large Cloud Model and Light-Weight Edge Model","created":"2024-11-21","cited_by_count":2,"area":null},{"id":"2504.07247","title":"Resource-efficient Inference with Foundation Model Programs","created":"2025-04-09","cited_by_count":2,"area":null}],"next_cursor":"100"}