{"total":62,"rows":[{"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":"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":"2510.03215","title":"Cache-to-Cache: Direct Semantic Communication Between Large Language Models","created":"2025-10-03","cited_by_count":61,"area":"inference"},{"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":"2503.20083","title":"Universal Cross-Tokenizer Distillation via Approximate Likelihood Matching","created":"2025-03-25","cited_by_count":41,"area":"distill"},{"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":"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":"2412.04692","title":"Smoothie: Label Free Language Model Routing","created":"2024-12-06","cited_by_count":31,"area":null},{"id":"2502.01976","title":"CITER: Collaborative Inference for Efficient Large Language Model Decoding with Token-Level Routing","created":"2025-02-04","cited_by_count":30,"area":null},{"id":"2410.11163","title":"Model Swarms: Collaborative Search to Adapt LLM Experts via Swarm Intelligence","created":"2024-10-15","cited_by_count":27,"area":null},{"id":"2410.09300","title":"Nudging: Inference-time Alignment of LLMs via Guided Decoding","created":"2024-10-11","cited_by_count":23,"area":null},{"id":"2502.04510","title":"Heterogeneous Swarms: Jointly Optimizing Model Roles and Weights for Multi-LLM Systems","created":"2025-02-06","cited_by_count":23,"area":null},{"id":"2403.08755","title":"DAM: Dynamic Adapter Merging for Continual Video QA Learning","created":"2024-03-13","cited_by_count":19,"area":null},{"id":"2410.21287","title":"A Systematic Assessment of OpenAI o1-Preview for Higher Order Thinking in Education","created":"2024-10-11","cited_by_count":16,"area":"lr"},{"id":"2501.14082","title":"Communicating Activations Between Language Model Agents","created":"2025-01-23","cited_by_count":16,"area":"lr"},{"id":"2503.21720","title":"Collab: Controlled Decoding using Mixture of Agents for LLM Alignment","created":"2025-03-27","cited_by_count":14,"area":"agents"},{"id":"2504.09923","title":"Guiding Reasoning in Small Language Models with LLM Assistance","created":"2025-04-14","cited_by_count":14,"area":"lr"},{"id":"2406.12295","title":"Fast and Slow Generating: An Empirical Study on Large and Small Language Models Collaborative Decoding","created":"2024-06-18","cited_by_count":13,"area":null},{"id":"2409.02685","title":"RouterRetriever: Routing over a Mixture of Expert Embedding Models","created":"2024-09-04","cited_by_count":12,"area":"moe"},{"id":"2502.04557","title":"Speeding up Speculative Decoding via Sequential Approximate Verification","created":"2025-02-06","cited_by_count":11,"area":"inference"},{"id":"2510.09913","title":"Don't Throw Away Your Pretrained Model","created":"2025-10-10","cited_by_count":10,"area":"lr"},{"id":"2406.15480","title":"On Giant's Shoulders: Effortless Weak to Strong by Dynamic Logits Fusion","created":"2024-06-17","cited_by_count":9,"area":null},{"id":"2601.05106","title":"Token-Level LLM Collaboration via FusionRoute","created":"2026-01-08","cited_by_count":9,"area":null},{"id":"2502.08020","title":"Speculate, then Collaborate: Fusing Knowledge of Language Models during Decoding","created":"2025-02-11","cited_by_count":8,"area":null},{"id":"2506.06998","title":"What makes Reasoning Models Different? Follow the Reasoning Leader for Efficient Decoding","created":"2025-06-08","cited_by_count":8,"area":"lr"},{"id":"2604.17139","title":"The Consensus Trap: Rescuing Multi-Agent LLMs from Adversarial Majorities via Token-Level Collaboration","created":"2026-04-18","cited_by_count":8,"area":"agents"},{"id":"2410.10141","title":"Temperature-Centric Investigation of Speculative Decoding with Knowledge Distillation","created":"2024-10-14","cited_by_count":7,"area":"inference"},{"id":"2601.21257","title":"MoCo: A One-Stop Shop for Model Collaboration Research","created":"2026-01-29","cited_by_count":6,"area":null},{"id":"2410.12891","title":"Multi-trait User Simulation with Adaptive Decoding for Conversational Task Assistants","created":"2024-10-16","cited_by_count":5,"area":null},{"id":"2508.11915","title":"CORE: Measuring Multi-Agent LLM Interaction Quality under Game-Theoretic Pressures","created":"2025-08-16","cited_by_count":5,"area":"agents"},{"id":"2407.02723","title":"e-Health CSIRO at \"Discharge Me!\" 2024: Generating Discharge Summary Sections with Fine-tuned Language Models","created":"2024-07-03","cited_by_count":4,"area":null},{"id":"2410.01677","title":"Mind Scramble: Unveiling Large Language Model Psychology Via Typoglycemia","created":"2024-10-02","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":"2504.07878","title":"Token Level Routing Inference System for Edge Devices","created":"2025-04-10","cited_by_count":4,"area":null},{"id":"2505.21959","title":"EnsemW2S: Enhancing Weak-to-Strong Generalization with Large Language Model Ensembles","created":"2025-05-28","cited_by_count":4,"area":null},{"id":"2607.28979","title":"KV Cache Translation across Heterogeneous Large Language Models","created":"2026-07-31","cited_by_count":4,"area":"inference"},{"id":"2502.02908","title":"COSMosFL: Ensemble of Small Language Models for Fault Localisation","created":"2025-02-05","cited_by_count":3,"area":null},{"id":"2502.21265","title":"Token-level Ensembling of Models with Different Vocabularies","created":"2025-02-28","cited_by_count":3,"area":null},{"id":"2510.15346","title":"When to Ensemble: Identifying Token-Level Points for Stable and Fast LLM Ensembling","created":"2025-10-17","cited_by_count":3,"area":null},{"id":"2512.23765","title":"Entropy-Aware Token Rejection for Improving Speculative Decoding","created":"2025-12-29","cited_by_count":3,"area":"inference"},{"id":"2504.15188","title":"Synergistic Weak-Strong Collaboration by Aligning Preferences","created":"2025-04-21","cited_by_count":2,"area":"lr"},{"id":"2508.18763","title":"Dynamic Collaboration of Multi-Language Models based on Minimal Complete Semantic Units","created":"2025-08-26","cited_by_count":2,"area":"lr"},{"id":"2602.05182","title":"The Single-Multi Evolution Loop for Self-Improving Model Collaboration Systems","created":"2026-02-05","cited_by_count":2,"area":"synth"},{"id":"2602.05307","title":"MentorCollab: Large-to-Small Inference-Time Mentorship for Concise Reasoning in Language Models","created":"2026-02-05","cited_by_count":2,"area":"lr"},{"id":"2605.11167","title":"The Bicameral Model: Bidirectional Hidden-State Coupling Between Parallel Language Models","created":"2026-05-11","cited_by_count":2,"area":null},{"id":"2606.07812","title":"Scaling Participation in Modular AI Systems","created":"2026-06-05","cited_by_count":2,"area":null},{"id":"2607.14277","title":"Multi-Head Latent Control: A Unified Interface for LLM Agent Decision Making","created":"2026-07-15","cited_by_count":2,"area":"agents"},{"id":"2608.21027","title":"Don't Solve, Just Compare: Tiny Advisors for Runtime Intervention in LLM Agents","created":"2026-08-21","cited_by_count":2,"area":"agents"},{"id":"2410.04571","title":"EnsemW2S: Enhancing Weak-to-Strong Generalization with Large Language Model Ensembles","created":"2024-10-06","cited_by_count":1,"area":null},{"id":"2505.23846","title":"Scalable, Symbiotic, AI and Non-AI Agent Based Parallel Discrete Event Simulations","created":"2025-05-28","cited_by_count":1,"area":null},{"id":"2509.22984","title":"From Deferral to Learning: Online In-Context Knowledge Distillation for LLM Cascades","created":"2025-09-26","cited_by_count":1,"area":"distill"},{"id":"2604.22849","title":"R$^3$AG: Retriever Routing for Retrieval-Augmented Generation","created":"2026-04-22","cited_by_count":1,"area":"rag"},{"id":"2607.20327","title":"PyroDash: Cost-Efficient Token-Level Small-Large Language Model Collaborative Inference","created":"2026-07-22","cited_by_count":1,"area":"lr"},{"id":"2607.23394","title":"Inference-Time Consensus for Mitigating Hidden Behaviors from LLM Fine-Tuning","created":"2026-07-25","cited_by_count":1,"area":null},{"id":"2408.11327","title":"Plug, Play, and Fuse: Zero-Shot Joint Decoding via Word-Level Re-ranking Across Diverse Vocabularies","created":"2024-08-21","cited_by_count":0,"area":null},{"id":"2510.27182","title":"SERFLOW: A Cross-Service Cost Optimization Framework for SLO-Aware Dynamic ML Inference","created":"2025-10-31","cited_by_count":0,"area":null},{"id":"2602.09173","title":"$n$-Musketeers: Reinforcement Learning Shapes Collaboration Among Language Models","created":"2026-02-09","cited_by_count":0,"area":"lr"},{"id":"2604.18106","title":"Efficient Low-Resource Language Adaptation via Multi-Source Dynamic Logit Fusion","created":"2026-04-20","cited_by_count":0,"area":null},{"id":"2608.14797","title":"Beyond Tokens: A Survey on Decoding Methods for Large Language and Vision-Language Models","created":"2026-08-14","cited_by_count":0,"area":"mllm"},{"id":"2609.32913","title":"Allspark: Weak to Strong Transfer via Alternating Chain of Thought","created":"2026-09-26","cited_by_count":0,"area":"lr"},{"id":"2609.38274","title":"Which Models Work Well Together? Measuring Heterogeneity for LLM Team Selection","created":"2026-09-29","cited_by_count":0,"area":null},{"id":"2610.02491","title":"What Does a Token Cost? A Mixture-of-Agents Measurement of Sufficient Per-Token Compute","created":"2026-10-01","cited_by_count":0,"area":"agents"}],"next_cursor":null}