{"total":85,"rows":[{"id":"2504.15930","title":"StreamRL: Scalable, Heterogeneous, and Elastic RL for LLMs with Disaggregated Stream Generation","created":"2025-04-22","cited_by_count":61,"area":null},{"id":"2412.20993","title":"Efficiently Scaling LLM Reasoning with Certaindex","created":"2024-12-30","cited_by_count":58,"area":"lr"},{"id":"2410.01035","title":"Don't Stop Me Now: Embedding Based Scheduling for LLMs","created":"2024-10-01","cited_by_count":35,"area":null},{"id":"2502.20576","title":"OmniRouter: Budget and Performance Controllable Multi-LLM Routing","created":"2025-02-27","cited_by_count":35,"area":null},{"id":"2412.03594","title":"BatchLLM: Optimizing Large Batched LLM Inference with Global Prefix Sharing and Throughput-oriented Token Batching","created":"2024-11-29","cited_by_count":32,"area":null},{"id":"2511.14617","title":"Seer: Online Context Learning for Fast Synchronous LLM Reinforcement Learning","created":"2025-11-18","cited_by_count":25,"area":null},{"id":"2411.16102","title":"BlendServe: Optimizing Offline Inference for Auto-regressive Large Models with Resource-aware Batching","created":"2024-11-25","cited_by_count":19,"area":null},{"id":"2502.07115","title":"Online Scheduling for LLM Inference with KV Cache Constraints","created":"2025-02-10","cited_by_count":19,"area":"inference"},{"id":"2504.11320","title":"Optimizing LLM Inference: Fluid-Guided Online Scheduling with Memory Constraints","created":"2025-04-15","cited_by_count":19,"area":null},{"id":"2504.20068","title":"JITServe: SLO-aware LLM Serving with Imprecise Request Information","created":"2025-04-24","cited_by_count":17,"area":"inference"},{"id":"2504.19720","title":"Taming the Titans: A Survey of Efficient LLM Inference Serving","created":"2025-04-28","cited_by_count":16,"area":"inference"},{"id":"2504.19867","title":"semi-PD: Towards Efficient LLM Serving via Phase-Wise Disaggregated Computation and Unified Storage","created":"2025-04-28","cited_by_count":16,"area":"inference"},{"id":"2502.15197","title":"TETRIS: Optimal Draft Token Selection for Batch Speculative Decoding","created":"2025-02-21","cited_by_count":15,"area":"inference"},{"id":"2409.17264","title":"No Request Left Behind: Tackling Heterogeneity in Long-Context LLM Inference with Medha","created":"2024-09-25","cited_by_count":13,"area":null},{"id":"2410.18248","title":"Fast Inference for Augmented Large Language Models","created":"2024-10-23","cited_by_count":10,"area":"inference"},{"id":"2501.14808","title":"HyGen: Efficient LLM Serving via Elastic Online-Offline Request Co-location","created":"2025-01-15","cited_by_count":10,"area":"inference"},{"id":"2507.09019","title":"On Evaluating Performance of LLM Inference Serving Systems","created":"2025-07-11","cited_by_count":10,"area":"inference"},{"id":"2508.14544","title":"Adaptively Robust LLM Inference Optimization under Prediction Uncertainty","created":"2025-08-20","cited_by_count":10,"area":null},{"id":"2411.17741","title":"Chameleon: Adaptive Caching and Scheduling for Many-Adapter LLM Inference Environments","created":"2024-11-24","cited_by_count":9,"area":"inference"},{"id":"2412.04504","title":"Multi-Bin Batching for Increasing LLM Inference Throughput","created":"2024-12-03","cited_by_count":9,"area":null},{"id":"2602.11812","title":"Predicting LLM Output Length via Entropy-Guided Representations","created":"2026-02-12","cited_by_count":9,"area":"inference"},{"id":"2604.00499","title":"Scheduling LLM Inference with Uncertainty-Aware Output Length Predictions","created":"2026-04-01","cited_by_count":8,"area":null},{"id":"2508.06133","title":"LLM Serving Optimization with Variable Prefill and Decode Lengths","created":"2025-08-08","cited_by_count":7,"area":"inference"},{"id":"2504.07494","title":"Apt-Serve: Adaptive Request Scheduling on Hybrid Cache for Scalable LLM Inference Serving","created":"2025-04-10","cited_by_count":6,"area":"inference"},{"id":"2505.23022","title":"Scorpio: Serving Right Requests at the Right Time for Heterogeneous SLOs in LLM Inference","created":"2025-05-29","cited_by_count":6,"area":"inference"},{"id":"2601.10729","title":"OrbitFlow: SLO-Aware Long-Context LLM Serving with Fine-Grained KV Cache Reconfiguration","created":"2026-01-05","cited_by_count":6,"area":"inference"},{"id":"2605.00528","title":"SAGA: Workflow-Atomic Scheduling for AI Agent Inference on GPU Clusters","created":"2026-05-01","cited_by_count":6,"area":"agents"},{"id":"2605.13414","title":"TRIAGE: Evaluating Prospective Metacognitive Control in LLMs under Resource Constraints","created":"2026-05-13","cited_by_count":6,"area":null},{"id":"2503.07545","title":"Queueing, Predictions, and LLMs: Challenges and Open Problems","created":"2025-03-10","cited_by_count":5,"area":null},{"id":"2505.17074","title":"Semi-Clairvoyant Scheduling of Speculative Decoding Requests to Minimize LLM Inference Latency","created":"2025-05-20","cited_by_count":4,"area":"inference"},{"id":"2601.19300","title":"Queue Length Regret Bounds for Contextual Queueing Bandits","created":"2026-01-27","cited_by_count":4,"area":null},{"id":"2603.22206","title":"Chimera: Latency- and Performance-Aware Multi-agent Serving for Heterogeneous LLMs","created":"2026-03-23","cited_by_count":4,"area":"agents"},{"id":"2506.02025","title":"Evaluating the Efficacy of LLM-Based Reasoning for Multiobjective HPC Job Scheduling","created":"2025-05-29","cited_by_count":3,"area":"lr"},{"id":"2602.01237","title":"Predictive Scheduling for Efficient Inference-Time Reasoning in Large Language Models","created":"2026-02-01","cited_by_count":3,"area":"lr"},{"id":"2602.16603","title":"FlowPrefill: Decoupling Preemption from Prefill Scheduling Granularity to Mitigate Head-of-Line Blocking in LLM Serving","created":"2026-02-18","cited_by_count":3,"area":"inference"},{"id":"2604.07931","title":"Robust Length Prediction: A Perspective from Heavy-Tailed Prompt-Conditioned Distributions","created":"2026-04-09","cited_by_count":3,"area":"inference"},{"id":"2510.03243","title":"Ranking Before Serving: Low-Latency LLM Serving via Pairwise Learning-to-Rank","created":"2025-09-25","cited_by_count":2,"area":"lr"},{"id":"2510.17015","title":"Justitia: Fair and Efficient Scheduling of Task-parallel LLM Agents with Selective Pampering","created":"2025-10-19","cited_by_count":2,"area":"agents"},{"id":"2601.20309","title":"SuperInfer: SLO-Aware Rotary Scheduling and Memory Management for LLM Inference on Superchips","created":"2026-01-28","cited_by_count":2,"area":"inference"},{"id":"2602.22718","title":"RLHFless: Serverless Computing for Efficient RLHF","created":"2026-02-26","cited_by_count":2,"area":"alignment"},{"id":"2603.06403","title":"Adapter-Augmented Bandits for Online Multi-Constrained Multi-Modal Inference Scheduling","created":"2026-03-06","cited_by_count":2,"area":"mllm"},{"id":"2605.01214","title":"Agentic AI Systems Should Be Designed as Marginal Token Allocators","created":"2026-05-02","cited_by_count":2,"area":"agents"},{"id":"2606.01839","title":"Observation, Not Prediction: Conversation-Level Disaggregated Scheduling for Agentic Serving","created":"2026-06-01","cited_by_count":2,"area":"agents"},{"id":"2606.09668","title":"Algorithm for Contextual Queueing Bandits with Rate-Optimal Queue Length Regret","created":"2026-06-08","cited_by_count":2,"area":null},{"id":"2412.18695","title":"TimelyLLM: Segmented LLM Serving System for Time-sensitive Robotic Applications","created":"2024-12-24","cited_by_count":1,"area":null},{"id":"2506.12204","title":"Semantic Scheduling for LLM Inference","created":"2025-06-13","cited_by_count":1,"area":null},{"id":"2506.14851","title":"Efficient Serving of LLM Applications with Probabilistic Demand Modeling","created":"2025-06-17","cited_by_count":1,"area":null},{"id":"2509.24957","title":"Intra-request branch orchestration for efficient LLM reasoning","created":"2025-09-29","cited_by_count":1,"area":"lr"},{"id":"2510.13668","title":"STAR: Decode-Phase Rescheduling for LLM Inference","created":"2025-10-15","cited_by_count":1,"area":null},{"id":"2512.19179","title":"CascadeInfer: Length-Aware Scheduling of LLM Serving with Low Latency and Load Balancing","created":"2025-12-22","cited_by_count":1,"area":"inference"},{"id":"2602.06072","title":"PackInfer: Compute- and I/O-Efficient Attention for Batched LLM Inference","created":"2026-02-03","cited_by_count":1,"area":"inference"},{"id":"2602.09392","title":"LLMAC: A Global and Explainable Access Control Framework with Large Language Model","created":"2026-02-10","cited_by_count":1,"area":null},{"id":"2602.21626","title":"Multi-Layer Scheduling for MoE-Based LLM Reasoning","created":"2026-02-25","cited_by_count":1,"area":"moe"},{"id":"2603.05692","title":"Parallelization Strategies for Dense LLM Deployment: Navigating Through Application-Specific Tradeoffs and Bottlenecks","created":"2026-03-05","cited_by_count":1,"area":null},{"id":"2604.22161","title":"Logistic Bandits with $\\tilde{O}(\\sqrt{dT})$ Regret without Context Diversity Assumptions","created":"2026-04-24","cited_by_count":1,"area":null},{"id":"2606.07571","title":"Enabling KV Caching of Shared Prefix for Diffusion Language Models","created":"2026-05-26","cited_by_count":1,"area":"diffusion"},{"id":"2607.18253","title":"Mind the Delay: Latency-aware Query Routing for Heterogeneous LLM pools","created":"2026-05-13","cited_by_count":1,"area":null},{"id":"2609.01068","title":"OUTLETS: Output-Length Prediction from Speculative Decoding Backbones","created":"2026-09-01","cited_by_count":1,"area":"inference"},{"id":"2503.16893","title":"Improving the End-to-End Efficiency of Offline Inference for Multi-LLM Applications Based on Sampling and Simulation","created":"2025-03-21","cited_by_count":0,"area":null},{"id":"2507.03220","title":"Symbiosis: Multi-Adapter Inference and Fine-Tuning","created":"2025-07-03","cited_by_count":0,"area":null},{"id":"2511.22481","title":"OmniInfer: System-Wide Acceleration Techniques for Optimizing LLM Serving Throughput and Latency","created":"2025-11-27","cited_by_count":0,"area":"inference"},{"id":"2512.12928","title":"ProServe: Unified Multi-Priority Request Scheduling for LLM Serving","created":"2025-12-15","cited_by_count":0,"area":"inference"},{"id":"2602.17778","title":"Asking Forever: Universal Activations Behind Turn Amplification in Conversational LLMs","created":"2026-02-19","cited_by_count":0,"area":null},{"id":"2602.18931","title":"WANSpec: Leveraging Global Compute Capacity for LLM Inference","created":"2026-02-21","cited_by_count":0,"area":"inference"},{"id":"2603.07917","title":"SageSched: Efficient LLM Scheduling Confronting Demand Uncertainty and Hybridity","created":"2026-03-09","cited_by_count":0,"area":"inference"},{"id":"2603.11273","title":"Duration Aware Scheduling for ASR Serving Under Workload Drift","created":"2026-03-11","cited_by_count":0,"area":null},{"id":"2603.26498","title":"TCM-Serve: Modality-aware Scheduling for Multimodal Large Language Model Inference","created":"2026-03-27","cited_by_count":0,"area":"mllm"},{"id":"2604.09562","title":"StreamServe: Adaptive Speculative Flows for Low-Latency Disaggregated LLM Serving","created":"2026-02-11","cited_by_count":0,"area":"inference"},{"id":"2605.06113","title":"Tackling the Data-Parallel Load Balancing Bottleneck in LLM Serving: Practical Online Routing at Scale","created":"2026-05-07","cited_by_count":0,"area":"inference"},{"id":"2605.27566","title":"DynaSchedBench: Calibrated Dynamic Scheduling Benchmarks and Observability Paradox in LLM-based Scheduling Agents","created":"2026-05-26","cited_by_count":0,"area":"agents"},{"id":"2606.00946","title":"Lodestar: An Online-Learning LLM Inference Router","created":"2026-05-31","cited_by_count":0,"area":null},{"id":"2606.07248","title":"Clairvoyant: Predictive Shortest-Job-First Admission for Serial LLM Inference","created":"2026-06-05","cited_by_count":0,"area":"inference"},{"id":"2606.18431","title":"Beyond Prediction: Tail-Aware Scheduling for LLM Inference","created":"2026-06-16","cited_by_count":0,"area":"inference"},{"id":"2606.22327","title":"Geometry-Aware Online Scheduling for LLM Serving: From Theoretical Bound to System Practice","created":"2026-06-21","cited_by_count":0,"area":"inference"},{"id":"2607.02525","title":"PEEK: Predictive Queue-Informed KV Cache Management for LLM Serving","created":"2026-05-10","cited_by_count":0,"area":"inference"},{"id":"2607.03948","title":"Online Linear Programming for Multi-Objective Routing in LLM Serving","created":"2026-07-04","cited_by_count":0,"area":"inference"},{"id":"2607.26633","title":"NELSSA: A GPU-PNM Heterogeneous System for Mixed-Length LLM Serving via Length-based Request Placement","created":"2026-07-29","cited_by_count":0,"area":"inference"},{"id":"2608.06135","title":"LLM Inference Under Bursty Workload Distribution: Modifying the WAIT Algorithm","created":"2026-08-06","cited_by_count":0,"area":null},{"id":"2609.15359","title":"MAPS: Memory-Aware Predictive Scheduling Framework for Large Language Model Serving","created":"2026-09-14","cited_by_count":0,"area":"inference"},{"id":"2609.16206","title":"Calibrate, Then Route: A Measured Study of Learned Request Routing for Disaggregated LLM Serving","created":"2026-09-14","cited_by_count":0,"area":"inference"},{"id":"2609.34370","title":"Spexis: Speculative Lookahead Scheduling for LLM Inference","created":"2026-09-28","cited_by_count":0,"area":"inference"},{"id":"2609.35760","title":"TokenCast: Forecasting Token Consumption During LLM Agent Execution","created":"2026-09-28","cited_by_count":0,"area":"agents"},{"id":"2610.00499","title":"Denoising Surface: Modeling and Predicting Inference Cost for Diffusion LLM Serving","created":"2026-09-30","cited_by_count":0,"area":"inference"},{"id":"2610.03430","title":"Jumping the Line: Exploiting Length Predictions in LLM Scheduling","created":"2026-10-02","cited_by_count":0,"area":null},{"id":"2610.15899","title":"Lightweight Scheduling Policies for Continuously Batched LLM Serving Across GPU Architectures","created":"2026-10-11","cited_by_count":0,"area":"inference"}],"next_cursor":null}