arXiv AI
Jun 16
Frontier: Towards Comprehensive and Accurate LLM Inference Simulation
arXiv:2605. 21312v2 Announce Type: replace-cross Abstract: Modern LLM serving is no longer homogeneous or monolithic.
Global Simulation-Guided Dynamic Operator Scheduling for Efficient Multi-Tenant Model Serving
arXiv:2608. 15762v1 Announce Type: cross Abstract: Container-granularity scheduling leaves abundant short-lived idle slices within containers unexploited.
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arXiv AI
Jul 23
Fine-grained Computation-Communication Overlap via Tile-level Signaling and Scheduling for Mixture-of-Experts
arXiv:2607. 19539v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) architectures increase model capacity without proportionally increasing computation cost and have become a key building block for scaling large language models (LLMs) to trillion-parameter regimes.
arXiv AI
Jul 7
OctoPipe: Reducing Pipeline Bubbles for Heterogeneous Models via Co-Optimizing Partitioning, Placement, and Scheduling
arXiv:2509. 23722v2 Announce Type: replace-cross Abstract: Pipeline parallelism is widely used to train large language models (LLMs).
arXiv AI
Jun 9
AGENTSERVESIM: A Hardware-aware Simulator for Multi-Turn LLM Agent Serving
arXiv:2606. 09613v1 Announce Type: cross Abstract: Multi-turn LLM agents interleave model calls with external tool invocations, shifting serving from stateless request processing to stateful program execution.
arXiv Machine Learning
Jun 16
MARS: Efficient, Adaptive Co-Scheduling for Heterogeneous Agentic Systems
arXiv:2604. 26963v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly deployed as the execution core of autonomous agents rather than as standalone text generators.
arXiv Machine Learning
1d ago
Beyond Binary Priorities: Multi-Tier SLA Scheduling for Large Language Model Serving
arXiv:2608. 16336v1 Announce Type: cross Abstract: Modern LLM serving deployments must simultaneously satisfy heterogeneous service-level objectives (SLOs) across a diverse population of user tiers, ranging from latency-critical API calls to background batch processing.
arXiv AI
Jun 6
RedKnot: Efficient Long-Context LLM Serving with Head-Aware KV Reuse and SegPagedAttention
arXiv:2606. 06256v1 Announce Type: new Abstract: As the input length of large language model (LLM) serving continues to grow, the KV cache has become a dominant bottleneck in AI infrastructure.
arXiv AI
Jul 7
Data Driven Optimization of GPU efficiency for Distributed LLM-Adapter Serving
arXiv:2602. 24044v2 Announce Type: replace-cross Abstract: Large Language Model (LLM) adapters enable low-cost model specialization, but introduce complex caching and scheduling challenges in distributed serving systems where hundreds of adapters must be hosted concurrently.
Hugging Face Trending Papers
Jun 8
AGENTSERVESIM: A Hardware-aware Simulator for Multi-Turn LLM Agent Serving
Multi-turn LLM agents interleave model calls with external tool invocations, shifting serving from stateless request processing to stateful program execution. Serving these workloads requires scheduling, KV-cache management, and routing policies that use program-level context, including turn dependencies, tool-induced gaps, and reusable KV state.
arXiv AI
Jul 31
ServerlessT2I: Efficient Text-to-Image Workflow Serving on a Serverless Platform
arXiv:2607. 26566v1 Announce Type: cross Abstract: Text-to-image (T2I) workflows are increasingly deployed on serverless platforms because users often compose customized workflows and invoke them intermittently.
arXiv AI
Jul 23
AgentCgroup: Understanding and Controlling OS Resources of AI Agents
arXiv:2602. 09345v3 Announce Type: replace-cross Abstract: AI agents are increasingly deployed in multi-tenant cloud environments, where they execute diverse tool calls within sandboxed containers, each call with distinct resource demands and rapid fluctuations.
arXiv Machine Learning
Jun 3
Multi-Segment Attention: Enabling Efficient KV-Cache Management for Faster Large Language Model Serving
arXiv:2606. 02964v1 Announce Type: cross Abstract: Large Language Model (LLM) inference relies on key-value (KV) caches to avoid redundant attention computation.