arXiv AI
Jul 13
Director: Accelerating Distributed MoE Serving via Online Proactive Expert Placement
arXiv:2607. 08782v1 Announce Type: cross Abstract: Expert parallelism has become the prevailing paradigm to serve Mixture-of-Experts (MoE) models.
Optimal Scheduling in a Question-Answering Forum of Knowledge Workers
arXiv:2606. 19759v1 Announce Type: new Abstract: As individuals turn to the Internet to find answers to questions they may have, several Question Answering (QA) forums have evolved, where users knowledgeable in certain topics can contribute their expertise to answering these requests for information.
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arXiv Machine Learning
Jun 16
Design and Scheduling of an AI-based Queueing System
arXiv:2406. 06855v3 Announce Type: replace-cross Abstract: To leverage prediction models to make optimal scheduling decisions in service systems, we must understand how predictive errors impact congestion due to externalities on the delay of other jobs.
arXiv AI
Aug 13
User-Assisted Collaborative Distributed Inference for Efficient QoS-Aware Autoscaling
arXiv:2608. 11840v1 Announce Type: cross Abstract: Growing demand for artificial intelligence (AI) inference services requires scalable infrastructure, yet centralized serving costs rise with demand.
arXiv AI
Jun 8
How AI Agents Reshape Knowledge Work: Autonomy, Efficiency, and Scope
arXiv:2606. 07489v1 Announce Type: new Abstract: Frontier AI systems are bridging the gap between intelligence and utility by shifting from conversational assistants to autonomous agents that execute tasks end to end.
arXiv Machine Learning
Jun 3
MOSAIC: Efficient Mixture-of-Agent Scheduling via Adaptive Aggregation and Inference Concurrency
arXiv:2606. 03014v1 Announce Type: new Abstract: Mixture-of-Agents (MoA) systems improve reasoning accuracy by routing each query to multiple expert LLMs and aggregating their outputs.
arXiv AI
Aug 11
How to Ask the AI: A User Perspective Survey for Large Language Model Prompting
arXiv:2608. 07494v1 Announce Type: cross Abstract: AI tools like ChatGPT and DeepSeek, powered by Large Language Models (LLMs), allow users to obtain instant and effective content responses simply by typing requests, such as ``plan a three-day Vienna trip'', ``solve the attached mathematical problem'', ``draft an email to inquire review progress'', etc.
arXiv AI
2d ago
DeAR: Decentralized Agentic Reasoning via Capability Grounding and Collaborative Thought Navigation
arXiv:2608. 17282v1 Announce Type: new Abstract: Existing agentic reasoning systems typically rely on centralized protocols.
arXiv AI
Jul 21
ClawBench: Can AI Agents Complete Everyday Online Tasks?
arXiv:2604. 08523v2 Announce Type: replace-cross Abstract: AI agents may be able to assist with emails and documents, but can they reliably complete everyday online workflows on real websites?
arXiv AI
Jun 29
Ranking Before Serving: Low-Latency LLM Serving via Pairwise Learning-to-Rank
arXiv:2510. 03243v3 Announce Type: replace-cross Abstract: Efficient scheduling of large language model (LLM) inference tasks is critical for achieving low latency and high throughput, a challenge that is becoming increasingly acute with the rise of reasoning-capable LLMs whose generation lengths are highly variable.
arXiv AI
Jul 13
Agora: Enhancing LLM Agent Reasoning Via Auction-Based Task Allocation
arXiv:2607. 09600v1 Announce Type: new Abstract: Enhancing the reasoning capabilities of large language model (LLM) agents requires effective orchestration of diverse expert models and tools.
arXiv Machine Learning
Jul 30
Incast-Free MoE Rate-Based Scheduling
arXiv:2607. 26340v1 Announce Type: cross Abstract: Mixture of Experts (MoE) architectures have become key to large language models; however, their typical round-robin (RR) scheduling introduces significant bottlenecks.
arXiv Machine Learning
Jun 4
Scheduling in Queueing Systems with Uncertain and Evolving Holding Costs
arXiv:2505. 21331v2 Announce Type: replace-cross Abstract: In content moderation for social media platforms, the cost of delaying the review of a content is proportional to its view trajectory, which fluctuates and is apriori unknown.