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 AI
Sep 30
Asking for What Was Never Requested: Horizontal and Vertical Proactivity in Agents
arXiv:2609.37236v1 Announce Type: new Abstract: An agent that uses tools typically responds to what the user explicitly asks, yet completing the task may require information the user never requested....
arXiv AI
Sep 3
Beyond-RAG: Question Identification and Answer Generation in Real-Time Conversations
The paper presents a decision‑support system that enhances retrieval‑augmented generation (RAG) for customer contact centers by first identifying customer questions in real time. If a query matches a frequently asked question (FAQ), the system retrieves the answer directly from the FAQ database; otherwise it generates an answer via RAG, delivering responses to agents within two seconds. The approach reduces manual query formulation, lowers average handling times, and cuts operational costs, and it includes an automated workflow that uses LLMs to extract FAQs from historical transcripts when none are predefined.
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.
Hugging Face Trending Papers
Sep 29
Rational Clarification by Assistive Agents via Value-of-Information Reasoning
Users of language-based assistive agents often make ambiguous requests. In response, an assistant can either directly act on its interpretation of the request --- risking misalignment with the user --...
arXiv AI
Sep 30
Rational Clarification by Assistive Agents via Value-of-Information Reasoning
arXiv:2609.37588v1 Announce Type: new Abstract: Users of language-based assistive agents often make ambiguous requests. In response, an assistant can either directly act on its interpretation of the...
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
Sep 18
Not All AI Agents Are Equal: Characterizing Resource and Performance Dynamics
The paper investigates how large‑language‑model (LLM) based AI agents mix latency, local resource usage, and container bottlenecks when processing user requests that involve remote LLM calls and local tool execution. By measuring three representative tasks—retrieval‑augmented question answering, web search, and software coding—the authors show that agents exhibit diverse resource dynamics, with concurrent requests revealing task‑specific bottlenecks in CPU, disk I/O, and memory. Leveraging these insights, they propose CPU‑aware tool admission and task‑aware CPU allocation, achieving up to a 5.4× speed‑up for CPU‑sensitive tasks and a 32% reduction in average latency across multiple tasks.
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
Aug 25
Clarify User Expertise: Towards Proactive Conversational Agents Tailoring Responses to User Proficiency
arXiv:2608.22266v1 Announce Type: new Abstract: In the context of information seeking, conversational agents are undergoing an evolution from reactive tools to proactive, personalized assistants. A c...
arXiv AI
Jun 2
NBQ: Next-Best-Question for Dynamic Profiling
arXiv:2606. 00809v1 Announce Type: new Abstract: Many real-world conversational settings for knowledge discovery, including podcasts, hiring screens, and marketplaces, require a purpose-driven understanding of a person.