arXiv AI
Aug 11
SkillSmith: Enhancing Locally Deployed Agents via Automatic Skill Construction and Evolution
arXiv:2608. 08037v1 Announce Type: new Abstract: LLM-based agent frameworks now act as personal assistants for multi-step tasks.
Large-Small Model Collaboration for Enhancing Edge-Deployed Small Models
arXiv:2503. 10367v2 Announce Type: replace-cross Abstract: Edge devices host domain-specific small language models (SLMs) with limited resources, while private clouds offer larger LLMs.
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arXiv AI
Jun 2
CoMIC: Collaborative Memory and Insights Circulation for Long-Horizon LLM Agents in Cloud-Edge Systems
arXiv:2606. 00756v1 Announce Type: new Abstract: Deploying lightweight Large Language Model (LLM) agents on edge servers can reduce latency and move agentic services closer to users, but resource-constrained edge models often struggle with long-horizon tasks that require persistent memory, subgoal tracking, and reflection.
arXiv AI
5d ago
SPADE: Speculative Decoding for Precise and Low Cost Distributed Edge Cloud Inference
arXiv:2608. 13076v1 Announce Type: new Abstract: Large Language Models (LLMs) have achieved remarkable success in natural language understanding and generation, but their deployment is constrained by high computational demands.
arXiv AI
Jul 20
Scalable LLM Agent Tool Access in the Cloud
arXiv:2607. 15593v1 Announce Type: cross Abstract: LLM agents increasingly rely on tool calling to act on external systems, and the Model Context Protocol (MCP) has quickly become its de facto interface.
arXiv AI
Jun 15
PLAIground: SLO-Driven Runtime Model Selection for Compound AI Systems in the Edge-Cloud-Space Continuum
arXiv:2606. 14356v1 Announce Type: cross Abstract: Applications in the 3D Computing Continuum, which unifies edge, cloud, and space, require combining multiple AI tasks such as object detection, time-series analytics, and natural language processing into Compound AI systems.
arXiv Machine Learning
Jul 14
Device-Cloud Collaborative LLM Inference with Multi-Modal, Multi-Task, Multi-Turn Conversations
arXiv:2502. 11007v5 Announce Type: replace Abstract: Compared to traditional machine learning models, recent large language models (LLMs) can exhibit multi-task-solving capabilities through multi-modal data sources and multi-turn conversations.
arXiv AI
Jun 3
From Prompt to Service: An SLM-Based Agent Orchestration Gateway for AI-Driven Virtual Worlds
arXiv:2606. 03557v1 Announce Type: new Abstract: As generative AI capabilities expand, AI-driven virtual worlds face a growing architectural challenge.
arXiv AI
Aug 5
Getting the Parameters Right: A Difficulty-Graded Benchmark and Probe-Guided Training for LLM Tool Calls
arXiv:2608. 03071v1 Announce Type: new Abstract: Large language model agents derive much of their capability from tool use.
arXiv AI
Jul 16
Efficient and Privacy Aware Edge Cloud Collaborative Inference for Large Language Models
arXiv:2607. 13093v1 Announce Type: cross Abstract: On-device LLM inference faces a trilemma of response latency, limited hardware resources and user privacy.
arXiv AI
Jun 9
Meeting SLOs, Slashing Hours: Automated Enterprise LLM Optimization with OptiKIT
arXiv:2601. 20408v2 Announce Type: replace-cross Abstract: Enterprise LLM deployment faces a critical scalability challenge: organizations must optimize models systematically to scale AI initiatives within constrained compute budgets, yet the specialized expertise required for manual optimization remains a niche and scarce skillset.
arXiv AI
Jun 3
Synthesize and Reward -- Reinforcement Learning for Multi-Step Tool Use in Live Environments
arXiv:2606. 03892v1 Announce Type: cross Abstract: Training LLMs to orchestrate multi-step tool calls is held back by three coupled obstacles: realistic stateful execution environments are costly to build, synthetic training queries are often detached from the server's actual state (so the generated tool calls fail to execute), and recall-based RL rewards incentivize verbose tool-calling patterns.
arXiv Machine Learning
Jun 4
Efficient Reasoning on the Edge
arXiv:2603. 16867v2 Announce Type: replace Abstract: Large language models (LLMs) with chain-of-thought reasoning achieve state-of-the-art performance across complex problem-solving tasks, but their verbose reasoning traces and large context requirements make them impractical for edge deployment.