CAMPHOR: Collaborative Agents for Multi-input Planning and High-Order Reasoning On Device

• URL: http://arxiv.org/abs/2410.09407v1
• Date: Sat, 12 Oct 2024 07:28:10 GMT
• Title: CAMPHOR: Collaborative Agents for Multi-input Planning and High-Order Reasoning On Device
• Authors: Yicheng Fu, Raviteja Anantha, Jianpeng Cheng,
• Abstract summary: We introduce an on-device Small Language Models (SLMs) framework designed to handle multiple user inputs and reason over personal context locally. CAMPHOR employs a hierarchical architecture where a high-order reasoning agent decomposes complex tasks and coordinates expert agents responsible for personal context retrieval, tool interaction, and dynamic plan generation. By implementing parameter sharing across agents and leveraging prompt compression, we significantly reduce model size, latency, and memory usage.
• Score: 2.4100803794273005
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While server-side Large Language Models (LLMs) demonstrate proficiency in function calling and complex reasoning, deploying Small Language Models (SLMs) directly on devices brings opportunities to improve latency and privacy but also introduces unique challenges for accuracy and memory. We introduce CAMPHOR, an innovative on-device SLM multi-agent framework designed to handle multiple user inputs and reason over personal context locally, ensuring privacy is maintained. CAMPHOR employs a hierarchical architecture where a high-order reasoning agent decomposes complex tasks and coordinates expert agents responsible for personal context retrieval, tool interaction, and dynamic plan generation. By implementing parameter sharing across agents and leveraging prompt compression, we significantly reduce model size, latency, and memory usage. To validate our approach, we present a novel dataset capturing multi-agent task trajectories centered on personalized mobile assistant use-cases. Our experiments reveal that fine-tuned SLM agents not only surpass closed-source LLMs in task completion F1 by~35\% but also eliminate the need for server-device communication, all while enhancing privacy.

Related papers

• AgentOccam: A Simple Yet Strong Baseline for LLM-Based Web Agents [52.13695464678006]
  This study enhances an LLM-based web agent by simply refining its observation and action space. AgentOccam surpasses the previous state-of-the-art and concurrent work by 9.8 (+29.4%) and 5.9 (+15.8%) absolute points respectively.
  arXiv  Detail & Related papers  (2024-10-17T17:50:38Z)
• Internet of Agents: Weaving a Web of Heterogeneous Agents for Collaborative Intelligence [79.5316642687565]
  Existing multi-agent frameworks often struggle with integrating diverse capable third-party agents. We propose the Internet of Agents (IoA), a novel framework that addresses these limitations. IoA introduces an agent integration protocol, an instant-messaging-like architecture design, and dynamic mechanisms for agent teaming and conversation flow control.
  arXiv  Detail & Related papers  (2024-07-09T17:33:24Z)
• CAAP: Context-Aware Action Planning Prompting to Solve Computer Tasks with Front-End UI Only [21.054681757006385]
  We propose an agent that perceives its environment solely through screenshot images. By leveraging the reasoning capability of the Large Language Models, we eliminate the need for large-scale human demonstration data. Agent achieves an average success rate of 94.5% on MiniWoB++ and an average task score of 62.3 on WebShop.
  arXiv  Detail & Related papers  (2024-06-11T05:21:20Z)
• AgentScope: A Flexible yet Robust Multi-Agent Platform [66.64116117163755]
  AgentScope is a developer-centric multi-agent platform with message exchange as its core communication mechanism. The abundant syntactic tools, built-in agents and service functions, user-friendly interfaces for application demonstration and utility monitor, zero-code programming workstation, and automatic prompt tuning mechanism significantly lower the barriers to both development and deployment.
  arXiv  Detail & Related papers  (2024-02-21T04:11:28Z)
• When Large Language Model Agents Meet 6G Networks: Perception, Grounding, and Alignment [100.58938424441027]
  We propose a split learning system for AI agents in 6G networks leveraging the collaboration between mobile devices and edge servers. We introduce a novel model caching algorithm for LLMs within the proposed system to improve model utilization in context.
  arXiv  Detail & Related papers  (2024-01-15T15:20:59Z)
• MobileAgent: enhancing mobile control via human-machine interaction and SOP integration [0.0]
  Large Language Models (LLMs) are now capable of automating mobile device operations for users. Privacy concerns related to personalized user data arise during mobile operations, requiring user confirmation. We have designed interactive tasks between agents and humans to identify sensitive information and align with personalized user needs. Our approach is evaluated on the new device control benchmark AitW, which encompasses 30K unique instructions across multi-step tasks.
  arXiv  Detail & Related papers  (2024-01-04T03:44:42Z)
• Dynamic LLM-Agent Network: An LLM-agent Collaboration Framework with Agent Team Optimization [59.39113350538332]
  Large language model (LLM) agents have been shown effective on a wide range of tasks, and by ensembling multiple LLM agents, their performances could be further improved. Existing approaches employ a fixed set of agents to interact with each other in a static architecture. We build a framework named Dynamic LLM-Agent Network ($textbfDyLAN$) for LLM-agent collaboration on complicated tasks like reasoning and code generation.
  arXiv  Detail & Related papers  (2023-10-03T16:05:48Z)
• AutoAgents: A Framework for Automatic Agent Generation [27.74332323317923]
  AutoAgents is an innovative framework that adaptively generates and coordinates multiple specialized agents to build an AI team according to different tasks. Our experiments on various benchmarks demonstrate that AutoAgents generates more coherent and accurate solutions than the existing multi-agent methods.
  arXiv  Detail & Related papers  (2023-09-29T14:46:30Z)
• Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations [53.76682562935373]
  We introduce an efficient framework called textbfInteRecAgent, which employs LLMs as the brain and recommender models as tools. InteRecAgent achieves satisfying performance as a conversational recommender system, outperforming general-purpose LLMs.
  arXiv  Detail & Related papers  (2023-08-31T07:36:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.