Sibyl: Simple yet Effective Agent Framework for Complex Real-world Reasoning

• URL: http://arxiv.org/abs/2407.10718v2
• Date: Tue, 16 Jul 2024 14:16:53 GMT
• Title: Sibyl: Simple yet Effective Agent Framework for Complex Real-world Reasoning
• Authors: Yulong Wang, Tianhao Shen, Lifeng Liu, Jian Xie,
• Abstract summary: Sibyl is a powerful framework designed to tackle complex reasoning tasks by efficiently leveraging a minimal set of tools. Sibyl implements a multi-agent debate-based jury to self-refine the final answers, ensuring a comprehensive and balanced approach. Our experimental results on the GAIA benchmark test set reveal that the Sibyl agent achieves state-of-the-art performance with an average score of 34.55%.
• Score: 12.80689911863731
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing agents based on large language models (LLMs) demonstrate robust problem-solving capabilities by integrating LLMs' inherent knowledge, strong in-context learning and zero-shot capabilities, and the use of tools combined with intricately designed LLM invocation workflows by humans. However, these agents still exhibit shortcomings in long-term reasoning and under-use the potential of existing tools, leading to noticeable deficiencies in complex real-world reasoning scenarios. To address these limitations, we introduce Sibyl, a simple yet powerful LLM-based agent framework designed to tackle complex reasoning tasks by efficiently leveraging a minimal set of tools. Drawing inspiration from Global Workspace Theory, Sibyl incorporates a global workspace to enhance the management and sharing of knowledge and conversation history throughout the system. Furthermore, guided by Society of Mind Theory, Sibyl implements a multi-agent debate-based jury to self-refine the final answers, ensuring a comprehensive and balanced approach. This approach aims to reduce system complexity while expanding the scope of problems solvable-from matters typically resolved by humans in minutes to those requiring hours or even days, thus facilitating a shift from System-1 to System-2 thinking. Sibyl has been designed with a focus on scalability and ease of debugging by incorporating the concept of reentrancy from functional programming from its inception, with the aim of seamless and low effort integration in other LLM applications to improve capabilities. Our experimental results on the GAIA benchmark test set reveal that the Sibyl agent instantiated with GPT-4 achieves state-of-the-art performance with an average score of 34.55%, compared to other agents based on GPT-4. We hope that Sibyl can inspire more reliable and reusable LLM-based agent solutions to address complex real-world reasoning tasks.

Related papers

• ConceptAgent: LLM-Driven Precondition Grounding and Tree Search for Robust Task Planning and Execution [33.252158560173655]
  ConceptAgent is a natural language-driven robotic platform designed for task execution in unstructured environments. We present innovations designed to limit shortcomings, including 1) Predicate Grounding to prevent and recover from infeasible actions, and 2) an embodied version of LLM-guided Monte Carlo Tree Search with self reflection.
  arXiv  Detail & Related papers  (2024-10-08T15:05:40Z)
• Large Language Models for Base Station Siting: Intelligent Deployment based on Prompt or Agent [62.16747639440893]
  Large language models (LLMs) and their associated technologies advance, particularly in the realms of prompt engineering and agent engineering. This approach entails the strategic use of well-crafted prompts to infuse human experience and knowledge into these sophisticated LLMs. This integration represents the future paradigm of artificial intelligence (AI) as a service and AI for more ease.
  arXiv  Detail & Related papers  (2024-08-07T08:43:32Z)
• Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More? [54.667202878390526]
  Long-context language models (LCLMs) have the potential to revolutionize our approach to tasks traditionally reliant on external tools like retrieval systems or databases. We introduce LOFT, a benchmark of real-world tasks requiring context up to millions of tokens designed to evaluate LCLMs' performance on in-context retrieval and reasoning. Our findings reveal LCLMs' surprising ability to rival state-of-the-art retrieval and RAG systems, despite never having been explicitly trained for these tasks.
  arXiv  Detail & Related papers  (2024-06-19T00:28:58Z)
• Adaptive In-conversation Team Building for Language Model Agents [33.03550687362213]
  Leveraging multiple large language model (LLM) agents has shown to be a promising approach for tackling complex tasks. Our new adaptive team-building paradigm offers a flexible solution, realized through a novel agent design named Captain Agent. A comprehensive evaluation across six real-world scenarios demonstrates that Captain Agent significantly outperforms existing multi-agent methods.
  arXiv  Detail & Related papers  (2024-05-29T18:08:37Z)
• Towards Efficient LLM Grounding for Embodied Multi-Agent Collaboration [70.09561665520043]
  We propose a novel framework for multi-agent collaboration that introduces Reinforced Advantage feedback (ReAd) for efficient self-refinement of plans. We provide theoretical analysis by extending advantage-weighted regression in reinforcement learning to multi-agent systems. Experiments on Over-AI and a difficult variant of RoCoBench show that ReAd surpasses baselines in success rate, and also significantly decreases the interaction steps of agents.
  arXiv  Detail & Related papers  (2024-05-23T08:33:19Z)
• Smurfs: Leveraging Multiple Proficiency Agents with Context-Efficiency for Tool Planning [14.635361844362794]
  Smurfs' is a cutting-edge multi-agent framework designed to revolutionize the application of large language models. Smurfs can enhance the model's ability to solve complex tasks at no additional cost.
  arXiv  Detail & Related papers  (2024-05-09T17:49:04Z)
• Enhancing the General Agent Capabilities of Low-Parameter LLMs through Tuning and Multi-Branch Reasoning [56.82041895921434]
  Open-source pre-trained Large Language Models (LLMs) exhibit strong language understanding and generation capabilities. When used as agents for dealing with complex problems in the real world, their performance is far inferior to large commercial models such as ChatGPT and GPT-4.
  arXiv  Detail & Related papers  (2024-03-29T03:48:12Z)
• Small LLMs Are Weak Tool Learners: A Multi-LLM Agent [73.54562551341454]
  Large Language Model (LLM) agents significantly extend the capabilities of standalone LLMs. We propose a novel approach that decomposes the aforementioned capabilities into a planner, caller, and summarizer. This modular framework facilitates individual updates and the potential use of smaller LLMs for building each capability.
  arXiv  Detail & Related papers  (2024-01-14T16:17:07Z)
• Corex: Pushing the Boundaries of Complex Reasoning through Multi-Model Collaboration [83.4031923134958]
  Corex is a suite of novel general-purpose strategies that transform Large Language Models into autonomous agents. Inspired by human behaviors, Corex is constituted by diverse collaboration paradigms including Debate, Review, and Retrieve modes. We demonstrate that orchestrating multiple LLMs to work in concert yields substantially better performance compared to existing methods.
  arXiv  Detail & Related papers  (2023-09-30T07:11:39Z)
• TPTU: Large Language Model-based AI Agents for Task Planning and Tool Usage [28.554981886052953]
  Large Language Models (LLMs) have emerged as powerful tools for various real-world applications. Despite their prowess, intrinsic generative abilities of LLMs may prove insufficient for handling complex tasks. This paper proposes a structured framework tailored for LLM-based AI Agents.
  arXiv  Detail & Related papers  (2023-08-07T09:22:03Z)

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.