Large language models (LLMs) have revolutionized human-computer interaction but face challenges in complex real-world scenarios requiring extensive reasoning. LLM-based agents struggle with lengthy reasoning chains, leading to error propagation and reduced accuracy. Existing systems’ complexity hinders practical deployment and scalability. Also, long-context management poses a significant challenge, with a gap between claimed and effective context lengths LLMs can handle. The “context dilution” problem further complicates information integration from diverse sources. These challenges underscore the need for a simpler approach that enhances reasoning capabilities while improving context management, ensuring LLMs maintain focus on relevant information without being overwhelmed by data volume.
Recent advancements in AI have led to the integration of LLMs into autonomous agents, pushing towards Artificial General Intelligence (AGI). These LLM-based agents have shown promise in various domains, including mathematical problem-solving, coding, role-playing, and social simulation. Open-source communities have developed frameworks like Langchain, BabyAGI, and AutoGPT to create more versatile agents capable of handling general tasks. While these agents perform well in straightforward scenarios, they struggle with complex real-world challenges. This limitation highlights the need for further improvements in general-purpose LLM-based agents to effectively address more intricate problems and bridge the gap between specialized and truly versatile AI systems.
Researchers from Baichuan Inc. and the College of Intelligence and Computing, Tianjin University, introduce Sibyl, a robust LLM-based agent framework designed to tackle complex reasoning tasks. It comprises four main modules: a tool planner, an external information acquisition channel, a multi-agent debate-based jury, and a global workspace. The key innovation lies in the external information acquisition channel, which efficiently compresses and processes information using a custom representation language. This approach allows Sibyl to focus on relevant details, conserve context length, and enable extended reasoning steps. The framework also incorporates a global workspace for seamless information sharing and a jury for self-refinement before final responses.
Sibyl’s design is rooted in functional programming principles, emphasizing reusability and statelessness. It uses QA functions instead of dialogues in internal LLM inference requests, allowing independent operation without persistent states. This approach simplifies the framework’s structure and facilitates debugging and enhancement. Experimental results on the GAIA benchmark test set demonstrate Sibyl’s state-of-the-art performance, particularly in challenging scenarios. This underscores Sibyl’s improved capability in solving complex reasoning tasks and its potential to advance LLM-based applications towards more deliberate, System-2 thinking.
The Sibyl framework is built on a design philosophy that aims to reduce complexity while enhancing the capabilities of LLM-based agents. It employs a human-oriented browser interface instead of Retrieval Augmented Generation, preserving more context and depth in data access. Sibyl uses a stateless, reentrant QA function rather than dialogues, simplifying the system architecture and facilitating easier maintenance. The framework centralizes its functionalities around two primary tools: a Web browser and Python environments, aligning the browser’s interface more closely with human interaction modes.
Sibyl emphasizes enhancing capabilities for long-term memory, planning, and error correction. It incorporates a global workspace shared by all modules, storing information with an incremental state-based representation language. This selectively compresses past events, adding only relevant information increments. The framework also includes planning and self-correction mechanisms, summarizing tool outcomes and planning subsequent steps based on current progress assessment. A “Jury” mechanism utilizing a multi-agent debate format enables self-critique and correction, efficiently using information stored in the global workspace to refine responses and ensure accurate problem-solving.
The experimental results demonstrate Sibyl’s superior performance on the GAIA benchmark test set, particularly in challenging Level 2 and Level 3 scenarios. Sibyl outperformed other models, including GPT-4 with and without plugins, AutoGPT-4, AutoGen, and FRIDAY. On the test set, Sibyl achieved an overall accuracy of 34.55%, compared to 32.33% for AutoGen and 24.25% for FRIDAY. The performance gap widened in more complex scenarios, highlighting Sibyl’s enhanced ability to mitigate error propagation in complex reasoning processes.
Sibyl also exhibited superior generalization capabilities, with a smaller decline in accuracy from validation to test set (40.00% to 34.55%) compared to AutoGen (39.39% to 32.33%) and FRIDAY (34.55% to 24.25%). In terms of efficiency, Sibyl consistently outperformed humans when solving problems correctly, using significantly fewer steps across all difficulty levels. Despite being limited to 20 reasoning steps, Sibyl demonstrated high reasoning efficiency, indicating a strong capability to mitigate unnecessary reasoning and suppress error propagation. These results underscore Sibyl’s potential in advancing LLM-based agents towards more deliberate and efficient problem-solving in complex scenarios.
Sibyl represents a significant advancement in LLM-based agent frameworks, designed to enhance complex reasoning capabilities. By incorporating a modular design and a global workspace for efficient information sharing and collaboration, Sibyl facilitates the transition from rapid, intuitive System-1 thinking to slower, more deliberate System-2 thinking in LLM-based agents. Experimental results on the GAIA benchmark demonstrate Sibyl’s superiority over existing state-of-the-art solutions, particularly when instantiated with GPT-4. This performance underscores the effectiveness of Sibyl’s innovative approach in addressing complex real-world tasks. As AI continues to evolve, Sibyl’s framework offers a promising path towards developing more capable and versatile LLM applications, potentially bridging the gap between current AI capabilities and the requirements of intricate, multi-step reasoning processes in real-world scenarios.
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