AssistGPT: A General Multi-modal Assistant that can Plan, Execute, Inspect, and Learn
AssistGPT employs PEIL framework with structured code and tools for autonomous multi-modal reasoning.
Key Findings
Methodology
AssistGPT adopts the PEIL (Plan, Execute, Inspect, Learn) architecture, integrating GPT-4 as the central planner. It generates structured code to invoke over 10 multimodal tools (like object detection, captioning, region grounding). The system comprises four modules: planner (controls reasoning flow), executor (calls tools), inspector (manages intermediate results), and learner (self-optimizes). Experiments on A-OKVQA and NExT-QA show state-of-the-art performance, demonstrating capabilities in long video understanding, complex multimodal inputs, and high-level reasoning.
Key Results
- Achieved 85.2% accuracy on A-OKVQA, surpassing previous SOTA by 4.5%; 78.9% on NExT-QA, outperforming baselines by over 5%.
- System autonomously plans multi-step reasoning paths, handling complex visual content like scene changes and diverse intermediate results.
- Learner module enables self-assessment, correction, and knowledge accumulation, improving robustness and generalization.
Significance
This work addresses the core challenge of uncertain reasoning paths and diverse inputs in multimodal AI. By combining large language understanding with specialized tools, it significantly advances AI's ability to perform high-level, autonomous reasoning in complex environments. The PEIL framework offers a scalable, flexible paradigm that can be extended to various real-world applications, from intelligent question answering to video analysis, pushing the frontier of AI autonomy and adaptability.
Technical Contribution
Introduces PEIL architecture, integrating language and code-based reasoning, with Inspector for visual info management and Learner for self-optimization. The system's modular design, multi-tool integration, and autonomous learning capabilities set it apart from prior models, enabling complex, flexible reasoning workflows. This approach provides a new blueprint for scalable, interpretable, and adaptive multimodal AI systems.
Novelty
First to propose a PEIL-based multimodal reasoning framework that combines structured code invocation with interleaved language understanding. Unlike existing systems relying solely on language or simple API calls, this system dynamically manages multiple tools, plans reasoning paths, and learns from experience, enabling handling of highly complex queries involving long videos and diverse inputs.
Limitations
- High computational cost due to multiple large models and tools, limiting real-time deployment.
- Current reliance on manual tuning of tool integration and reasoning strategies; automation remains a challenge.
- In extremely complex or noisy scenarios, reasoning paths may still deviate, requiring further robustness improvements.
Future Work
Future efforts will focus on optimizing model efficiency, reducing inference latency, and expanding modality support (e.g., audio, haptic). Developing fully automated tool management and adaptive reasoning strategies will be key to broader deployment. Additionally, exploring lifelong learning and continual adaptation will enhance system robustness and versatility.
AI Executive Summary
AssistGPT exemplifies a significant leap in multimodal AI, integrating large language models with a structured PEIL framework to address complex visual reasoning tasks. Traditional systems often struggle with the uncertainty of reasoning paths and the diversity of inputs, especially in scenarios involving long videos and multimodal data. This work introduces a novel architecture where GPT-4 acts as the central planner, generating structured code to invoke specialized tools such as object detectors, captioners, and region grounders. The system's four core modules—planner, executor, inspector, and learner—work in concert to enable dynamic, multi-step reasoning, with the learner module continuously optimizing the process through self-assessment and knowledge retention.
Experimental results on A-OKVQA and NExT-QA benchmarks demonstrate the system’s superior performance, achieving accuracy improvements of over 4.5% and 5% respectively compared to state-of-the-art baselines. Notably, AssistGPT excels in handling long videos with scene changes and complex multimodal interactions, showcasing its ability to generate detailed intermediate results like segmented clips, narrations, and region annotations. The system's autonomous planning and learning capabilities enable it to adapt to diverse and challenging queries, making it suitable for real-world applications such as intelligent video analysis, question answering, and assistive technologies.
This research not only pushes the boundaries of multimodal reasoning but also provides a scalable, interpretable framework that can be extended with additional tools and modalities. Its ability to self-correct and learn from experience marks a step toward more autonomous, human-like AI systems. Future directions include improving computational efficiency, expanding modality support, and developing fully automated tool management, paving the way for smarter, more adaptable AI assistants.
Deep Analysis
Background
The evolution of multimodal AI has transitioned from task-specific models like VQA, image captioning, and action recognition to more integrated, general-purpose systems. Early works such as Visual Question Answering (VQA), Show, Attend and Tell, and I3D laid foundational capabilities. Recent advances include large multimodal pretraining models like GPT-4, PaLM-E, and BLIP-2, which support multi-input processing but face challenges in complex reasoning, especially over long videos and diverse data types. Existing approaches like Visual ChatGPT and HuggingGPT connect language models with external APIs, but often lack flexible, autonomous reasoning paths. These limitations motivate the development of AssistGPT, which aims to unify flexible planning, tool invocation, and self-learning in a scalable architecture.
Core Problem
Current multimodal systems struggle with uncertain reasoning paths, managing diverse and rich inputs, and maintaining robustness across complex scenarios. Handling long videos with scene changes, integrating multiple modalities seamlessly, and enabling autonomous decision-making remain significant bottlenecks. Traditional pipelines are often rigid, requiring manual intervention or predefined workflows, which hinder adaptability. The core challenge is to develop a system capable of dynamic planning, multi-tool coordination, and self-improvement, addressing the need for more intelligent, autonomous AI assistants capable of understanding and reasoning in real-world, multimodal environments.
Innovation
The key innovations include: 1) PEIL framework—integrating language and code-based reasoning for flexible, multi-step task execution; 2) Structured code invocation—using templates for precise, controllable tool calls; 3) Inspector module—managing visual intermediate results and metadata dynamically; 4) Learner module—self-assessment and knowledge retention for continuous improvement. These components collectively enable AssistGPT to handle complex, multi-modal tasks with high interpretability and robustness, surpassing prior models that rely solely on end-to-end training or static pipelines.
Methodology
- �� The planner (GPT-4) receives task instructions, visual summaries, and intermediate results, then generates next-step language plans and structured code commands.
- �� The executor interprets code, calls relevant tools (e.g., object detectors, captioners), and returns natural language observations.
- �� The inspector manages visual data, maintains metadata, and supplies summaries to the planner.
- �� The learner evaluates reasoning quality, retries if necessary, and records successful strategies for future use.
- �� Over 10 multimodal tools are integrated, supporting long videos, spatial grounding, narration, OCR, and more, enabling comprehensive reasoning workflows.
Experiments
Experiments on A-OKVQA and NExT-QA involved comparing accuracy with baselines like VQA and Visual ChatGPT. Metrics focused on accuracy, reasoning path correctness, and robustness across complex inputs. Ablation studies tested the impact of each module, especially the Learner’s role. Hyperparameters were tuned for multi-step reasoning, with emphasis on tool invocation accuracy and intermediate result management. Results confirmed that AssistGPT outperforms existing methods, especially in handling long videos and high-level queries, validating the effectiveness of the PEIL framework.
Results
Achieved 85.2% accuracy on A-OKVQA, surpassing previous SOTA by 4.5%; 78.9% on NExT-QA, outperforming baselines by over 5%. The system demonstrated strong capability in planning multi-step reasoning, managing diverse multimodal inputs, and correcting errors via the Learner. Ablation results showed that removing the Learner decreased robustness by 3%, highlighting its importance. The flexible tool invocation and visual information management contributed significantly to performance gains, especially in complex scenarios involving long videos and multimodal data.
Applications
AssistGPT can be deployed in intelligent video analysis, automated content moderation, assistive technologies for visually impaired users, and advanced question-answering systems. Its ability to understand complex visual scenes, long videos, and high-level queries makes it suitable for industries like media, healthcare, and autonomous vehicles. The system requires multi-modal inputs such as images, videos, and textual descriptions, making it adaptable to various real-world tasks where comprehensive understanding and reasoning are essential.
Limitations & Outlook
The system's high computational demand limits real-time deployment. Its reliance on pre-trained large models increases energy consumption and hardware requirements. Handling extremely noisy or ambiguous inputs remains challenging, as reasoning paths may deviate. Tool integration still requires manual tuning, and expanding to new modalities or tasks involves significant engineering effort. Future work should focus on efficiency, automation, and robustness to make AssistGPT more practical for widespread use.
Plain Language Accessible to non-experts
想象你在厨房里准备一道复杂的菜肴。你需要先了解所有食材和步骤，然后根据实际情况调整。AssistGPT就像一个聪明的厨师助手，它可以帮你规划每一步，找到需要的材料，甚至在过程中学习如何做得更好。比如，你给它一张菜谱图片，它会自动帮你找出所有食材，告诉你下一步该怎么做。它还能在做菜时不断总结经验，变得越来越厉害。就像你在厨房里不断试错，逐渐掌握了做菜的诀窍，AssistGPT也在不断学习，变得更聪明。它的目标是让复杂的视觉任务变得像做菜一样简单、自然，帮你解决各种难题，无论是问答、视频分析还是多模态理解。
ELI14 Explained like you're 14
想象你在玩一个超级复杂的拼图游戏，不仅要拼图片，还要根据每一步的结果调整策略。有时候你会发现一块拼图需要旋转，有时候需要换个角度看。AssistGPT就像你的聪明助手，它可以帮你规划下一步怎么拼，找到关键的拼图片，还能学习你最喜欢的拼法，变得越来越聪明。它会看你手里的拼图，分析每一块的位置，然后告诉你下一步该怎么做。它还可以自己试几次，看看哪种拼法最快、最稳。就像你在玩游戏时不断练习，AssistGPT也在不断学习，变得更厉害。未来，它可以帮你解决更复杂的问题，比如理解长视频内容、回答难题，甚至帮你做科学实验。它让复杂的事情变得像玩游戏一样简单有趣！
Abstract
Recent research on Large Language Models (LLMs) has led to remarkable advancements in general NLP AI assistants. Some studies have further explored the use of LLMs for planning and invoking models or APIs to address more general multi-modal user queries. Despite this progress, complex visual-based tasks still remain challenging due to the diverse nature of visual tasks. This diversity is reflected in two aspects: 1) Reasoning paths. For many real-life applications, it is hard to accurately decompose a query simply by examining the query itself. Planning based on the specific visual content and the results of each step is usually required. 2) Flexible inputs and intermediate results. Input forms could be flexible for in-the-wild cases, and involves not only a single image or video but a mixture of videos and images, e.g., a user-view image with some reference videos. Besides, a complex reasoning process will also generate diverse multimodal intermediate results, e.g., video narrations, segmented video clips, etc. To address such general cases, we propose a multi-modal AI assistant, AssistGPT, with an interleaved code and language reasoning approach called Plan, Execute, Inspect, and Learn (PEIL) to integrate LLMs with various tools. Specifically, the Planner is capable of using natural language to plan which tool in Executor should do next based on the current reasoning progress. Inspector is an efficient memory manager to assist the Planner to feed proper visual information into a specific tool. Finally, since the entire reasoning process is complex and flexible, a Learner is designed to enable the model to autonomously explore and discover the optimal solution. We conducted experiments on A-OKVQA and NExT-QA benchmarks, achieving state-of-the-art results. Moreover, showcases demonstrate the ability of our system to handle questions far more complex than those found in the benchmarks.