This repository curates research on "Interleaving Reasoning," a novel paradigm for AI systems that demonstrates superior performance on complex tasks compared to standard reasoning methods. It aims to guide the development of next-generation reasoning systems essential for achieving Artificial General Intelligence (AGI). The project targets researchers and engineers seeking to advance AI reasoning capabilities.

How It Works

Interleaving Reasoning is characterized by multi-turn interactions and sophisticated reasoning dynamics, contrasting with the single-pass inference of standard models. The repository categorizes this approach into four key areas: Multimodal Interleaving Reasoning (processing diverse data types), Multi-Round Acting Interleaving Reasoning (iterative environmental interaction), Multi-Agent Interleaving Reasoning (collaborative or competitive agent dynamics), and Unified Understanding and Generation Interleaving Reasoning (generating multimodal outputs as intermediate steps). This structured taxonomy facilitates a deeper understanding and development of more robust reasoning systems.

Quick Start & Requirements

This repository serves as a curated collection of research papers, projects, and datasets related to Interleaving Reasoning. It does not provide direct installation instructions, executable code, or specific system requirements. Users are directed to individual project links for setup details.

Highlighted Details

• Comprehensive Taxonomy: Offers a detailed classification of Interleaving Reasoning, including Multimodal, Multi-Round Acting, Multi-Agent, and Unified Understanding/Generation paradigms.
• Extensive Research Curation: Compiles a broad list of relevant papers, projects, and datasets, categorized by reasoning type and associated techniques (e.g., SFT, RL, Prompting).
• AGI Focus: Positions Interleaving Reasoning as a critical advancement towards Artificial General Intelligence (AGI).
• Key Examples: References prominent models and research such as OpenAI o1, o3, Deepseek-R1, DeepEyes, VITAL, ReAct, and MetaGPT.

Maintenance & Community

The project is explicitly marked as "in progress." It lists originators (Wenxuan Huang, Zhenfei Yin) and members. While it mentions organizing an ICCV 2025 Workshop MMRAgi, no external community links (e.g., Discord, Slack) are provided.

Licensing & Compatibility

No specific licensing information is present in the provided README content. This lack of clarity may pose compatibility concerns for commercial use or integration into closed-source projects.

Limitations & Caveats

The repository is an evolving collection of research pointers rather than a runnable codebase, lacking direct installation or usage instructions. The absence of explicit licensing information is a significant caveat for potential adopters. The "work in progress" status indicates ongoing development and potential for changes.