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awesome-agentic-reasoning

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Updated 10-15 23:40

About this skill

Awesome Agentic Reasoning

Skill by ara.so — AI Agent Skills collection.

This skill provides expertise in navigating and utilizing the Awesome Agentic Reasoning repository — a comprehensive, curated collection of research papers and resources on agentic reasoning for Large Language Models (LLMs). The repository is based on the survey paper "Agentic Reasoning for Large Language Models: A Survey" and organizes cutting-edge research into foundational reasoning, self-evolving systems, and multi-agent collaboration.

What This Repository Provides

The Awesome Agentic Reasoning repository offers:

  • Categorized Research Papers: Organized by thematic areas including planning, tool use, search, self-evolution, multi-agent systems, and real-world applications
  • Benchmarks: Comprehensive lists of evaluation frameworks for agentic reasoning capabilities
  • Three-Layer Framework:
    • Foundational Reasoning: Core single-agent abilities (planning, tool-use, search)
    • Self-Evolving Reasoning: Adaptation through feedback, memory, and learning
    • Collective Reasoning: Multi-agent coordination and collaborative intelligence
  • Application Domains: Math/coding agents, scientific discovery, embodied agents, healthcare, web exploration
  • Survey Materials: Slides and the comprehensive survey paper

Repository Structure

Awesome-Agentic-Reasoning/
├── README.md                          # Main curated list
├── CONTRIBUTING.md                    # Contribution guidelines
├── materials/                         # Survey slides and materials
│   └── Agentic Reasoning Survey Talk.pdf
└── figs/                             # Framework diagrams
    ├── overview.png
    └── planning.png

Navigating the Repository

Main Categories

The repository organizes papers into three primary layers:

1. Foundational Agentic Reasoning

Planning Reasoning:

  • In-context Planning (workflow design, tree search)
  • Post-training Planning (supervised fine-tuning, reinforcement learning)

Tool-Use Optimization:

  • In-context Tool-Use (API orchestration, workflow design)
  • Post-training Tool-Use (supervised learning, RL fine-tuning)

Agentic Search:

  • In-context Search (web navigation, knowledge retrieval)
  • Post-training Search (RL optimization)

2. Self-Evolving Agentic Reasoning

  • Agentic Feedback Mechanisms: Self-reflection, critique, and iterative refinement
  • Agentic Memory: Short-term and long-term memory systems
  • Evolving Foundational Capabilities: Continuous improvement of planning, tool-use, and search

3. Collective Multi-Agent Reasoning

  • Role Taxonomy: Debate, collaboration, hierarchical structures
  • Collaboration Patterns: Division of labor, coordination strategies
  • Multi-Agent Memory and Evolution: Shared knowledge, collective learning

Applications

The repository covers real-world applications:

  • 💻 Math Exploration & Coding Agents
  • 🔬 Scientific Discovery Agents
  • 🤖 Embodied Agents
  • 🏥 Healthcare & Medicine Agents
  • 🌐 Autonomous Web Exploration & Research Agents

Benchmarks

Organized by:

  • Core Mechanisms: Tool Use, Search, Memory & Planning, Multi-Agent Systems
  • Application Domains: Embodied, Scientific Discovery, Medical, Web, General Tool-Use

Usage Patterns

Finding Papers on Specific Topics

Example 1: Finding Planning Papers

Navigate to the Planning Reasoning section to find papers on:

  • Workflow design approaches (ReAct, ReWOO, Plan-and-Solve)
  • Tree search methods (Tree of Thoughts, MCTS-based approaches)
  • Post-training planning optimization

Example 2: Multi-Agent System Research

The Collective Multi-Agent Reasoning section includes:

  • Role specialization papers
  • Collaboration frameworks
  • Multi-agent memory systems

Exploring Application Domains

Example: Embodied Agent Research

  1. Check the Applications > Embodied Agents section
  2. Cross-reference with Benchmarks > Embodied Agents for evaluation frameworks
  3. Review foundational papers on planning and tool-use that apply to embodied settings

Finding Benchmarks

Example: Evaluating Tool-Use Capabilities

## Tool Use Benchmarks

Navigate to: Benchmarks > Core Mechanisms > Tool Use

Key benchmarks include:
- API-Bank: API selection and execution
- ToolBench: Multi-tool orchestration
- T-Eval: Tool learning evaluation

Contributing to the Repository

Adding New Papers

Create a pull request with papers organized by category:

| [Paper Title](https://arxiv.org/abs/XXXX.XXXXX) | Conference/Year |

Guidelines:

  • Place papers in the appropriate thematic section
  • Follow the existing table format
  • Include the full arXiv link or conference proceedings URL
  • Add the publication year or venue

Suggesting Resources

Open an issue to suggest:

  • New paper categories
  • Additional benchmarks
  • Application domains not yet covered
  • Survey materials or tutorials

Contact:

Key Research Paradigms

In-Context Reasoning vs. Post-Training Reasoning

The repository distinguishes between two optimization approaches:

In-Context Reasoning:

  • Test-time scaling through structured orchestration
  • Adaptive workflows without parameter updates
  • Examples: ReAct, Tree of Thoughts, Chain-of-Thought prompting

Post-Training Reasoning:

  • Behavior optimization via RL and supervised fine-tuning
  • Parameter updates to internalize reasoning strategies
  • Examples: RLHF for tool-use, Q-learning for planning

Environmental Dynamics

Papers are organized by the environmental setting:

  • Static environments: Fixed tool sets, deterministic outcomes
  • Dynamic environments: Feedback loops, adaptation requirements
  • Multi-agent environments: Coordination, communication, emergent behavior

Working with Survey Materials

Accessing the Survey Paper

The foundational survey is available at:

Using the Slides

Presentation materials are in materials/Agentic Reasoning Survey Talk.pdf:

  • Framework overview
  • Key insights from each reasoning layer
  • Application case studies
  • Future research directions

Common Patterns

Building a Research Bibliography

Pattern: Comprehensive Literature Review

# Pseudo-code for extracting papers by category

categories = [
    "Planning Reasoning",
    "Tool-Use Optimization", 
    "Agentic Search",
    "Multi-Agent Systems"
]

papers_by_category = {}

for category in categories:
    # Navigate to README section
    papers = extract_papers_from_section(category)
    papers_by_category[category] = papers
    
# Generate BibTeX or reading list

Tracking New Research

Pattern: Monitoring Updates

The repository is actively maintained. To stay current:

  1. Watch the repository for updates
  2. Check the News section in README for announcements
  3. Review recent commits for newly added papers
  4. Subscribe to GitHub notifications

Cross-Referencing Applications and Benchmarks

Pattern: Application-Specific Research

For a specific application domain:

1. Identify application section (e.g., "Healthcare & Medicine Agents")
2. Review papers in that section
3. Navigate to corresponding benchmark section
4. Check foundational techniques used (planning, tool-use, etc.)
5. Trace back to foundational reasoning sections for core methods

Citation

When using this repository in research or projects:

@article{wei2026agentic,
  title={Agentic Reasoning for Large Language Models},
  author={Wei, Tianxin and Li, Ting-Wei and Liu, Zhining and Ning, Xuying and Yang, Ze and Zou, Jiaru and Zeng, Zhichen and Qiu, Ruizhong and Lin, Xiao and Fu, Dongqi and others},
  journal={arXiv preprint arXiv:2601.12538},
  year={2026}
}

Integration with Development Workflows

For Researchers

Literature Review Workflow:

  1. Clone the repository for offline access
  2. Use the categorized structure to identify relevant papers
  3. Cross-reference applications with foundational techniques
  4. Export citations for reference management tools

For Practitioners

Implementation Workflow:

  1. Identify your application domain (e.g., web agents, coding)
  2. Review application-specific papers and benchmarks
  3. Trace foundational techniques (planning, tool-use)
  4. Reference implementation papers for code patterns
  5. Evaluate using suggested benchmarks

For Tool Builders

Benchmark Selection:

  1. Determine core capability (planning, tool-use, search)
  2. Navigate to corresponding benchmark section
  3. Review evaluation frameworks and metrics
  4. Compare agent performance across standard benchmarks

Best Practices

Exploring New Topics

  1. Start with the Overview: Read the survey paper introduction and framework diagram
  2. Navigate by Layer: Begin with foundational reasoning before advanced topics
  3. Cross-Reference: Link application papers back to foundational techniques
  4. Check Benchmarks: Understand evaluation standards for each capability

Contributing Quality Additions

  1. Verify Relevance: Ensure papers fit the agentic reasoning scope
  2. Check Duplicates: Search existing entries before adding
  3. Provide Context: Include venue/year information
  4. Follow Format: Maintain consistent table structure

Staying Current

  1. Monitor Commits: The repository updates regularly with new papers
  2. Check News Section: Major updates announced at the top of README
  3. Watch Discussions: GitHub issues may highlight emerging trends
  4. Follow Survey Updates: Authors plan continued improvements

Troubleshooting

Finding Specific Papers

Issue: Can't locate a specific paper

Solution:

  • Use browser search (Ctrl+F / Cmd+F) on the README
  • Check multiple related sections (papers may fit several categories)
  • Review the benchmarks section for evaluation-focused papers
  • Check recent commits if it's a new publication

Understanding Categories

Issue: Unclear which section contains relevant papers

Solution:

  • Refer to the framework overview diagram
  • Read the category descriptions in the survey paper
  • Cross-reference with similar known papers
  • Check application sections if domain-specific

Accessing Papers

Issue: Links not working or papers behind paywalls

Solution:

  • Most papers link to arXiv versions (open access)
  • For conference papers, search on Google Scholar
  • Check author websites for preprints
  • Use institutional access for published versions

Related Resources

Quick Reference

Category Key Papers Benchmarks
Planning Tree of Thoughts, ReAct, Plan-and-Solve PlanBench, BlocksWorld
Tool-Use Gorilla, ToolLLM, HuggingGPT API-Bank, ToolBench
Search WebGPT, Agent-E, Mind2Web WebArena, GAIA
Multi-Agent ChatDev, AgentVerse, MetaGPT MAgIC, AgentBench
Embodied LM-Nav, PERIA, RT-1 CALVIN, MetaWorld
Scientific FunSearch, AI Scientist ScienceBench

This skill enables AI coding agents to effectively navigate and utilize the Awesome Agentic Reasoning repository, helping developers access cutting-edge research on LLM-based agents, understand agentic reasoning frameworks, and apply state-of-the-art techniques to their projects.