Awesome Agentic Reasoning

Skill by ara.so — AI Agent Skills collection.

What This Repository Provides

• 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

1. Foundational Agentic Reasoning

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

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

• 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

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

Benchmarks

• 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

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

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

Exploring Application Domains

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

## 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

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

• 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

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

• Email: twei10@illinois.edu, twli@illinois.edu, liu326@illinois.edu
• GitHub Issues: For suggestions and discussions

Key Research Paradigms

In-Context Reasoning vs. Post-Training Reasoning

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

• 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

• 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

• arXiv: https://arxiv.org/abs/2601.12538
• HuggingFace Papers: https://huggingface.co/papers/2601.12538

Using the Slides

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

# 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

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

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

@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

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

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

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

• 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

• 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

• 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

• Survey Paper: https://arxiv.org/abs/2601.12538
• Presentation Slides:

  materials/Agentic Reasoning Survey Talk.pdf

• HuggingFace: https://huggingface.co/papers/2601.12538
• License: MIT (open for use and contribution)

Quick Reference