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:

• 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

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:

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

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

• 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

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.