awesome-adaptation-agentic-ai

# Awesome Adaptation of Agentic AI > Skill by [ara.so](https://ara.so) — AI Agent Skills collection. ## Overview This repository is a curated research collection accompanying the paper "Adaptation of Agentic AI" (arXiv:2512.16301). It systematically organizes papers on how AI agents adapt their behavior, particularly focusing on: - **Agent Adaptation**: Methods to improve agent decision-making through tool execution signals (A1) or output signals (A2) - **Tool Adaptation**: Approaches to optimize tools either independently (T1) or with agent supervision (T2) The collection categorizes 40+ research papers by adaptation strategy, training method (RL, SFT, DPO), task domain, and implementation details. ## Repository Structure ``` awesome-adaptation-of-agentic-ai/ ├── README.md # Main paper collection with categorized tables ├── images/ │ ├── intro.png # Overview diagram │ ├── a1_illustrate.png # Tool execution signaled adaptation │ ├── a1_timeline.png # Development timeline │ ├── paper_icon.png # Paper link icons │ └── code_icon.png # Code link icons └── LICENSE ``` ## Installation & Usage ### Cloning the Repository ```bash # Clone the repository git clone https://github.com/pat-jj/Awesome-Adaptation-of-Agentic-AI.git cd Awesome-Adaptation-of-Agentic-AI # View the main README cat README.md ``` ### Browsing Papers The repository organizes papers into four main categories: 1. **A1: Tool Execution Signaled Agent Adaptation** - RL-based methods (GRPO, PPO, AlphaZero-like) - SFT & DPO methods 2. **A2: Agent Output Signaled Adaptation** - Coming in future updates 3. **T1: Agent-Agnostic Tool Adaptation** - Coming in future updates 4. **T2: Agent-Supervised Tool Adaptation** - Coming in future updates ## Key Research Categories ### A1: Tool Execution Signaled (RL-Based) Papers where agents learn from tool execution feedback using reinforcement learning: **Notable Methods:** - **Orion** (2025.11): IR agents with GRPO on LFM2 - **DeepSeek-R1-Zero** (2025.01): Coding agents with code executor feedback - **DeepSeek-Prover-V2** (2025.04): Formal theorem proving with Lean compiler - **FTRL** (2025.08): Multi-step tool-use with GRPO **Common Pattern:** ``` Agent → Tool Call → Execution Feedback → RL Update (GRPO/PPO) ``` ### A1: Tool Execution Signaled (SFT & DPO) Papers using supervised fine-tuning and direct preference optimization: **Notable Methods:** - **ToolLLM** (2023.07): API planning with real-world APIs - **RetPO** (2024.02): Information retrieval with DPO - **AWL** (2024.12): Scientific reasoning with adaptive learning **Common Pattern:** ``` Agent → Tool Call → Execution Trace → Supervised Learning ``` ## Common Use Cases ### Finding Papers by Task **Example: Formal Theorem Proving** ```bash # Search README for theorem proving papers grep -i "theorem proving" README.md ``` Papers include: AlphaProof, BFS-Prover-V2, Goedel-Prover-V2, Leanabell-Prover-V2, DeepSeek-Prover-V1.5/V2 **Example: Coding & Code Execution** ```bash # Find coding-related papers grep -i "coding\|code executor" README.md ``` Papers include: olmOCR2, R1-Code-Interpreter, Code-R1, DeepSeek-R1-Zero, RLEF, LeDex, CYCLE, CodeAct ### Finding Papers by Method **Example: GRPO (Group Relative Policy Optimization)** ```bash # List all GRPO papers grep "GRPO" README.md ``` Commonly used in: Tool-N1, DeepSeek-Prover-V2, SQL-R1, Rec-R1, DeepRetrieval, etc. **Example: DPO (Direct Preference Optimization)** ```bash # List all DPO papers grep "DPO" README.md ``` Used in: AWL, LeReT, TP-LLaMA, RetPO ### Finding Papers by Model Backbone **Example: Qwen2.5-based agents** ```bash # Find Qwen2.5 implementations grep "Qwen2.5" README.md ``` Models include: olmOCR2, ToolExpander, BFS-Prover-V2, WebGen-Agent, Tool-R1, etc. ## Accessing Paper Resources ### Paper Links All papers include arXiv or conference links: ```markdown [Paper](https://arxiv.org/abs/2512.16301) ``` ### Code Repositories Many papers provide implementation code: ```markdown [Code](https://github.com/example/repo) ``` ### Reading Strategy ```python # Pseudo-code for systematic review def research_adaptation_strategy(task_domain, method_type): """ Navigate to specific adaptation category Args: task_domain: e.g., "coding", "theorem proving", "IR" method_type: "RL", "SFT", "DPO" Returns: List of relevant papers with links """ # 1. Go to appropriate section (A1, A2, T1, T2) # 2. Filter by method_type (RL-based vs SFT/DPO) # 3. Search table for task_domain # 4. Check paper links, code availability, model backbones pass ``` ## Citation Format When using this repository for research: ```bibtex @article{jiang2025adaptation, title={Adaptation of Agentic AI}, author={Jiang, Pengcheng and Lin, Jiacheng and Shi, Zhiyi and Wang, Zifeng and He, Luxi and Wu, Yichen and Zhong, Ming and Song, Peiyang and Zhang, Qizheng and Wang, Heng and others}, journal={arXiv preprint arXiv:2512.16301}, year={2025} } ``` ## Contributing The repository welcomes pull requests for: - New papers on agentic AI adaptation - Updates to existing paper information - Corrections to categorizations - Additional metadata (benchmarks, datasets) **Contribution Pattern:** ```bash # 1. Fork the repository # 2. Add paper to appropriate category in README.md # 3. Follow existing table format: # | Time | Method | Venue | Task(s) | Tool(s) | Agent Backbone | Tuning | # Example entry: # | 2025.XX | YourMethod | Venue<br>🔗 [Paper](link)<br>💻 [Code](link) | Task | Tools | Model | Method | # 4. Submit pull request ``` ## Key Insights from the Collection ### Adaptation Taxonomy 1. **Signal Type**: - Tool execution feedback (A1) - Agent output quality (A2) 2. **Training Methods**: - **GRPO**: Group Relative Policy Optimization (most common in 2025) - **PPO**: Proximal Policy Optimization - **AlphaZero-like**: Self-play with value/policy networks - **SFT**: Supervised fine-tuning on execution traces - **DPO**: Direct preference optimization 3. **Trend**: Growing use of GRPO for tool-augmented agents (2025), especially with Qwen2.5 and DeepSeek models ### Domain Coverage - **Coding**: Code execution sandboxes (DeepSeek-R1-Zero, R1-Code-Interpreter) - **Formal Math**: Lean compilers (AlphaProof, DeepSeek-Prover) - **Information Retrieval**: Search engines, retrievers (DeepRetrieval, ReZero) - **Tool-Calling**: API execution (ToolLLM, Tool-N1, ToolExpander) - **Web Agents**: GUI interaction (WebGen-Agent) ## Troubleshooting ### Finding Specific Research **Q: How do I find papers using a specific model like LLaMA3?** ```bash grep -i "llama3" README.md ``` **Q: Which papers have open-source code?** ```bash # Look for code icon in tables grep "code_icon.png" README.md ``` **Q: What are the most recent papers?** ```bash # Check Time column (sorted by date within categories) # Most recent: 2025.11 (Orion), 2025.10 (olmOCR2, AlphaProof) ``` ### Understanding Abbreviations - **IR**: Information Retrieval - **GRPO**: Group Relative Policy Optimization - **PPO**: Proximal Policy Optimization - **SFT**: Supervised Fine-Tuning - **DPO**: Direct Preference Optimization - **TTRL**: Test-Time Reinforcement Learning - **EI**: Expert Iteration ### Repository Maintenance The repository is actively maintained with: - **Last Update**: 2026-05-15 (per metadata) - **Stars**: 650+ (growing at ~3 stars/day) - **Open Issues**: 2 For questions or issues, check: https://github.com/pat-jj/Awesome-Adaptation-of-Agentic-AI/issues ## Related Resources - **Homepage**: https://arxiv.org/abs/2512.16301 - **License**: CC BY-NC-ND 4.0 (content), NOASSERTION (code) - **Topics**: adaptation, agentic-ai, large-language-models