JTPRO: A Joint Tool-Prompt Reflective Optimization Framework for Language Agents
cs.AI, cs.SE
Submitted: 2026-04-20
Updated: 2026-04-20
Terminology
Sources
- GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning
- ReTool: Reinforcement Learning for Strategic Tool Use in LLMs
- Feedback Descent: Open-Ended Text Optimization via Pairwise Comparison
- ToolACE: Winning the Points of LLM Function Calling
- HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face
- ToolRL: Reward is All Tool Learning Needs
- MT-OSC: Path for LLMs that Get Lost in Multi-Turn Conversation
- Tool Learning with Foundation Models
- RestGPT: Connecting Large Language Models with Real-World RESTful APIs
- AutoPDL: Automatic Prompt Optimization for LLM Agents
- ReWOO: Decoupling Reasoning from Observations for Efficient Augmented Language Models
- Dynamic Cheatsheet: Test-Time Learning with Adaptive Memory
- CRAFT: Customizing LLMs by Creating and Retrieving from Specialized Toolsets
- Maestro: Joint Graph & Config Optimization for Reliable AI Agents
- Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models
- DiffuMask: Diffusion Language Model for Token-level Prompt Pruning
- Structure-aware Fine-tuning for Code Pre-trained Models
- ToolRerank: Adaptive and Hierarchy-Aware Reranking for Tool Retrieval
- Seal-Tools: Self-Instruct Tool Learning Dataset for Agent Tuning and Detailed Benchmark
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