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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.01434 |
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| _version_ | 1866915646965547008 |
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| author | Xavier, Daull Bellot, Patrice Bruno, Emmanuel Martin, Vincent Murisasco, Elisabeth |
| author_facet | Xavier, Daull Bellot, Patrice Bruno, Emmanuel Martin, Vincent Murisasco, Elisabeth |
| contents | We introduce CollabToolBuilder, a flexible multiagent LLM framework with expert-in-the-loop (HITL) guidance that iteratively learns to create tools for a target goal, aligning with human intent and process, while minimizing time for task/domain adaptation effort and human feedback capture. The architecture generates and validates tools via four specialized agents (Coach, Coder, Critic, Capitalizer) using a reinforced dynamic prompt and systematic human feedback integration to reinforce each agent's role toward goals and constraints. This work is best viewed as a system-level integration and methodology combining multi-agent in-context learning, HITL controls, and reusable tool capitalization for complex iterative problems such as scientific document generation. We illustrate it with preliminary experiments (e.g., generating state-of-the-art research papers or patents given an abstract) and discuss its applicability to other iterative problem-solving. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_01434 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Flexible Multi-Agent LLM-Human Framework for Fast Human Validated Tool Building Xavier, Daull Bellot, Patrice Bruno, Emmanuel Martin, Vincent Murisasco, Elisabeth Artificial Intelligence We introduce CollabToolBuilder, a flexible multiagent LLM framework with expert-in-the-loop (HITL) guidance that iteratively learns to create tools for a target goal, aligning with human intent and process, while minimizing time for task/domain adaptation effort and human feedback capture. The architecture generates and validates tools via four specialized agents (Coach, Coder, Critic, Capitalizer) using a reinforced dynamic prompt and systematic human feedback integration to reinforce each agent's role toward goals and constraints. This work is best viewed as a system-level integration and methodology combining multi-agent in-context learning, HITL controls, and reusable tool capitalization for complex iterative problems such as scientific document generation. We illustrate it with preliminary experiments (e.g., generating state-of-the-art research papers or patents given an abstract) and discuss its applicability to other iterative problem-solving. |
| title | A Flexible Multi-Agent LLM-Human Framework for Fast Human Validated Tool Building |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2512.01434 |