Image de couverture de livre

Machine Learning as a Tool (MLAT): A Framework for Integrating Statistical ML Models as Callable Tools within LLM Agent Workflows

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Chen, Edwin, Bibi, Zulekha
Format:	Preprint
Publié:	2026
Sujets:	Machine Learning Artificial Intelligence
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

ML-Tool-Bench: Tool-Augmented Planning for ML Tasks
par: Chittepu, Yaswanth, et autres
Publié: (2025)

ML-Agent: Reinforcing LLM Agents for Autonomous Machine Learning Engineering
par: Liu, Zexi, et autres
Publié: (2025)

SynthTools: A Framework for Scaling Synthetic Tools for Agent Development
par: Castellani, Tommaso, et autres
Publié: (2025)

A Survey on Data Quality Dimensions and Tools for Machine Learning
par: Zhou, Yuhan, et autres
Publié: (2024)

Incentivizing Agentic Reasoning in LLM Judges via Tool-Integrated Reinforcement Learning
par: Xu, Ran, et autres
Publié: (2025)

LLM Agents Making Agent Tools
par: Wölflein, Georg, et autres
Publié: (2025)

Reward Hacking Benchmark: Measuring Exploits in LLM Agents with Tool Use
par: Thaman, Kunvar
Publié: (2026)

Understanding Tool-Integrated Reasoning
par: Lin, Heng, et autres
Publié: (2025)

Tool-Star: Empowering LLM-Brained Multi-Tool Reasoner via Reinforcement Learning
par: Dong, Guanting, et autres
Publié: (2025)

Exploring Multi-Modal Data with Tool-Augmented LLM Agents for Precise Causal Discovery
par: Shen, ChengAo, et autres
Publié: (2024)

RLFactory: A Plug-and-Play Reinforcement Learning Post-Training Framework for LLM Multi-Turn Tool-Use
par: Chai, Jiajun, et autres
Publié: (2025)

PLAY2PROMPT: Zero-shot Tool Instruction Optimization for LLM Agents via Tool Play
par: Fang, Wei, et autres
Publié: (2025)

Data Pipeline Training: Integrating AutoML to Optimize the Data Flow of Machine Learning Models
par: Wu, Jiang, et autres
Publié: (2024)

The Causal Impact of Tool Affordance on Safety Alignment in LLM Agents
par: Yu, Shasha, et autres
Publié: (2026)

AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML
par: Trirat, Patara, et autres
Publié: (2024)

WALT: Web Agents that Learn Tools
par: Prabhu, Viraj, et autres
Publié: (2025)

Outcome-Aware Tool Selection for Semantic Routers: Latency-Constrained Learning Without LLM Inference
par: Chen, Huamin, et autres
Publié: (2026)

Tool Learning with Foundation Models
par: Qin, Yujia, et autres
Publié: (2023)

AutoML-Med: A Framework for Automated Machine Learning in Medical Tabular Data
par: Francia, Riccardo, et autres
Publié: (2025)

ToolRL: Reward is All Tool Learning Needs
par: Qian, Cheng, et autres
Publié: (2025)

Tool-as-Interface: Learning Robot Policies from Observing Human Tool Use
par: Chen, Haonan, et autres
Publié: (2025)

TxAgent: An AI Agent for Therapeutic Reasoning Across a Universe of Tools
par: Gao, Shanghua, et autres
Publié: (2025)

Reinforcement Learning for Tool-Calling Agents in Fast Healthcare Interoperability Resources (FHIR)
par: Knorr, Marius S., et autres
Publié: (2026)

Provable Benefits of In-Tool Learning for Large Language Models
par: Houliston, Sam, et autres
Publié: (2025)

Large Language Models for Constructing and Optimizing Machine Learning Workflows: A Survey
par: Gu, Yang, et autres
Publié: (2024)

When Does Multi-Agent RL Improve LLM Workflows? Workflow, Scale, and Policy-Sharing Tradeoffs
par: Zeng, Yifan, et autres
Publié: (2026)

CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning
par: Wu, Duo, et autres
Publié: (2024)

PALADIN: Self-Correcting Language Model Agents to Cure Tool-Failure Cases
par: Vuddanti, Sri Vatsa, et autres
Publié: (2025)

Current Agents Fail to Leverage World Model as Tool for Foresight
par: Qian, Cheng, et autres
Publié: (2026)

How Many Tools Should an LLM Agent See? A Chance-Corrected Answer
par: Repantis, Vyzantinos, et autres
Publié: (2026)

LoopTool: Closing the Data-Training Loop for Robust LLM Tool Calls
par: Zhang, Kangning, et autres
Publié: (2025)

Recoverability Has a Law: The ERR Measure for Tool-Augmented Agents
par: Vuddanti, Sri Vatsa, et autres
Publié: (2026)

TAACKIT: Track Annotation and Analytics with Continuous Knowledge Integration Tool
par: Lee, Lily, et autres
Publié: (2024)

ToolSandbox: A Stateful, Conversational, Interactive Evaluation Benchmark for LLM Tool Use Capabilities
par: Lu, Jiarui, et autres
Publié: (2024)

ToolACE-R: Model-aware Iterative Training and Adaptive Refinement for Tool Learning
par: Zeng, Xingshan, et autres
Publié: (2025)

Many-to-English Machine Translation Tools, Data, and Pretrained Models
par: Gowda, Thamme, et autres
Publié: (2021)

EnvScaler: Scaling Tool-Interactive Environments for LLM Agent via Programmatic Synthesis
par: Song, Xiaoshuai, et autres
Publié: (2026)

A Comprehensive Perspective on Explainable AI across the Machine Learning Workflow
par: Paterakis, George, et autres
Publié: (2025)

rule4ml: An Open-Source Tool for Resource Utilization and Latency Estimation for ML Models on FPGA
par: Rahimifar, Mohammad Mehdi, et autres
Publié: (2024)

FairAgent: Democratizing Fairness-Aware Machine Learning with LLM-Powered Agents
par: Dai, Yucong, et autres
Publié: (2025)