# T1: A Tool-Oriented Conversational Dataset for Multi-Turn Agentic Planning arXiv: https://arxiv.org/abs/2505.16986 · HTML: https://mlsanity.com/paper/2505.16986/ ## Key facts - Cited by 14 papers in the map (data through 14 Oct 2026). - Placed in Tool learning and function calling, one of 16 directions in LLM agents and tool use. - Builds on 22 of its 39 references that are in the map. - First posted on arXiv 22 May 2025. ## Placed in - LLM agents and tool use / [Tool learning and function calling](https://mlsanity.com/agents/tool-learning-and-function-calling/) ## Builds on (most cited first, 22 of 22) - [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022) - [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019) - [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023) - [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://mlsanity.com/paper/2308.08155/) (2023) - [ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs](https://mlsanity.com/paper/2307.16789/) (2023) - [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018) - [τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains](https://mlsanity.com/paper/2406.12045/) (2024) - [s1: Simple test-time scaling](https://mlsanity.com/paper/2501.19393/) (2025) - [Gorilla: Large Language Model Connected with Massive APIs](https://mlsanity.com/paper/2305.15334/) (2023) - [ALFWorld: Aligning Text and Embodied Environments for Interactive Learning](https://mlsanity.com/paper/2010.03768/) (2020) - [HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face](https://mlsanity.com/paper/2303.17580/) (2023) - [GAIA: a benchmark for General AI Assistants](https://mlsanity.com/paper/2311.12983/) (2023) - [Chameleon: Mixed-Modal Early-Fusion Foundation Models](https://mlsanity.com/paper/2405.09818/) (2024) - [API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs](https://mlsanity.com/paper/2304.08244/) (2023) - [LLMs Get Lost In Multi-Turn Conversation](https://mlsanity.com/paper/2505.06120/) (2025) - [ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases](https://mlsanity.com/paper/2306.05301/) (2023) - [TravelPlanner: A Benchmark for Real-World Planning with Language Agents](https://mlsanity.com/paper/2402.01622/) (2024) - [Auto-GPT for Online Decision Making: Benchmarks and Additional Opinions](https://mlsanity.com/paper/2306.02224/) (2023) - [ADaPT: As-Needed Decomposition and Planning with Language Models](https://mlsanity.com/paper/2311.05772/) (2023) - [Phi-4-reasoning Technical Report](https://mlsanity.com/paper/2504.21318/) (2025) - [GTA: A Benchmark for General Tool Agents](https://mlsanity.com/paper/2407.08713/) (2024) - [m&m's: A Benchmark to Evaluate Tool-Use for multi-step multi-modal Tasks](https://mlsanity.com/paper/2403.11085/) (2024) ## Built on by (most cited first, 14 of 14) - [A Survey of Context Engineering for Large Language Models](https://mlsanity.com/paper/2507.13334/) (2025) - [GTM: Simulating the World of Tools for AI Agents](https://mlsanity.com/paper/2512.04535/) (2025) - [CacheWise: Understanding Workloads and Optimizing KVCache Management for Efficiently Serving LLM Coding Agents](https://mlsanity.com/paper/2606.16824/) (2026) - [TraceCompiler: Skill-Guided Mining and Compilation of LLM Agent Traces into Mostly Deterministic Workflows](https://mlsanity.com/paper/2608.02680/) (2026) - [SEADialogues: A Multilingual Culturally Grounded Multi-turn Dialogue Dataset on Southeast Asian Languages](https://mlsanity.com/paper/2508.07069/) (2025) - [Feedback by Design: Understanding and Overcoming User Feedback Barriers in Conversational Agents](https://mlsanity.com/paper/2602.01405/) (2026) - [Empowering Real-World: A Survey on the Technology, Practice, and Evaluation of LLM-driven Industry Agents](https://mlsanity.com/paper/2510.17491/) (2025) - [SynAE: A Framework for Measuring the Quality of Synthetic Data for Tool-Calling Agent Evaluations](https://mlsanity.com/paper/2605.22564/) (2026) - [Momento: Evaluating Persistent Memory and Reasoning with Multi-Session Agentic Conversations](https://mlsanity.com/paper/2606.00832/) (2026) - [T1-Bench: Benchmarking Multi-Scenario Agents in Real-World Domains](https://mlsanity.com/paper/2606.11070/) (2026) - [ATOD: An Evaluation Framework and Benchmark for Agentic Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2601.11854/) (2026) - [Towards Scalable Customization and Deployment of Multi-Agent Systems for Enterprise Applications](https://mlsanity.com/paper/2606.18502/) (2026) - [Out of Sight: Compression-Aware Content Protection against Agentic Crawlers](https://mlsanity.com/paper/2607.08180/) (2026) - [PAUSE: A User-Centric Benchmark for Personal AI Assistants in Unified Service Environments](https://mlsanity.com/paper/2607.27354/) (2026) ## Abstract (arXiv) Large Language Models (LLMs) have demonstrated impressive capabilities as intelligent agents capable of solving complex problems. However, effective planning in scenarios involving dependencies between API or tool calls-particularly in multi-turn conversations-remains a significant challenge. To address this, we introduce T1, a tool-augmented, multi-domain, multi-turn conversational dataset specifically designed to capture and manage inter-tool dependencies across diverse domains. T1 enables rigorous evaluation of agents' ability to coordinate tool use across nine distinct domains (4 single domain and 5 multi-domain) with the help of an integrated caching mechanism for both short- and long-term memory, while supporting dynamic replanning-such as deciding whether to recompute or reuse cached results. Beyond facilitating research on tool use and planning, T1 also serves as a benchmark for evaluating the performance of open-weight and proprietary large language models. We present results powered by T1-Agent, highlighting their ability to plan and reason in complex, tool-dependent scenarios.