arxiv:2611.05036

ToolSpace: Scaling Tool-Centric Environment Synthesis for Long-Horizon Agents

Published on Nov 3

· Submitted by

KABI on Nov 4

Renmin University of China

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Authors:

Xin Deng ,

Abstract

ToolSpace constructs realistic multi-environment tool-use benchmarks by mining execution traces, clustering tool compositions, and validating tasks through replayable state-aware random walks.

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Large language models are deployed as long-horizon agents that compose diverse tools across MCP services into executable workflows. Existing MCP environment-scaling methods are data-centric: they first synthesize a database and scenario, then derive tools and tasks from them. As a result, tool interfaces may not match real ones, tool compositions are not grounded in execution, and tasks rarely span more than one environment. We propose ToolSpace, a tool-centric framework that reverses this order. ToolSpace mines data, state, and retry dependencies from adjacent calls in execution traces of real MCP services and open agent corpora, using deterministic rules rather than LLM judgment, and clusters the resulting weighted tool-composition graph into environments. Databases are synthesized only for state-mutating environments, while query and stateless tools replay recorded returns. ToolSpace then performs state-aware random walks with rollback and decompiles each chain into a natural-language task with assertion-based checks, keeping only samples that replay successfully from the initial state. Applied to AutomationBench and tau3-Bench Banking, the pipeline produces harder Pro versions on which 7 frontier models drop by up to 38.2 points. Across seven long-horizon tool-use benchmarks, ToolSpace raises Qwen3.6-35B-A3B from 23.0 to 41.0 average score (43.2 with RL) and GLM-5.2 from 44.6 to 56.6, on par with Claude Opus 5. Ablations show that execution-witnessed edges clearly outperform LLM-judged ones, and performance keeps improving with data and environment scale.

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dongguanting

Paper submitter about 10 hours ago

Happy to share our latest work on agent environment synthesis:

🌟 ToolSpace: Scaling Tool-Centric Environment Synthesis for Long-Horizon Agents

Most existing environment synthesis methods are data-centric: they first build the underlying database, then synthesize tools and tasks. Tool interfaces easily drift from reality, and it is hard to form tasks that span environments.

ToolSpace takes the opposite approach. It first collects tools from real call traces, then chains real tool sets into a tool universe, and finally synthesizes the underlying database on demand. This preserves authenticity while flexibly generating verifiable tasks across any environment × any tool.

💡 Key methods
1️⃣ Tool universe exploration: mine tool dependencies from real traces and link them into a tool-world graph; synthesize databases only for environments that change state.
2️⃣ Verifiable task synthesis: walk the graph with state-aware traversal and backtracking, decompile executable tool chains into tasks and verification scripts, and keep environment state continuous. The same chain can yield high-quality SFT data and mixed-environment RL data.
3️⃣ Harder evaluation benchmarks: ToolSpace seamlessly upgrades two frontier benchmarks into AutomationBench Pro and τ³-Bench Banking Pro. Strong existing models all score below 40, setting a harder bar for the field.

📊 Experiments
✅ We evaluate ToolSpace-8B, 35B, and 744B on 7 long-horizon tool benchmarks. ToolSpace-744B averages 56.6, on par with Claude Opus 5.
✅ ToolSpace-744B: AutomationBench 54.7, Harvey LAB 27.5, SkillsBench 67.8, OfficeQA Pro 67.1, JobBench 57.5.
✅ Agent performance scales as environment and data scale grow.

We welcome discussion and feedback. Code and models are open-sourced 👏👏👏

📄 Paper: https://arxiv.org/pdf/2611.05036
🌐 Project page: https://toolspace-agent.github.io/
💻 GitHub: https://github.com/dongguanting/ToolSpace
🤗 Hugging Face: https://huggingface.co/collections/dongguanting/toolspace