ToolSpace: Scaling Tool-Centric Environment Synthesis for Long-Horizon Agents
ToolSpace is a tool-centric framework for synthesizing environments for long-horizon LLM agents.
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- Primary sourceHugging Face
- Discovery linkHugging Face
- Linked in sourceView arXiv page
- Linked in sourceView PDF
- Linked in sourcehttps://github.com/dongguanting/ToolSpace
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ToolSpace is a tool-centric framework for synthesizing environments for long-horizon LLM agents. It mines data, state, and retry dependencies from adjacent calls in execution traces of real MCP services and open agent corpora using deterministic rules, clusters the weighted tool-composition graph into environments, and synthesizes databases only for state-mutating environments. State-aware random walks with rollback are decompiled into natural-language tasks with assertion-based checks, keeping only samples that replay successfully. The authors report harder Pro benchmark versions on which seven frontier models drop by up to 38.2 points, and gains for Qwen3.6-35B-A3B and GLM-5.2 across seven benchmarks.
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Source claims not independently checked; reported gains and benchmark results come from the authors' own experiments.
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Tool dependencies are mined from execution traces of real MCP services using deterministic rules rather than LLM judgment.
Source: Hugging Face ↗Stored source-based brief · key point 1 · AINewsIQ paraphraseDatabases are synthesized only for state-mutating environments; query and stateless tools replay recorded returns.
Source: Hugging Face ↗Stored source-based brief · key point 2 · AINewsIQ paraphraseSeven frontier models drop by up to 38.2 points on the derived AutomationBench and tau3-Bench Banking Pro versions.
Source: Hugging Face ↗Stored source-based brief · key point 3 · AINewsIQ paraphraseQwen3.6-35B-A3B rises from 23.0 to 41.0 average score; GLM-5.2 rises from 44.6 to 56.6 across seven benchmarks.
Source: Hugging Face ↗Stored source-based brief · key point 4 · AINewsIQ paraphraseLimits from the stored brief
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Why this may matter
Grounding synthetic agent environments in recorded execution traces aims to reduce interface drift and produce verifiable long-horizon tool-use tasks, which could improve both training data and evaluation difficulty for agents.
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