agent/papers/items/2026-2607-16900-environment-free-synthetic-data-generation-for-api-calling-agents.md

Paper: Environment-free Synthetic Data Generation for API-Calling Agents

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type: paper title: Environment-free Synthetic Data Generation for API-Calling Agents authors: Seanie Lee, Sanjoy Chowdhury, Chao Jiang, Cheng-Yu Hsieh, Ting-Yao Hu, Alexander T Toshev, Oncel Tuzel, Raviteja Vemulapalli year: 2026 venue: arXiv url: https://arxiv.org/abs/2607.16900 code_url: source: arxiv collected_at: 2026-07-27 published_at: 2026-07-21 updated_at: 2026-07-21 status: queued relevance: high topics:

• agent-evaluation
• memory
• rag
• tool-use
• workflow-agent
• world-model methods:

benchmarks:

models:

datasets:

• cs.AI related_concepts:

related_jobs:

related_experiments:

related_projects:

collection_score: 14 collection_queries: llm-agent

One-line Takeaway

Auto-collected from arXiv because it matched the Agent collection queries. Needs human skim.

Why Collected

• matched queries: llm-agent
• inferred topics: agent-evaluation, memory, rag, tool-use, workflow-agent, world-model
• arXiv categories: cs.AI
• collection score: 14

Review Checklist

• Does this paper directly inform Agent architecture, evaluation, memory, tools, safety, coding agents, GUI/browser agents, or multi-agent workflows?
• Does it include a benchmark, dataset, code, or reproducible experimental setup?
• Should it be promoted from queued to skimmed or summarized?

Links

• arXiv: https://arxiv.org/abs/2607.16900