arXiv:2605.28556v1 Announce Type: new Abstract: As agent capabilities advance, existing benchmarks, such as $\tau^2$-Bench, are becoming increasingly saturated. Yet constructing new benchmark tasks remains complex, costly, and labor-intensive. Moreover, the standard approach, in which scenarios are first written in natural language and then mapped to tool sequences, captures only a narrow subset of the tool-use patterns agents exercise. In this paper, we address these problems by reversing the task construction process. We propose TASTE: Task Synthesis from Tool Sequence Evolution, an automati
As AI agent capabilities rapidly advance, the current benchmarking methods are proving insufficient and are reaching saturation, necessitating new and more efficient evaluation techniques.
This development proposes an automated, reverse-engineered approach to task construction, significantly improving the scalability and depth of AI agent evaluation.
The process of creating and evaluating benchmarks for AI agents could become more efficient and comprehensive, leading to faster development cycles and more robust agents.
- · AI Agent developers
- · Benchmarking platforms
- · AI researchers
- · Tool-use agent companies
- · Manual benchmark creators
- · Traditional task-mapping approaches
More sophisticated and versatile AI agents are developed due to improved benchmarking.
Accelerated deployment of AI agents in complex environments, potentially collapsing more workflows.
Enhanced AI agent capabilities lead to a broader societal impact, necessitating new regulatory frameworks and ethical considerations.
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Read at arXiv cs.AI