Tool-Genesis: A Task-Driven Tool Creation Benchmark for Self-Evolving Language Agent
arXiv:2603.05578v1 Announce Type: cross Abstract: Research on self-evolving language agents has accelerated, drawing increasing attention to their ability to create, adapt, and maintain tools from task requirements. However, existing benchmarks predominantly rely on predefined specifications, which limits scalability and hinders truly autonomous evolution. While recent studies attempt to dynamically generate tools, they primarily emphasize downstream performance, resulting in a "black-box" evaluation that makes it difficult to attribute failures to specific causes. To address this, we propose Tool-Genesis, a diagnostic benchmark designed to quantify agent capabilities across multiple dimensions, including interface compliance, functional correctness, and downstream utility. Tool-Genesis evaluates whether agents can construct task-relevant tools solely from abstract requirements (without preset specifications) and use them to solve realistic problems. Crucially, we find that even state
arXiv:2603.05578v1 Announce Type: cross Abstract: Research on self-evolving language agents has accelerated, drawing increasing attention to their ability to create, adapt, and maintain tools from task requirements. However, existing benchmarks predominantly rely on predefined specifications, which limits scalability and hinders truly autonomous evolution. While recent studies attempt to dynamically generate tools, they primarily emphasize downstream performance, resulting in a "black-box" evaluation that makes it difficult to attribute failures to specific causes. To address this, we propose Tool-Genesis, a diagnostic benchmark designed to quantify agent capabilities across multiple dimensions, including interface compliance, functional correctness, and downstream utility. Tool-Genesis evaluates whether agents can construct task-relevant tools solely from abstract requirements (without preset specifications) and use them to solve realistic problems. Crucially, we find that even state-of-the-art models struggle to produce precise tool interfaces or executable logic in a one-shot setting. These minor initial flaws are amplified through the pipeline, leading to a sharp degradation in downstream metrics. We hope Tool-Genesis will guide future research toward training and steering models to synthesize persistent, general-purpose tools that better address real-world challenges.
Executive Summary
The article introduces Tool-Genesis, a novel benchmark designed to evaluate the autonomous tool creation capabilities of self-evolving language agents. Unlike existing benchmarks that rely on predefined specifications, Tool-Genesis focuses on task-driven evolution by assessing agents' ability to generate tools solely from abstract requirements, without preset templates. The authors identify a critical gap in current evaluation methods: most assessments are 'black-box,' obscuring the root causes of failure. Tool-Genesis addresses this by quantifying agent performance across interface compliance, functional correctness, and downstream utility. The study reveals a significant limitation: even state-of-the-art models exhibit substantial deficiencies in generating precise interfaces or executable logic in a one-shot setting, with these initial errors cascading into measurable degradation in downstream outcomes. This finding is pivotal for redirecting future research toward training models to produce persistent, general-purpose tools aligned with real-world demands.
Key Points
- ▸ Tool-Genesis introduces a diagnostic benchmark for autonomous tool creation
Merits
Innovative Evaluation Framework
Tool-Genesis fills a critical void by offering a structured, diagnostic approach to evaluate agent capabilities beyond predefined specs, enabling granular analysis of interface compliance, functional correctness, and utility.
Identification of Core Limitation
The study uncovers a systemic issue: state-of-the-art models fail to generate precise tool logic in a one-shot setting, revealing a foundational bottleneck that demands targeted intervention.
Demerits
Limited Scope of Real-World Applicability
While the benchmark is theoretically robust, its focus on abstract requirements may not fully capture the complexity of dynamic, context-sensitive tool creation in actual deployment scenarios.
Expert Commentary
Tool-Genesis represents a significant advancement in the evaluation of self-evolving language agents. Historically, benchmarks in this domain have been constrained by the reliance on predefined specifications, which limits scalability and obscures failure modes. The authors’ decision to shift toward abstract requirement-based evaluation is both timely and necessary. Their empirical findings—specifically, the amplification effect of initial interface flaws—are not merely academic; they carry practical implications for model design. If agents cannot reliably generate correct interfaces or executable logic in a single interaction, then downstream success rates will inevitably suffer. This insight demands a paradigm shift: future training pipelines must incorporate feedback loops that detect and correct interface-level anomalies early, ideally through interpretable, modular architectures. Moreover, the benchmark’s potential to inform policy on evaluation standards could catalyze a broader movement toward more rigorous, cause-and-effect-oriented benchmarks across AI domains. The work is not only a methodological breakthrough but a catalyst for rethinking the architecture of autonomous agent development.
Recommendations
- ✓ Integrate Tool-Genesis as a standard evaluation protocol for research on self-evolving agents.
- ✓ Encourage interdisciplinary collaboration between NLP researchers and systems engineers to develop modular, transparent architectures that enable early detection of interface-level errors.