Agent psychometrics: Task-level performance prediction in agentic coding benchmarks
arXiv:2604.00594v1 Announce Type: new Abstract: As the focus in LLM-based coding shifts from static single-step code generation to multi-step agentic interaction with tools and environments, understanding which tasks will challenge agents and why becomes increasingly difficult. This is compounded by current practice: agent performance is typically measured by aggregate pass rates on benchmarks, but single-number metrics obscure the diversity of tasks within a benchmark. We present a framework for predicting success or failure on individual tasks tailored to the agentic coding regime. Our approach augments Item Response Theory (IRT) with rich features extracted from tasks, including issue statements, repository contexts, solutions, and test cases, and introduces a novel decomposition of agent ability into LLM and scaffold ability components. This parameterization enables us to aggregate evaluation data across heterogeneous leaderboards and accurately predict task-level performance for
arXiv:2604.00594v1 Announce Type: new Abstract: As the focus in LLM-based coding shifts from static single-step code generation to multi-step agentic interaction with tools and environments, understanding which tasks will challenge agents and why becomes increasingly difficult. This is compounded by current practice: agent performance is typically measured by aggregate pass rates on benchmarks, but single-number metrics obscure the diversity of tasks within a benchmark. We present a framework for predicting success or failure on individual tasks tailored to the agentic coding regime. Our approach augments Item Response Theory (IRT) with rich features extracted from tasks, including issue statements, repository contexts, solutions, and test cases, and introduces a novel decomposition of agent ability into LLM and scaffold ability components. This parameterization enables us to aggregate evaluation data across heterogeneous leaderboards and accurately predict task-level performance for unseen benchmarks, as well as unseen LLM-scaffold combinations. Our methods have practical utility for benchmark designers, who can better calibrate the difficulty of their new tasks without running computationally expensive agent evaluations.
Executive Summary
This article introduces a novel framework for predicting success or failure on individual tasks in the context of agentic coding benchmarks. By augmenting Item Response Theory (IRT) with rich features extracted from tasks, the authors develop a model that accurately predicts task-level performance for unseen benchmarks and LLM-scaffold combinations. This approach has practical utility for benchmark designers, enabling them to calibrate task difficulty without running computationally expensive agent evaluations. The framework also introduces a novel decomposition of agent ability into LLM and scaffold ability components, providing a more nuanced understanding of agent performance. The article's contribution lies in its ability to address the limitations of current practice, which often relies on aggregate pass rates that obscure task diversity.
Key Points
- ▸ The article introduces a framework for predicting task-level performance in agentic coding benchmarks.
- ▸ The framework augments Item Response Theory (IRT) with rich features extracted from tasks.
- ▸ The authors introduce a novel decomposition of agent ability into LLM and scaffold ability components.
Merits
Strength in Addressing Current Limitations
The article effectively addresses the limitations of current practice in agentic coding benchmark evaluation, providing a more nuanced understanding of agent performance.
Practical Utility for Benchmark Designers
The framework has practical utility for benchmark designers, enabling them to calibrate task difficulty without running computationally expensive agent evaluations.
Demerits
Limitation in Generalizability
The framework's generalizability to other domains or applications is not fully explored, potentially limiting its broader impact.
Complexity of Task Features
The extraction and use of rich features from tasks may introduce additional complexity, potentially making the framework more difficult to implement and interpret.
Expert Commentary
While the article makes a significant contribution to the field of agentic coding benchmarks, its limitations in generalizability and complexity of task features should be acknowledged. Furthermore, the article's focus on predicting task-level performance raises questions about the role of human evaluation in AI development and deployment. As the field continues to evolve, it will be essential to consider the broader implications of these insights and to develop methods that can capture the nuances of human evaluation. Ultimately, the article's contribution lies in its ability to address the limitations of current practice, providing a more nuanced understanding of agent performance and enabling benchmark designers to calibrate task difficulty more effectively.
Recommendations
- ✓ Future research should explore the generalizability of the framework to other domains and applications, as well as its potential extensions to other areas of AI development and deployment.
- ✓ The development of methods that can capture the nuances of human evaluation should be a priority, as the field continues to evolve and AI systems become increasingly sophisticated.
Sources
Original: arXiv - cs.AI
