Agent Psychometrics: Task-Level Performance Prediction in Agentic Coding Benchmarks
Summary: arXiv:2604.00594v1 Announce Type: new
Introduction
As the landscape of large language model (LLM)-based coding evolves, the shift from static single-step code generation to multi-step agentic interaction with various tools and environments presents unique challenges. Understanding which tasks will present difficulties for agents and the underlying reasons is becoming increasingly complex. Current methodologies for evaluating agent performance predominantly rely on aggregate pass rates across coding benchmarks. However, this approach often masks the nuanced diversity of tasks that exist within these benchmarks.
Challenges in Current Evaluation Practices
The reliance on single-number metrics can lead to a skewed perception of an agent’s capabilities. For instance, while an agent may demonstrate a high pass rate, this figure does not reveal the specific areas where it may struggle or excel. As such, a more granular approach is necessary to accurately assess agent performance across a spectrum of tasks.
Proposed Framework
To address these challenges, we introduce a novel framework designed to predict success or failure on individual tasks tailored for the agentic coding regime. Our approach builds on traditional Item Response Theory (IRT) by integrating a variety of rich features extracted from tasks, which include:
- Issue statements
- Repository contexts
- Proposed solutions
- Test cases
Decomposing Agent Ability
A key innovation of our framework is the decomposition of agent ability into two distinct components: LLM ability and scaffold ability. This parameterization allows for a more detailed understanding of how different agents interact with various tasks, enabling the aggregation of evaluation data across heterogeneous leaderboards. Consequently, we can accurately predict task-level performance for both unseen benchmarks and novel combinations of LLM and scaffold.
Practical Applications
Our methods hold significant practical utility for benchmark designers. By providing a means to better calibrate the difficulty of new tasks, benchmark creators can avoid the resource-intensive process of conducting computationally expensive agent evaluations. This not only streamlines the development of coding benchmarks but also ensures that assessments are more reflective of actual agent capabilities.
Conclusion
The evolution of coding benchmarks necessitates an innovative approach to performance evaluation. By utilizing a framework that accounts for the diversity of tasks and the multi-faceted nature of agent abilities, we can enhance our understanding of LLM performance in agentic contexts. This advancement not only benefits researchers and developers but also paves the way for more effective and insightful evaluations in the future.