Beyond Outcomes: Dual-View Relational Learning for Efficient Agent Benchmarking

Problem

The paper addresses a gap in the cost-effectiveness of agent benchmark evaluations compared to conventional large language model (LLM) benchmarks. The authors highlight the inefficiencies in current methodologies, particularly in the context of evaluating agent performance across multiple tasks.

Method

The proposed method, DualViewEval, leverages a dual-view relational learning approach. Key components include:

• Trajectory Analysis: It analyzes large-scale trajectories to extract relevant performance signals.
• Process Signals: The method identifies six complementary process signals that correlate with final agent performance, allowing for a more nuanced understanding of agent behavior.
• Miniset Learning: DualViewEval jointly exploits both outcome and process relations to learn an exact-size miniset, which is a compact representation of the necessary evaluations.
• Score Prediction: The model predicts full-benchmark scores based on the learned representations, enhancing the efficiency of the evaluation process.

Results

The results demonstrate significant improvements over conventional benchmarks:

• Compression Efficiency: DualViewEval achieves a compression ratio of $24 imes$ to $40 imes$ on APEX-Agents and BFCL across 20 tasks, indicating a substantial reduction in evaluation costs.
• Mean Absolute Error (MAE): The method shows a reduction in MAE by $14.5 ext{%}$ to $28.2 ext{%}$ compared to the strongest competitors, showcasing its accuracy in performance prediction.
• Kendall's τ Improvement: There is an improvement of up to $7.2 ext{%}$ in Kendall's τ relative to EssenceBench on the SWE-bench Verified dataset, indicating enhanced ranking consistency in performance evaluations.

Limitations

The authors do not report any limitations in the study. However, the absence of reported limitations may suggest a need for further validation across diverse agent types and tasks to ensure generalizability.

Why it matters

The implications of this work are significant for downstream research in agent evaluation methodologies. By providing a more efficient and accurate benchmarking process, DualViewEval can facilitate faster iterations in agent development and improve the overall understanding of agent performance dynamics. This could lead to advancements in various applications, including robotics, autonomous systems, and AI-driven decision-making.