Alipay-PIBench: A Realistic Payment Integration Benchmark for Coding Agents

2026-07-20 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Expert, extended

Summary

Alipay-PIBench is a new repository-level benchmark developed by Alipay to evaluate AI coding agents on realistic payment integration tasks within the Alipay Open Platform ecosystem. Comprising nine product-specific projects and 18 task instances, the benchmark assesses functional correctness, reliability, security, and business-state consistency across two progressive scenarios: Basic (Functional Payment Completion) and Advanced (Risk-Aware Payment Hardening). Solutions are evaluated using deterministic checks derived from scenario-specific rubrics, complemented by LLM-assisted assessment. Across six evaluated models, mean Rubric Pass Rate (RPR) under the "with-skill" condition, which provides structured Alipay payment guidance, ranged from 68.58% to 91.37%. This "with-skill" condition improved mean RPR by an average of 10.31 percentage points, with larger gains for Basic tasks (+11.27 pp) than Advanced tasks (+9.35 pp), highlighting the value of domain-specific guidance.

Key takeaway

For AI Engineers developing or evaluating coding agents for complex domain-specific integrations, you should prioritize incorporating structured domain guidance or "skills" into your agent architectures. This approach, demonstrated by Alipay-PIBench, significantly improves agent performance and efficiency, particularly for tasks requiring deep domain knowledge, cross-component coordination, and adherence to security boundaries. Integrating such guidance can lead to more reliable solutions and reduced development cycles.

Key insights

Structured domain guidance significantly enhances AI coding agents' ability to perform complex, repository-level payment integrations.

Principles

• Realistic coding agent evaluation requires domain-specific, repository-level tasks.
• Payment integration demands cross-component coordination and state consistency.
• Structured guidance improves agent performance, especially for complex logic.

Method

Alipay-PIBench uses Product-Grounded Task Construction, progressive Basic/Advanced scenarios, Rubric-Derived Evaluation (deterministic checks + LLM-assisted assessment), and a paired without-skill/with-skill protocol to measure agent performance and guidance impact.

In practice

• Design benchmarks with progressive scenarios to test functional completion and risk hardening.
• Combine deterministic and LLM-assisted checks for comprehensive code evaluation.
• Integrate structured domain-specific "skills" to boost agent integration efficiency.

Topics

• Alipay-PIBench
• Coding Agents
• Payment Integration
• AI Benchmarking
• LLM Evaluation
• Software Engineering
• Skill Intervention

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.