What is AlphaEval
AlphaEval is a production-grade Agent evaluation framework jointly launched by multiple institutions including SII-GAIR, Shanghai Jiao Tong University, and KuaFuAI. The framework covers 94 production tasks across 7 real enterprise scenarios, building a complete reproducible pipeline from requirement clarification to App delivery. The framework establishes Rubrics standards, breaking down delivery quality into dimensions such as static structure, visual accuracy, functional logic, responsive adaptation, and experience quality, returning Agent evaluation from model leaderboards to real business delivery, and advancing evaluation from judging answers to judging delivery.
Main features of AlphaEval
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Reproducible evaluation task construction: Based on real application generation practices, it builds task pipelines and process records covering mini-programs, H5, and other forms.
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Multi-dimensional Rubrics standards: It breaks down App delivery quality into evaluable dimensions such as static structure, visual accuracy, functional logic, responsive adaptation, and experience quality.
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Process annotation system: Around key nodes such as requirement understanding, problem localization, fix coverage, and delivery compliance, it annotates the Agent's generation and iteration process.
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End-to-end delivery evaluation: The framework can evaluate functional completeness, interaction usability, and the degree to which user intent is achieved.
Technical principles of AlphaEval
- Complete reproducible pipeline: AlphaEval builds a six-stage reproducible pipeline from real requirements to App delivery: initial requirements, requirement clarification, application generation, iterative feedback, fix iteration, and delivery evaluation, fully preserving the characteristics of ambiguous requirements and multiple rounds of iteration in real production.
- Multi-dimensional Rubrics scoring: It establishes quantifiable Rubrics standards, breaking down delivery quality into five dimensions: static structure, visual accuracy, functional logic, responsive adaptation, and experience quality, transforming subjective evaluation into a systematic engineering problem.
- Process annotation and root cause localization: Around key nodes such as requirement understanding, problem localization, fix coverage, and delivery compliance, it annotates the Agent's generation and iteration process. The evaluation examines whether the Agent can understand business rules, locate root causes, and cover the global pipeline.
- Delivery-oriented evaluation paradigm: It advances evaluation from "judging answers" to "judging delivery," establishing a three-layer progressive logic: code existence is the first layer, functional completeness is the second layer, and truly satisfying user intent is the most critical layer. By distilling application generation practices into an evaluable and reproducible methodology, it makes Agent evaluation closer to the actual decision-making logic of enterprise production environments.
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How to use AlphaEval
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Connect evaluation tasks: Researchers or platform providers connect their Agents to the 94 real production task datasets provided by AlphaEval.
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Run the complete pipeline: The Agent executes tasks according to the process of "requirement understanding -> page generation -> function fixing -> interaction verification -> final delivery."
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Combination of automatic and manual evaluation: Through Rubrics standards, it performs automatic judgment of static structure and functional logic on delivery results, and expert scoring of experience quality.
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Output evaluation reports: Generate a comprehensive evaluation result covering task understanding, generation quality, repair capability, and delivery compliance rate.
AlphaEval's project address
- Project official website：https://alphaeval.ai/
- GitHub repository：https://github.com/GAIR-NLP/AlphaEval
- arXiv technical paper：https://arxiv.org/pdf/2604.12162
Comparison of AlphaEval with similar competing products
| Dimension | AlphaEval | SWE-bench |
|---|---|---|
| Evaluation target | Production-grade Agent application generation (mini program/H5) | Fixing real GitHub Issues in code repositories |
| Task type | The complete pipeline from requirements to App delivery | Code patch generation and unit test passing |
| Evaluation dimensions | Static structure, visuals, functionality, adaptation, experience | Unit test pass rate, patch correctness |
| Scenario characteristics | Incomplete requirements, requires multiple rounds of iteration, focuses on interaction and visuals | Clear problem, verified with test cases, purely at the code level |
| Core difference | Emphasizes "evaluating delivery" and process reproducibility | Emphasizes "fixing bugs" and test-driven verification |
Application scenarios of AlphaEval
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Evaluation of Agent application generation platforms: Provides a standardized delivery quality evaluation system for low-code/no-code Agent platforms.
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Model selection and iteration: Helps teams compare the requirement understanding and delivery capabilities of different large models in real business scenarios.
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Agent R&D debugging: Uses process annotations to locate weak links in an Agent's requirement clarification, root cause identification, or global pipeline coverage.
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Academic research benchmark: Provides a public dataset and methodological reference for production-grade Agent evaluation for top conferences such as COLM.