project
AlphaEval - A production-grade agent evaluation framework launched by companies like Crossroads Technology.
AlphaEval is a production-grade agent evaluation framework jointly launched by Cross-Tech. The framework covers 94 production tasks from 7 real-world enterprise scenarios, building a complete and reproducible chain from requirement clarification to app delivery.
What is AlphaEval?
AlphaEval is a production-grade agent evaluation framework jointly launched by SII-GAIR, Shanghai Jiao Tong University, KuaFuAI, and other institutions. The framework covers 94 production tasks from 7 real-world enterprise scenarios, constructing a complete and reproducible chain 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, response adaptation, and user experience quality. This allows agent evaluation to shift from model leaderboards to real-world business delivery, driving evaluation from assessing answers to assessing deliverables.
The main functions of AlphaEval
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Construction of reproducible evaluation tasksBased on real-world application development practices, we have built task chains and process records covering formats such as mini-programs and H5.
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Multidimensional Rubrics StandardsApp delivery quality is broken down into measurable dimensions such as static structure, visual accuracy, functional logic, responsiveness, and user experience quality.
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Process labeling systemThe generation and iteration process of the Agent is marked around key nodes such as understanding requirements, locating problems, covering remediation, and achieving delivery targets.
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End-to-end delivery assessmentThe framework can assess functional completeness, interactive usability, and the degree to which user intent is achieved.
AlphaEval's technical principles
- Complete and reproducible linkAlphaEval builds a reproducible six-stage chain from real requirements to app delivery: initial requirements, requirements clarification, application generation, iterative feedback, fix iteration, and delivery evaluation, fully preserving the characteristics of ambiguous requirements and multiple iterations in real production.
- Rubrics Multidimensional RatingEstablish quantifiable Rubrics standards and break down delivery quality into five dimensions: static structure, visual accuracy, functional logic, responsiveness, and user experience quality, transforming subjective evaluations into systemic engineering problems.
- Process labeling and root cause localizationThe Agent generation and iteration process is annotated around key milestones such as requirement understanding, problem localization, remediation coverage, and delivery achievement. The assessment evaluates the Agent's ability to understand business rules, pinpoint root causes, and cover the entire value chain.
- Delivery-oriented assessment paradigmThis approach aims to upgrade evaluation from "evaluating answers" to "evaluating deliverables," establishing a three-tiered, progressive logic: code existence is the first tier, functional completeness is the second tier, and the genuine fulfillment of user intent is the most crucial tier. By distilling application generation practices into an assessable and reproducible methodology, agent evaluation becomes more closely aligned with the actual decision-making logic of enterprise production environments.
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How to use AlphaEval
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Access evaluation taskResearchers or platform providers will connect the Agent to 94 real production task datasets provided by AlphaEval.
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Running the complete linkThe Agent executes tasks according to the process of "requirements understanding → page generation → function repair → interaction verification → final delivery".
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Combining automated and manual evaluationThe Rubrics standard is used to automatically determine the static structure and functional logic of the delivered results, and to provide expert scoring for the quality of the user experience.
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Output evaluation reportGenerate a comprehensive evaluation result covering task understanding, generation quality, repair capabilities, 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 object | Production-grade Agent application generation (Mini Program/H5) | Real GitHub Issue Fixes in the Code Repository |
| Task type | The complete chain from demand to app delivery | Code patch generation and unit test pass |
| Evaluation Dimensions | Static structure, visuals, functionality, adaptation, and user experience | Unit test pass rate, patch correctness |
| Scene characteristics | Incomplete requirements, multiple iterations needed, focus on interaction and visual design | The problem is clearly defined, there are test cases for verification, and it is a pure code-level problem. |
| Core differences | Emphasis on "deliverable evaluation" and process reproducibility | Emphasis on "bug fixing" and test-driven verification |
Application scenarios of AlphaEval
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Agent application generation platform evaluationProvides a standardized delivery quality assessment system for low-code/no-code agent platforms.
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Model selection and iteration: Helps the team compare the ability to understand and deliver requirements in real business scenarios using different large models.
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Agent R&D and DebuggingBy labeling processes, we can identify weak links in the Agent's ability to clarify requirements, pinpoint root causes, or cover the entire supply chain.
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Academic research benchmarks: Provides public datasets and methodological references for evaluating production-grade agents for top conferences such as COLM.