arXiv:2602.03619v2 Announce Type: replace Abstract: Nowadays, developing reliable DeepResearch-style long-form report generation remains challenging, as training and evaluation lack verifiable reward signals. Accordingly, rubric-based evaluation has become a common practice. However, existing approaches either rely on coarse, pre-defined rubrics that lack sufficient granularity or depend on manually constructed query-specific rubrics that are costly and difficult to scale. In this paper, we propose a pipeline to train preference-grounded query-specific rubric generators tailored for DeepResear
The increasing complexity and scale of AI model outputs, particularly in long-form generation, necessitates more sophisticated and automated evaluation methods to accelerate development cycles.
Improving the verifiability and quality of AI-generated long-form content is crucial for its adoption in critical applications, directly impacting efficiency and reliability of AI agents.
The ability to automatically generate query-specific rubrics from human preferences will significantly streamline the training and evaluation of advanced generation models, moving beyond general, coarse metrics.
- · AI developers
- · Organizations deploying AI for content generation
- · Machine learning researchers
- · SaaS providers focused on AI evaluation
- · Manual rubric creators
- · Generative AI models with poor evaluation metrics
More accurate and nuanced evaluation of long-form AI-generated content becomes possible.
Accelerated development and deployment of reliable, high-quality deep research and report generation AI systems.
Increased trust and integration of autonomous AI agents in knowledge work, potentially collapsing more white-collar workflows.
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