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PrimeSeeker 提出一种面向能力的监督框架，通过「潜在锚点推理」将深度搜索分解为锚点解析与关系传递的耦合操作，据此构建网络证据结构、联合生成问题与参考证据骨架，训练出 30B 搜索智能体，在五个深度搜索基准上以更少工具调用取得强性能。
Presents Doc2DB-Bench, a benchmark for evaluating LLM-based extraction of relational databases from long documents, with 203 instances across 42 schemas and seven domains.
DataPrep-Bench is a unified benchmark evaluating LLMs' capabilities in training data construction and quality evaluation across six domains, including a skill-guided agent (Data-Construction-Skill) and a distribution-based evaluator (DAS) that achieves strong cross-model correlation.
DataEvolver is a self-evolving multi-agent framework that leverages feedback from rejected samples to iteratively enhance data quality for text-rich image generation, achieving 85.3% OCR-F1 improvement on TextScenesHQ and 35.3% on LongTextBench using PixArt-alpha.
This paper introduces CHILLGuard, a fine-grained Chinese LLM content safety guardrail built on a new 5-macro, 31-micro category risk taxonomy and a scalable multi-stage data construction pipeline. The model achieves state-of-the-art performance, improving F1 score by 15.92% over existing baselines.