The paper introduces SYNTRA, a self‑improving framework that transforms a small set of retrieval examples into reusable synthesis instructions for generating training data. By inferring implicit information needs and relevance criteria from an unlabelled corpus and refining instructions through feedback, SYNTRA produces effective training data across 24 datasets and 15 domains, improving retrieval performance even for strong embedding models. Experiments show that SYNTRA’s self‑improvement yields better results than human calibration on four tasks and scales effectively, with continued gains on MS MARCO across a 32‑fold range of synthetic data sizes.
By Zucan Lyu, Rui Tang, Jianlyu Chen, Peisheng Jin, Defu Lian
arXiv:2610.14730v1 Announce Type: cross
Abstract: Adapting embedding models to new tasks requires training data that reflect task-specific information needs and relevance criteria. However, manual da...
By Zucan Lyu, Rui Tang, Jianlyu Chen, Peisheng Jin, Zheng Liu, Defu Lian
The paper introduces TRIO, a retrieval‑augmented generation framework that uses three agents—query extension, judge, and answer—to iteratively refine queries and improve retrieval and answer quality. It aligns each agent’s objective with rewards derived from answer quality, employs unanimity voting to label expanded queries, and applies dynamic matching‑based rewards with curriculum learning for the answer agent. Experiments on multiple benchmarks show TRIO outperforms state‑of‑the‑art methods.
By Yingtao Ren, Xiao Luo, Yiwei Fu, Jinzhao Zhou, Yu-Cheng Chang, Chin-Teng Lin
arXiv:2605.24218v2 Announce Type: replace
Abstract: Deep research agents extend the role of search engines from retrieving keyword-matched pages to synthesizing knowledge, fundamentally changing how...
By Jian Xie, Tianhe Lin, Zilu Wang, Yuting Ning, Yuekun Yao, Tianci Xue, Zhehao Zhang, Zhongyang Li, Kai Zhang, Yufan Wu, Shijie Chen, Boyu Gou, Mingzhe Han, Yifei Wang, Vint Lee, Xinpeng Wei, Xiangjun Wang, Yu Su, Huan Sun
arXiv:2603.18272v2 Announce Type: replace
Abstract: While large language models (LLMs) have advanced the development of general-purpose agents, robust generalization to unseen tasks remains challengi...
By Thomas Palmeira Ferraz, Romain Deffayet, Vassilina Nikoulina, Herv\'e D\'ejean, St\'ephane Clinchant
The paper introduces a method for creating query‑specific rubrics for DeepResearch‑style long‑form report generation by training rubric generators with reinforcement learning. It builds a dataset of queries annotated with human preferences, then uses a hybrid reward that includes preference consistency, format validity, and LLM‑based rubric evaluation. The learned rubrics outperform generic or manually constructed alternatives in distinguishing preferred reports and, when used as rewards, improve performance of both single‑agent and multi‑agent DeepResearch systems.
By Changze Lv, Jie Zhou, Wentao Zhao, Jingwen Xu, Shihan Dou, Zisu Huang, Muzhao Tian, Xiaohua Wang, Zhengkang Guo, Yang Liu, Pluto Zhou, Tao Gui, Le Tian, Xiao Zhou, Xiaoqing Zheng, Xuanjing Huang, Jie Zhou