Papers with information synthesis
ProductResearch: Training E-Commerce Deep Research Agents via Multi-Agent Synthetic Trajectory Distillation (2026.acl-industry)
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| Challenge: | Existing LLM-based agents lack the interaction depth and contextual breadth required for complex product research. |
| Approach: | They propose a multi-agent framework that synthesizes high-fidelity tool-use trajectories for training robust e-commerce shopping agents. |
| Outcome: | The proposed framework synthesizes high-fidelity, long-horizon tool-use trajectories for training robust e-commerce shopping agents. |
Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document Inputs (D19-1)
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| Challenge: | Current approaches extract portions of web text as input to Sequence-to-Sequence models . a problem is generating relevant knowledge from noisy and redundant input such as webpages . |
| Approach: | They propose to restructure free text into local knowledge graphs that are linearized into sequences . they propose to encode the graph as a sequence and then linearize it into a structured sequence . |
| Outcome: | The proposed method compresses web search information and reduces redundancy . it can be used for open-domain question answering and multi-document summarization tasks . |
WebCPM: Interactive Web Search for Chinese Long-form Question Answering (2023.acl-long)
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Yujia Qin, Zihan Cai, Dian Jin, Lan Yan, Shihao Liang, Kunlun Zhu, Yankai Lin, Xu Han, Ning Ding, Huadong Wang, Ruobing Xie, Fanchao Qi, Zhiyuan Liu, Maosong Sun, Jie Zhou
| Challenge: | Long-form question answering requires two procedures: information retrieval and information synthesis. |
| Approach: | They propose a Chinese long-form question answering dataset called WebCPM . the dataset is based on a web search interface that engages with a search engine in real time . |
| Outcome: | The proposed dataset generates answers that are no worse than human-written ones . the dataset is the first Chinese LFQA dataset . |
Asclepius: A Spectrum Evaluation Benchmark for Medical Multi-Modal Large Language Models (2025.acl-long)
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Jie Liu, Wenxuan Wang, Su Yihang, Jingyuan Huang, Yudi Zhang, Cheng-Yi Li, Wenting Chen, Xiaohan Xing, Kao-Jung Chang, Linlin Shen, Michael R. Lyu
| Challenge: | Medical Multi-Modal Large Language Models (Med-MLLMs) are a promising new form of artificial general intelligence due to their ability to tackle complex tasks. |
| Approach: | They propose a new benchmark that comprehensively assesses medical multi-modal large language models in terms of distinct medical specialties and different diagnostic capacities. |
| Outcome: | The proposed model covers 15 medical specialties and different diagnostic capacities, and excludes overlap with existing VQA dataset. |
DR-Arena: an Automated Evaluation Framework for Deep Research Agents (2026.acl-long)
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| Challenge: | Existing benchmarks for evaluating deep research capabilities rely on static datasets. |
| Approach: | They propose a fully automated evaluation framework that pushes DR agents to their capability limits through dynamic investigation. |
| Outcome: | DR-Arena achieves a Spearman correlation of 0.94 with the LMSYS Search Arena leaderboard. |