Professional translation is often a team-based process: translators, reviewers, and project managers must coordinate terminology, legal force, and accountability across documents. Yet many LLM-based translation tools treat human corrections as isolated edits.
The paper introduces CoSLR, a Human‑AI collaborative system for systematic literature reviews that incorporates mandatory human checkpoints within a three‑phase pipeline using large language models and Retrieval‑Augmented Generation. In a survey of 63 participants, 42.9 % rated the system’s usability highly, yet 34.9 % indicated they would trust AI‑generated summaries without further human verification after brief interaction. The study highlights that effective human oversight in AI‑assisted literature reviews depends on users’ willingness to engage with the checkpoints, underscoring a calibration issue that interface design must directly address.
By MD Aidul Islam, Malik Abdul Sami, Muhammad Waseem, Zeeshan Rasheed, Kai-kristian Kemell, Zheying Zhang, Pekka Abrahamsson
arXiv:2609.39333v1 Announce Type: cross
Abstract: Autonomous AI agents can turn authors' goals into interactive narratives by independently organizing and carrying out generation and revision. As age...
By Wenjin Wang, Jiazhen Lei, Yuxin Sha, Nuwa Xi, Meng Zhao, Xingxi Yin, Qi Liu, Yuliang Shen, Zixun Sun
arXiv:2608.28596v1 Announce Type: new
Abstract: Large language model (LLM) agents are increasingly embedded in scientific workflows for literature analysis, drafting, and review. Existing systems adv...
By Nidhi Jha, Siddharth Chaudhary, Ajinkya Kulkarni
MOONWALK is a pre‑production review system for animation and VFX that aligns creative intent, evidence, and action across junior‑supervisor workflows. It records shared intent, anchors judgments to evidence, and translates decisions into clear revision tasks tied to reference notes, with AI handling administrative coordination. A studio study shows that MOONWALK improves intent alignment, decision traceability, and checklist executability compared to a chat‑only interface, while keeping aesthetic authority with practitioners.
By Shih-Yu Lai, Wen-Fan Wang, Sai Ling, Shaune Jan, Bing-Yu Chen, Xiang Anthony Chen
arXiv:2606. 06893v1 Announce Type: new Abstract: Large language model agents increasingly rely on Skills to encode procedural knowledge, yet high-quality Skills remain costly to hand-write.
By Yuyang Zhang, Xinyuan Han, Xudong Jiang, Run Wang