arXiv:2606. 29727v1 Announce Type: new Abstract: Professional translation is often a team-based process: translators, reviewers, and project managers must coordinate terminology, legal force, and accountability across documents.
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.
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...
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...
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.
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.
arXiv:2609.22793v1 Announce Type: new Abstract: LLM-based machine translation evaluation can closely match human judgments, but in practice it remains largely diagnostic, with the signals rarely tran...
arXiv:2509.21891v3 Announce Type: replace-cross Abstract: Fine-tuning large language models for code editing has typically relied on mining commits and pull requests. The working hypothesis has been...
arXiv:2506. 08134v4 Announce Type: replace Abstract: Peer review, the bedrock of scientific advancement in machine learning (ML), is strained by a crisis of scale.
arXiv:2607. 19865v1 Announce Type: new Abstract: As autonomous agents rapidly evolve, their ability to reliably manipulate ubiquitous digital documents has become critical for enabling general-purpose AI assistants and automating complex workspace workflows.
arXiv:2605. 17548v2 Announce Type: replace-cross Abstract: Code review has evolved for decades, from informal peer checking to today's pull request (PR) workflows, yet it remains a largely manual and cognitively demanding process.
arXiv:2607. 15272v1 Announce Type: cross Abstract: Editing the figures in a research paper is a routine and time-consuming part of everyday research practice: authors relabel components, rearrange panels, and restyle visuals as they revise their manuscripts.