Enregistré dans:
| Auteurs principaux: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2511.10902 |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866911451632893952 |
|---|---|
| author | Hong, Mengze Jiang, Di Zhao, Weiwei Li, Yawen Wang, Yihang Luo, Xinyuan Sun, Yanjie Zhang, Chen Jason |
| author_facet | Hong, Mengze Jiang, Di Zhao, Weiwei Li, Yawen Wang, Yihang Luo, Xinyuan Sun, Yanjie Zhang, Chen Jason |
| contents | While large language models (LLMs) offer promising capabilities for automating academic workflows, existing systems for academic peer review remain constrained by text-only inputs, limited contextual grounding, and a lack of actionable feedback. In this work, we present an interactive web-based system for multimodal, community-aware peer review simulation to enable effective manuscript revisions before paper submission. Our framework integrates textual and visual information through multimodal LLMs, enhances review quality via retrieval-augmented generation (RAG) grounded in web-scale OpenReview data, and converts generated reviews into actionable to-do lists using the proposed Action:Objective[\#] format, providing structured and traceable guidance. The system integrates seamlessly into existing academic writing platforms, providing interactive interfaces for real-time feedback and revision tracking. Experimental results highlight the effectiveness of the proposed system in generating more comprehensive and useful reviews aligned with expert standards, surpassing ablated baselines and advancing transparent, human-centered scholarly assistance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_10902 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multimodal Peer Review Simulation with Actionable To-Do Recommendations for Community-Aware Manuscript Revisions Hong, Mengze Jiang, Di Zhao, Weiwei Li, Yawen Wang, Yihang Luo, Xinyuan Sun, Yanjie Zhang, Chen Jason Computation and Language While large language models (LLMs) offer promising capabilities for automating academic workflows, existing systems for academic peer review remain constrained by text-only inputs, limited contextual grounding, and a lack of actionable feedback. In this work, we present an interactive web-based system for multimodal, community-aware peer review simulation to enable effective manuscript revisions before paper submission. Our framework integrates textual and visual information through multimodal LLMs, enhances review quality via retrieval-augmented generation (RAG) grounded in web-scale OpenReview data, and converts generated reviews into actionable to-do lists using the proposed Action:Objective[\#] format, providing structured and traceable guidance. The system integrates seamlessly into existing academic writing platforms, providing interactive interfaces for real-time feedback and revision tracking. Experimental results highlight the effectiveness of the proposed system in generating more comprehensive and useful reviews aligned with expert standards, surpassing ablated baselines and advancing transparent, human-centered scholarly assistance. |
| title | Multimodal Peer Review Simulation with Actionable To-Do Recommendations for Community-Aware Manuscript Revisions |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2511.10902 |