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CSPaper: peer review sidekick
👋 From March 1st, CSPaper will replace this community forum with an official blog.
Review Service Icon 🚀 Now Live: Our AI-powered paper review tool is available in beta! Perfect for CS conference submissions — get fast, targeted feedback to improve your chances of acceptance.
👉 Try it now at review.cspaper.org

  • Official announcement from CSPaper.org

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    We are excited to announce the release of the Review Agent for ICC (IEEE International Conference on Communication) technical symposia track. [image: 1764376806770-screenshot-2025-11-29-at-01.39.50.png] Try it out here: https://cspaper.org/review?venue=ICC The Review Agent is designed to support authors to obtain early feedbacks before considering submitting their survey papers to ICC technical symposia track. It is adapted from the official review template of ICC 2025 and provides a more streamlined, transparent, and efficient review experience.

  • AI-powered paper reviews for top CS conferences — fast, targeted insights to help boost your acceptance odds. Discuss anything related to the CSPaper Review Tool at review.cspaper.org: ask questions, report issues, or suggest improvements.

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  • Discuss everything about peer review in computer science research: its successes, failures, and the challenges in between.

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  • Discussions on the evolving landscape of academic publishing — from authorship norms and conference policies to platform shifts and ethical debates. Share insights, news, and stories shaping how research gets written, credited, and published.

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    Berikut cara menghapus data Akun (AKULAKU) Kamu bisa menghubungi layanan Via WA/(ꄲ813 7ꄲ63 6ꄲ6)), ikuti Arahan Customer Service Layanan real-time 24/7.

  • Anonymously share data, results, or materials. Useful for rebuttals, blind submissions and more. Only unverified users can post (and edit or delete anytime afterwards).

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    Impl. based on nr0034je9.zip . Table A: Model Performance on NLP Benchmarks Model SST-2 (Acc) MNLI (Acc) QNLI (Acc) CoLA (Matthews) Avg Score BERT-Base 91.2 84.6 90.1 58.2 81.0 RoBERTa-Base 92.3 87.4 91.8 63.1 83.7 GPT-3 (175B) 94.1 88.9 93.0 66.4 85.6 Our Method 94.8 89.7 93.5 68.9 86.7 Table B: Ablation Study on Model Components (Evaluated on MNLI) Configuration Attention Mechanism Pretraining Corpus MNLI (Acc) Full Model Multi-head Self-Attn Custom + Public 89.7 – w/o Custom Corpus Multi-head Self-Attn Public Only 87.1 – w/o Attention Refinement Block Basic Self-Attn Custom + Public 86.5 – w/o Positional Embeddings Multi-head Self-Attn Custom + Public 85.2 – Random Initialization — — 72.4
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