scientific writing
scientific writing on Beyond Market Intelligence: a running collection of 2 stories we have gathered and hand-picked because they are worth your time. Every post here touches on scientific writing in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around scientific writing, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
ICLR numbered citations possible? [R]
Navigating citation formatting for ICLR submissions can be a critical detail. The instructions specify Author Year format, but a shift to numbered citations (without spaces) risks immediate desk rejection. While community experience on this is valuable, definitive guidance remains scarce. Submitting with a non-compliant format introduces unnecessary risk. For further context on navigating conference deadlines and related considerations, explore our article, "NeurIPS 2026 Author Notifications Close to ICLR Deadline." Prioritize adherence to the provided guidelines to ensure your submission’s review.
How much does adding an honest limitations section hurt the paper? [D]
Addressing limitations honestly in research papers—while generally beneficial—raises critical questions about reviewer bias and potential requests for remediation. Does openly acknowledging constraints negatively impact perception, or will reviewers demand fixes outlined in the limitations section? Furthermore, the introduction of AI reviewers introduces a novel consideration: could these limitations inadvertently bias algorithmic assessment? Exploring these nuances, as discussed in "My Model Was Cheating on Its Own Test," highlights the complexities of transparency in AI research.