appendices
appendices on Beyond Market Intelligence: a running collection of 3 stories we have gathered and hand-picked because they are worth your time. Every post here touches on appendices 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 appendices, 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.
AAAI 2027 Review: No code submission? [D]
AAAI 2027 paper reviews have revealed a concerning trend: a surprisingly low number of submissions include accompanying code. This deviates from AAAI's explicit emphasis on reproducibility and raises questions about the rigor of some submissions. While initial scoring will reflect this omission, we seek community input. Providing code fosters transparency and allows for validation – a practice we strongly advocate, as evidenced by our own consistent code sharing on ArXiv.
Conference Reviews: Asking Too Much? [D]
Conference reviews sometimes request additions that extend beyond a paper's page limit, a practice particularly prevalent at top-tier events. While these expansions can be valuable, they often better suit journal publication—a concern that recently led one author to retract a submission. Does this approach inadvertently hinder future journal opportunities? We invite discussion on whether such additions are a conference review quirk or a sign of a broader disconnect.
Paper lengths, and reasonable assumptions in ML conferences. [D]
Observations regarding paper lengths and reviewer feedback at top ML conferences reveal a concerning trend. While conferences maintain consistent paper lengths – often supplemented by extensive appendices to mitigate reviewer fatigue – theoretical work appears unfairly penalized. Increasingly, rejections cite issues like perceived difficulty or unexplained terminology, rather than addressing the core impact of the research. This echoes experiences where inherent complexity is mistaken for a flaw. As highlighted in "NeurIPS 2026 AI-generated reviews," understanding these dynamics requires careful consideration.