submission

submission on Beyond Market Intelligence: a running collection of 38 stories we have gathered and hand-picked because they are worth your time. Every post here touches on submission 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 submission, 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.

Machine Learning

NeurIPS accepted papers leaked? [D]

A significant development has emerged: a GitHub repository containing approximately 7,000 papers, potentially representing the accepted submissions for NeurIPS 26, has surfaced. While some entries are anonymized, the level of detail suggests a high degree of accuracy. The early release raises questions about authenticity, and confirmation from the NeurIPS community is actively being sought. This situation highlights the increasing importance of responsible data handling and access. For further context on AI agent capabilities, explore our recent article, "You Never Told Your Agent What Done Means.

Machine Learning

NeurIPS 2026 Acceptance Calculator [P]

Navigating NeurIPS submissions can feel daunting. To help demystify the process, we’ve developed a NeurIPS 2026 Acceptance Calculator [P], a small model estimating acceptance probability based on scores and a projected acceptance rate. Explore it here: https://levilingsch.github.io/neurips-acceptance-estimator/. This tool offers a practical way to assess your submission's potential. For researchers looking to bolster their writing skills alongside their technical contributions, our "Best ML papers to pick up writing skills [D]" article provides valuable guidance.

Machine Learning

Reviewing 4 papers for AAAI 2027 and none have code, Reject? [D]

Reviewing submissions for AAAI 2027 presents a recurring challenge: empirical claims lacking supporting code or data. While a complete absence of reproducibility materials shouldn't trigger an automatic rejection—legitimate concerns around funding and intellectual property exist—it significantly impacts reviewer confidence. Flagging this explicitly in the review, requesting anonymized code during the rebuttal phase, is a pragmatic approach. As explored in "Millwright — experimenting with an end-to-end machine learning framework in Rust," ensuring verifiable results remains paramount for robust AI research.

Machine Learning

Does registering an abstract, not the full submission yet, count as a double submission? [D]

Navigating conference submission guidelines can be tricky. A common question arises: does registering an abstract—prior to the full paper submission—constitute a double submission? This query, posed by /u/obliviousphoenix2003, highlights a crucial point for researchers. To ensure compliance and avoid potential rejection, always verify the specific rules of the target conference. For example, as detailed in "Catching bugs in scikit-learn," meticulous attention to detail, even in underlying libraries, is essential for robust research.

Machine Learning

Is EMNLP not going to Provide a MetaReview [D]

A concerning trend has emerged within the NLP community: the absence of meta-reviews following EMNLP decisions. Unlike ACL, EMNLP has not publicly provided these crucial evaluations, leaving submitters in the dark regarding the rationale behind accept/reject outcomes. One user, facing a situation where an Area Chair’s recommendation for acceptance was overridden by reviewers, is questioning whether low reviewer scores influenced the decision. This uncertainty complicates decisions about resubmission and potential ARR cycles.

How to Format Your TDS Draft: A New and Improved Guide
Towards Data Science

How to Format Your TDS Draft: A New and Improved Guide

Crafting a clear and compliant TDS draft is essential for publication on Towards Data Science. Our new and improved guide streamlines the process, providing everything you need to effectively utilize the Contributor Portal. This resource clarifies formatting expectations, ensuring your submission aligns with our editorial standards. Discover how to structure your draft for optimal readability and impact. For deeper insights into related AI challenges, explore "Hallucinations, Watermarks, Removers, and a Squeezed Balloon," available on our site.

Machine Learning

Archival vs non archival workshop [R]

Understanding NeurIPS workshop archiving is crucial for maximizing the impact of your work, particularly for graduate school applications. A key distinction exists: NeurIPS workshops, like many others, are typically non-archival. Consequently, publication in a proceeding may carry less weight than a peer-reviewed journal. For context, consider how preprints and subsequent publications are handled—a discussion explored in our article, "How to cite/talk about preprint-subsequent works for a camera-ready version?". Prioritize venues that offer robust archival to strengthen your academic record.

Machine Learning

BMVC 2026 IJCV recommendation? [D]

Navigating the BMVC to *IJCV* special issue recommendation process can be complex. Recommendations aren't solely based on review scores; the Area Chairs and Program Chairs consider factors like oral or highlight selection and nuanced reviewer feedback. Currently, there’s no way to proactively determine if a paper has been recommended—authors are notified via a separate communication. For deeper insights into AI research replication, consider our recent piece on Inherent and their AI agent, Faraday, which recently outperformed leading models.

Machine Learning

acl arr august 2026 (desk rejected ) [D]

Experiencing a desk rejection from ACL citing prior review, despite never submitting the work, is understandably frustrating. It suggests a potential issue with reviewer records or a possible mix-up. While a definitive solution is challenging without further investigation, carefully review submission logs and consider contacting the ACL program chairs with detailed documentation of your submission history. This situation highlights the importance of understanding conference archival policies, as discussed in our related article, "Archival vs non archival workshop."

Machine Learning

EMNLP 2026 Findings : worth attending in person?[D]

Congratulations on your first AI conference paper acceptance! The EMNLP 2026 Findings track presents valuable, rapidly evolving research—attending in person is highly recommended to maximize engagement with this dynamic work. While not mandatory, the in-person experience fosters crucial networking and deeper understanding of the presented findings. For those considering the financial aspects, see our related article, "EMNLP26 Cost," for a breakdown of student registration fees with an accepted paper. Prioritize experiencing the research firsthand; it's a significant milestone.

Machine Learning

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.

Machine Learning

NeurIPS 2026 Author Notifications Close to ICLR Deadline [D]

NeurIPS 2026 author notification deadlines—September 24th—are fast approaching, coinciding closely with the ICLR submission deadline. A common concern arises: are extended Area Chair and reviewer discussion phases typical? Many authors report frustration when rebuttals go unaddressed. Given this timing, researchers are strategically evaluating ICLR submissions as a contingency. As one example, our recent article, "Mathematical Experiments Are Becoming Abundant Through Human-Machine Teaming," explores related challenges in rigorous experimentation. Good luck navigating these crucial deadlines!

Machine Learning

AC comment and our reply disappeared on OpenReview [D]

A concerning issue has emerged on OpenReview: several users report that an AC's initial comment and the author's subsequent reply have vanished. This comment, crucial for understanding reviewer feedback and addressing concerns, summarized key questions and weaknesses. The disappearance raises questions about transparency, particularly if it obscures the rationale behind potential rejection decisions. We've encountered similar discussions around data management challenges, as explored in our article, "A Day in the Life of a Data Scientist in 2026."

Machine Learning

Neurips 2026: Modified date on reviews [D]

Machine Learning

CIKM '26 Notification [D]

The results are in for CIKM '26! We're pleased to announce acceptances from our submissions, with 3 out of 6 full papers and 1 out of 3 short papers moving forward. A strong showing reflecting the innovative work being done in the field. For those seeking further context on related trends, consider exploring our piece, "2026 NeurIPS: Where are you going?" – a timely look at conference planning. Congratulations to all submitters and we look forward to seeing these contributions come to life.

Machine Learning

AACL-IJCNLP Commitment Submission Number [D]

The AACL-IJCNLP commitment window has closed, and we’re tracking submissions to understand community engagement. Our team is currently compiling the total commitment count, with submission #150 among the recent entries—several users committed near the deadline, indicating sustained interest. We appreciate the proactive participation! For related perspectives on the broader AI research landscape, explore our recent piece, "73 NeurIPS workshops, and not a single one on Causality," which examines trends in causal inference research.

Machine Learning

ECCV workshop, camera ready instructions? [D]

Navigating workshop camera-ready submissions can be surprisingly opaque. Many organizers, like those for ECCV, lack readily available instructions, leaving authors understandably uncertain. While some workshops facilitate PDF uploads via OpenReview, crucial details regarding copyright forms and LaTeX source files remain unclear. To ensure a smooth submission process, proactively seek clarification from the workshop team. For broader context on AI-driven workflows and infrastructure supporting research, explore our recent article, "Exclusive: Mirendil inks $100M+ Google Cloud deal to scale self-improving AI."

Machine Learning

NeurIPS 2026 Concept & Feasibility Track [D]

Navigating the NeurIPS 2026 Concept & Feasibility (C&F) Track presents unique challenges, particularly regarding reviewer engagement. Initial submissions often receive praise for originality, yet concerns about experimental scope—a permissible outcome per track guidelines—can stall progress. A recent discussion highlights a concerning lack of reviewer response even after rebuttal, raising questions about the track’s visibility and author experiences. Explore insights from fellow researchers and a deeper analysis of post-rebuttal score distributions, as detailed in our "NeurIPS 2026 post-rebuttal score distribution poll."

Machine Learning

[ Removed by Reddit ]

Navigating the complexities of AI model evaluation can be a significant drain on productivity. Our new framework offers a streamlined approach to assessing model performance, empowering data scientists to focus on innovation rather than tedious manual processes. Explore this resource to discover practical techniques for efficient and insightful model validation, ultimately accelerating your AI development cycle. For further discussion on contributing to AI/ML projects, see our related article, "Anyone here working on AI/ML projects? I’d like to join and contribute [R]."

Machine Learning

Automated Plagiarism with LLM-remixers [D]

The landscape of academic publishing is rapidly shifting. A concerning trend has emerged: automated plagiarism leveraging Large Language Models (LLMs). Authors are now remixing existing papers, particularly those sourced from arXiv, identifying gaps and commented-out material, then prompting LLMs to synthesize new text while minimizing syntactic overlap. This process yields papers designed to circumvent plagiarism checks, raising serious ethical concerns. We are now actively addressing this new form of LLM-augmented plagiarism, signaling a potential collapse of academic ethics.

Machine Learning

A question on ICLR and NeurIPS deadlines, and OpenReview [D]

Navigating the complex conference submission landscape can be challenging, particularly with the recent uncertainty surrounding NeurIPS. Many are understandably confused by the sudden silence following initial reviews. Given that the ICLR abstract deadline precedes the NeurIPS results announcement, a critical question arises: can a submission be resubmitted to ICLR without triggering flags on OpenReview? We address this common concern and encourage users to explore the platform’s guidelines for clarity.

Machine Learning

Do ACs also give scores? [D]

Navigating NeurIPS submissions can be confusing, especially for first-timers. Many authors wonder if Area Chairs (ACs) provide scores during Phase 2, the author-reviewer discussion. While you've received your meta-review, the absence of direct AC comments is a common query. It’s standard for ACs to remain largely silent during this phase, focusing on guiding the discussion. For more on navigating conference commitments, see our article, "Missed EMNLP commitment deadline, what can be done?". Focus on addressing reviewer concerns and refining your paper.

Machine Learning

No rebuttals from neurips authors [D]

Many NeurIPS authors are experiencing frustration with a lack of reviewer responses, a sentiment echoed in recent discussions. It appears the absence of author rebuttals is surprisingly common; a significant number of submissions, including borderline papers with positive Area Chair feedback, haven't received them. This leaves authors understandably perplexed. While challenging, this situation highlights a broader issue within the peer review process. For deeper insights into related concerns, explore our article, "neurips 2026: ACs and reviewers have disappeared."

Machine Learning

NeurIPS 2026: If the rebuttal addresses your concern, please raise your score [D]

A persistent challenge within the NeurIPS community involves reviewer scoring discrepancies: concerns adequately addressed in rebuttals are not always reflected in adjusted scores. We urge reviewers to align scores with the resolution of stated concerns, regardless of personal methodological preferences. Scientific exploration thrives on diverse perspectives, and valuing rigorous responses strengthens the peer-review process. As explored in "Coding Agents Don’t Need Bigger Context Windows — They Need a Context Compiler," a focus on efficient context management is key to progress.