academic

academic on Beyond Market Intelligence: a running collection of 20 stories we have gathered and hand-picked because they are worth your time. Every post here touches on academic 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 academic, 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

Good Machine Learning Posters [D]

Preparing for ECCV 2026 and seeking inspiration for impactful machine learning poster design? You're in the right place. We've gathered a community discussion highlighting exceptional ML/CV posters—a valuable resource for crafting a compelling visual presentation of your work. To further enhance your understanding of current trends, explore our analysis of "Sliding-window attention beats linear on long-context reasoning," demonstrating practical solutions for optimizing large language models. Discover examples and strategies to elevate your poster and maximize its impact at the conference.

Machine Learning

Is anyone esle going to ECCV and wants to get in a groupchat for socials? [D]

Heading to ECCV and seeking connection? This post highlights a common challenge: navigating a large conference when you're not part of a sizable team. One user is actively seeking others to connect with for informal socials and proposes a group chat to facilitate spontaneous gatherings. If you're in a similar situation and looking to expand your network at ECCV, reach out via DM!

Machine Learning

*ACL Findings or TMLR? [D]

Navigating the conference publication landscape presents a strategic challenge. With NeurIPS appearing unlikely given current scores, the decision between Transactions on Machine Learning Research (TMLR) and *ACL Findings* warrants careful consideration. While both venues offer visibility, *ACL Findings* likely presents a higher probability of acceptance. Genuinely curious about industry perspectives: would you prioritize *ACL Findings* or TMLR on your publication record? For deeper insights into related AI discovery research, explore our article on "Autonomous Mathematical Discovery in an Open-World Multi-Agent Environment."

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

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

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.

EMNLP26 Cost [D]
Machine Learning

EMNLP26 Cost [D]

Navigating EMNLP26 costs can be confusing, as highlighted by recent community discussion. For students with one accepted paper, understanding the actual attendance price is key. Current registration rates fluctuate—registering now in August may present a $350 or $550 option. Confirm pricing details directly with the conference organizers for accurate information. Congratulations to all accepted researchers! For those considering career paths in AI, explore "How to Build a Career in AI: 3 Distinct Pathways" for valuable insights.

Machine Learning

Discussion thread for EMNLP 2026 Notifications/Results [D]

EMNLP 2026 notifications and results are expected to be released today – wishing everyone the best as they gather in Budapest! This thread serves as a central hub for discussion surrounding these announcements. We anticipate a lively exchange as the community processes the outcomes. For context, recent developments in AI integration with spreadsheet tools are impacting workflows; for example, Microsoft is retiring the COPILOT function in Excel. Explore the thread for updates and share your insights.

Machine Learning

TMLR Relevance and Prestige [D]

Acceptance to *TMLR* signifies a notable achievement in machine learning research. While *NeurIPS*, *ICLR*, and *ICML* consistently rank as the highest-tier AI conferences, *TMLR* (Transactions on Machine Learning Research) holds considerable prestige as a respected journal. It’s generally considered on par with *JMLR* (Journal of Machine Learning Research) in terms of rigor and impact. Securing publication in *TMLR* demonstrates a commitment to well-validated, theoretically sound work. For further insights into transparency in algorithmic ranking, explore our article on X’s open-sourcing of its ranking algorithm.

Machine Learning

Would you choose a PhD advisor who gives you complete freedom but almost no guidance? [D]

Navigating the landscape of PhD advisors presents a critical decision. Consider this scenario: a fully funded ML PhD with a senior, respected advisor offering near-complete freedom—choose your topics, projects, and collaborations with minimal oversight. However, this autonomy comes at a cost: limited guidance or technical input. Is this a dream setup prioritizing independence, or a dealbreaker due to the lack of mentorship?

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

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 post-rebuttal score distribution poll [D]

Curious about the NeurIPS 2026 post-rebuttal score distribution? With discussions surrounding potentially lower scores this year, a quick poll aims to gauge the average score breakdown after the rebuttal phase—excluding confidence weights. This is a preliminary look, acknowledging inherent self-selection bias. Share your vote here: [https://loppy.be/poll/yczuv8yo](https://loppy.be/poll/yczuv8yo). For deeper insights into NeurIPS trends, see our related article, "NeurIPS 2026 Main Track — Theory papers score tracking post Rebuttal [D]," for specific analysis.

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

Bad but typical NeurIPS experience? [D]

The NeurIPS review process, as highlighted by one researcher's experience, can be a frustrating lottery. Despite conscientious reviewing and generous scoring, unexpectedly harsh reviews and unresponsive area chairs created a deeply discouraging experience. Adversarial reviewer feedback, coupled with a late-stage AC response, underscored the system’s inherent unpredictability and potential toxicity. This highlights a broader issue within the AI research community, prompting discussions around reviewer accountability—as explored in articles like "NeurIPS 2026: If the rebuttal addresses your concern, please raise your score."

Machine Learning

No replies to rebuttals and comments even by AC [D]

A concerning trend has emerged: many submissions are experiencing a complete lack of response to submitted rebuttals, even from Area Chairs. This situation, where feedback isn't addressed during the designated discussion period, undermines the review process. While frustrating, it’s crucial to acknowledge this systemic issue. Our community is actively documenting these challenges – see, for example, "No rebuttals from Neurips authors [D]" for broader coverage. Explore alternative strategies for ensuring your work receives due consideration despite these obstacles.

Machine Learning

Editing Neurips Rebuttal [D]

Regarding NeurIPS rebuttal edits, a clarification is emerging. The post-rebuttal button will transition to an “official comment” status on July 27th AoE. While we anticipate you'll retain the ability to edit your rebuttal after this change, we advise monitoring closely. For a deeper understanding of the NeurIPS meta-reviewer response process, explore our article, "How exactly does the NeurIPS meta reviewer response work?". Stay informed as these crucial deadlines approach.

Machine Learning

NeurIPS 2026 reviews exact timing[D]

The anticipation surrounding NeurIPS 2026 review release dates is understandably high. Many researchers find themselves frequently checking OpenReview, as highlighted by /u/Anshuman3480. While exact timing remains unconfirmed, historical patterns suggest a phased release, typically beginning mid-November. We understand the stress of waiting; staying informed is key. For those tracking submission numbers more broadly, our recent article on "Number of Submissions @ AAAI" offers related insights into the conference timeline. We’ll update this space as official announcements become available.

Machine Learning

NeurIPS reviews coming in soon! [D]

NeurIPS reviews are anticipated to appear around July 22nd at 5:30 PM AoE, based on observations across social platforms. For those who submitted to NeurIPS 2026 – whether to workshops or the main/other tracks – we'd welcome your perspectives on the upcoming reviews. This period marks a critical juncture for researchers. Explore insights into model performance; for example, our recent article on "Schema," a harness achieving 99% on ARC-3, offers a relevant case study in pushing boundaries. Share your thoughts and prepare for the assessments!

Machine Learning

short-paper at ACL/EMNLP/EACL [R]

Navigating the short-paper submission process for ACL/EMNLP/EACL can be challenging. Acceptance rates for these concise submissions often lag behind those of full-length papers, and understanding the landscape is key. We're seeking insights from anyone who has successfully had a short-paper accepted to these prestigious conferences in 2025 or 2026. Sharing your track and overall assessment would be invaluable. Recent developments, like those detailed in "Prism accidentally leaked," highlight the complexities of the AI research pipeline.