Beyond Market Intelligence/academic publishing

academic publishing

academic publishing on Beyond Market Intelligence: a running collection of 9 stories we have gathered and hand-picked because they are worth your time. Every post here touches on academic publishing 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 publishing, 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

I regret reviewing for AAAI [D]

Reviewing for prestigious conferences like AAAI can feel like a significant time investment, particularly when reciprocity isn’t guaranteed. A recent Reddit post articulated a common sentiment: the allure of feeling valued can outweigh the practical realities of dedicating time to evaluating work that doesn’t directly benefit one's own submissions.

Machine Learning

How to cite/talk about preprint-subsequent works for a camera-ready version? [R]

Navigating citations when a paper transitions from preprint to a conference camera-ready can be nuanced. To maintain both novelty and acknowledge impactful subsequent work, consider citing your preprint initially, then clearly state it's the precursor to the current publication. Acknowledge any works building upon your preprint’s methodology, demonstrating its influence. This approach transparently reflects the research lineage. For further insights into related challenges in AI research integrity, explore "AAAI 2027 Reviewer Bidding and Assignment Integrity [D]" for a deeper understanding of evolving ethical considerations.

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

For the people who got reviews back from neurips, cvpr, eccv, etc and also tested their paper through an agentic reviewer like the stanford one, how different were the reviews? [D]

For those who recently received reviews from NeurIPS, CVPR, ECCV, or similar conferences, and also utilized agentic reviewer tools like the Stanford model, a compelling question arises: how do the reviews compare? We're exploring the divergence between human and LLM assessments, seeking insights into this evolving landscape. Early indications suggest significant variations, prompting a deeper understanding of how AI-assisted review impacts the peer review process. For further context on related challenges, see our article, "My Model Was Cheating on Its Own Test."

Machine Learning

Completely dead NeurIPS review period from both ends? [D]

A concerning trend has emerged during the NeurIPS review period: unusually prolonged silence from both reviewers and authors. Reports indicate reviewers abandoning the process post-initial reviews, while authors remain unresponsive even without submitting rebuttals. This phenomenon, observed across multiple submissions, raises questions about shifts in academic publishing practices—potentially a strategy of widespread submission with limited follow-through. As noted in a related discussion on score tracking post-rebuttal for theory papers, NeurIPS 2026, maintaining engagement throughout the review cycle remains crucial.

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

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

Pattern Recognition (Elsevier): "With Editor" status date changed, but status didn't. Is this normal? [R]

Many researchers encounter unexpected nuances within Elsevier's Editorial Manager system. A recent query highlights a common observation: the status date updating while the visible status—in this case, "With Editor" for a *Pattern Recognition* manuscript—remains unchanged. While this can be initially perplexing, it’s often a procedural artifact rather than an indication of stalled progress. To understand typical timelines after this stage, and broader considerations within AI research, explore our related article, "NeurIPS 2026 AI-generated reviews," for further insights.

Machine Learning

TACL journal doubts [D]

Navigating the TACL review process can understandably generate questions. Submitting around June 1st for the July cycle suggests reviews may arrive within the subsequent weeks, though timelines can vary. Historically, the full TACL publication process takes several months. TACL holds considerable respect within the NLP community, viewed as a strong venue for impactful research. Its reputation reflects a rigorous review process and high publication standards. For those exploring related avenues, consider reviewing discussions around short-paper submissions at ACL/EMNLP/EACL, as detailed in a recent article.