reviewers

reviewers on Beyond Market Intelligence: a running collection of 24 stories we have gathered and hand-picked because they are worth your time. Every post here touches on reviewers 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 reviewers, 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

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.

Machine Learning

[N] EACL 2027 Industry Track - Deadline 11 September [N]

The EACL 2027 Industry Track offers a vital platform to showcase practical insights and emerging challenges in deploying language technologies. We invite submissions from industry, government, and non-profit organizations—those building real-world applications beyond the core NLP community. Papers, limited to six pages (excluding references and appendices), require a dedicated "Limitations" section for acceptance. The deadline is approaching: **September 11, 2026**. For details, see the full CFP and consider contributing as a reviewer.

Machine Learning

Rejected at EMNLP with decent scores. What can be done next? [D]

Facing rejection at EMNLP, even with promising scores (average 2.83), can be disheartening, especially for a first-time solo author. The key now is strategic action. Prioritize resubmission to ACL Rolling Review (ARR) to leverage existing reviewer feedback—though anticipate new assignments. Given your need for timely publication for internships, a swift ARR resubmission is likely the most effective path. Consider how low-capacity networks can acquire fine-grained scoring, as explored in "Estimating from No Data: Deriving a Continuous Score from Categories," for potential avenues of improvement.

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

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.

Machine Learning

Neurips 2026: Modified date on reviews [D]

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

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

Missed EMNLP commitment deadline, what can be done? [D]

Facing a missed EMNLP commitment deadline after a positive ARR review can be frustrating. Many researchers encounter deadline complexities with OpenReview systems, as highlighted in discussions around ICLR and NeurIPS. While responsibility rests with the submitter, the sudden shift in deadlines warrants immediate communication with the Program and Workflow Chairs. Given the anticipated volume of submissions, explore all avenues for recourse, emphasizing the circumstances. Prompt action, as you've already taken, demonstrates a commitment to the process.

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

NeurIPS 2026: Tips that might convince AC? [D]

Navigating NeurIPS acceptance with initially positive reviews, followed by a score decrease despite addressing reviewer concerns, can be frustrating. Authors facing similar scenarios—particularly those with average reviewer scores around 3.5—often find the Area Chair (AC) plays a crucial role in final decisions. Focus your efforts on a compelling meta-review response, clearly articulating how your revisions mitigate identified weaknesses. While AC engagement can vary, proactive communication highlighting your responsiveness is key.

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

It's time to desk reject papers that don't include code that can reproduce the results [D]

A concerning trend is emerging from recent conference review seasons: a significant lack of reproducible code accompanying submitted papers. Across 12 reviews this year, only one provided complete, runnable code, while seven offered none at all. This severely impacts quality assurance and reproducibility, with even partial code often containing critical bugs. Incentives currently favor code concealment, but a shift towards penalties for non-disclosure is needed to ensure rigorous scientific standards.

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.

Machine Learning

Question about NeurIPS discussion phase [D]

Navigating the NeurIPS discussion phase can be unpredictable. A common question arises: how often do reviewers update scores after indicating concerns are resolved? Experience suggests it’s less frequent than one might hope, particularly when initial engagement is limited. You're not alone in observing this—others have noted similar patterns. Our community has explored this dynamic further in "Conference Reviews: Asking Too Much?" As your case demonstrates, persistence can yield results, ultimately leading to score adjustments.

Machine Learning

Neurips 2026: does every metareview recommend accept/reject? [D]

Navigating NeurIPS decisions can be perplexing. A recent discussion reveals a surprising trend: some metareviews already include an accept/reject recommendation—often a rejection. Your team’s experience, with a metareview expressing cautious optimism despite reviewer disengagement, highlights this complexity. While a strong rebuttal is crucial, the lack of engagement raises questions about future prospects. As explored in "No rebuttals from NeurIPS authors," reviewer responsiveness remains a significant challenge. Consider carefully whether continued hope aligns with the current situation.

Machine Learning

neurips 2026: ACs and reviewers have disappeared [D]

NeurIPS 2026 reviewers and Area Chairs have inexplicably vanished, leaving several submitters in a state of uncertainty. Early rebuttal submissions, made via the designated "Rebuttal" button before the official discussion period opened, appear to have triggered no notifications, a problem also experienced by reviewers. Despite attempts to utilize meta-comments, reminders, and direct PC contact, responses remain absent with only one day left in the review cycle. This situation, impacting potential oral and spotlight candidates, highlights a systemic issue.

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

How exactly does the NeurIPS meta reviewer response work? [D]

Navigating NeurIPS meta-reviewer responses can be complex, especially with recent updates. Initially, authors were directed to AC confidential comments, but a recent announcement now requires posting answers to initial meta-reviews as comments on the July 28th thread by August 3rd – a shift designed for reviewer visibility. Clarifying whether this new option opens immediately, as the rebuttal period concludes, is crucial. Essentially, the process seems to demand public posting for reviewer access, rather than private AC updates.

Machine Learning

NeurIPS 2026 AI-generated reviews [D]

The NeurIPS 2026 paper on AI-generated reviews has sparked considerable debate, particularly regarding the ethics of leveraging LLMs in the peer-review process. Author /u/bricklerex raises a critical point: beyond the study itself, what action is being taken to address potentially problematic AI-assisted reviews? While outright plagiarism is unlikely, concerns exist about superficial engagement with submitted work and the potential for meta-reviewers also utilizing LLMs. For a deeper understanding of the NeurIPS meta-reviewer system, explore "How exactly does the NeurIPS meta reviewer response work?"

Machine Learning

Link plots/figures in NeurIPS rebuttal [R]

Reviewers at NeurIPS requested additional experiments best visualized through plots and figures, a format often more digestible than tabular data. While OpenReview’s technical guidelines restrict external links, experienced submitters sometimes leverage this for clarity. Proceeding cautiously is advised; a minor infraction is more likely than outright rejection, though outcomes vary. Consider the DONUT text extraction model, as discussed in a related article, for inspiration on effectively presenting complex data. Ultimately, advocate for OpenReview’s adoption of modern markdown to support figure embeds directly.

Machine Learning

Happy openreview refresh day to all those who celebrate [D]

Happy refresh day to the [D] community—may the odds be ever in your favor! As a NeurIPS Area Chair, this year's incentive structure appears to be yielding positive results, significantly reducing the need for reviewer follow-up. This marks a notable improvement over the past five years of Area Chair experience. Let's hope for active participation in discussions as well. For further context on the broader AI landscape and the challenges it presents, explore our interview with the Substack CEO on "The AI Slop Problem."

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!