rejection
rejection on Beyond Market Intelligence: a running collection of 10 stories we have gathered and hand-picked because they are worth your time. Every post here touches on rejection 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 rejection, 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.
![[UPDATE - EIC confirmed ghost reviewer]How to get rejected by IEEE T-PAMI with 'Excellent' scores?[D]](https://preview.redd.it/cqkvobgi84oh1.png?width=140&height=116&auto=webp&s=3e5e17d5e15d788954557f4379a98b6b41ce598e)
[UPDATE - EIC confirmed ghost reviewer]How to get rejected by IEEE T-PAMI with 'Excellent' scores?[D]
Navigating peer review can be unexpectedly challenging, as evidenced by our recent experience with IEEE T-PAMI. Despite receiving three "Excellent" reviews, our submission was rejected due to a discrepancy involving a missing fourth review – a situation now formally acknowledged by the Editor-in-Chief following IEEE investigation. This experience highlights critical vulnerabilities within the review process. We've documented this journey and offer insights into how seemingly positive outcomes can still lead to rejection.
*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."
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.
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.
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.
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.
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!
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.
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."
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.