research
research on Beyond Market Intelligence: a running collection of 72 stories we have gathered and hand-picked because they are worth your time. Every post here touches on research 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 research, 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.
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.
Microsoft kills off unsuccessful AI features while merging its separate Copilot apps
Microsoft is streamlining its Copilot AI offerings, consolidating its consumer and business apps into a single experience. This simplification includes the sunsetting of several AI features, notably AI-generated podcasts, Group Chats, Deep Research, and the Mico character. This move underscores a focus on core functionality and user experience within the evolving AI landscape. As AI continues to reshape workflows, understanding these shifts is critical—a point highlighted in discussions like the recent analysis of AI's impact on engineering progression.
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?
An unreleased Anthropic model made progress on one of math’s biggest unsolved problems
For over 150 years, the Riemann hypothesis has challenged mathematicians as one of the field's most enduring unsolved problems. Now, an unreleased Anthropic model has demonstrated unexpected progress toward understanding this complex concept. While not a solution, this advancement underscores the potential of AI to tackle fundamental mathematical challenges. Explore this significant development and its implications for the future of AI-driven discovery—a topic also examined in our article, "Claude Now Watermarks Everything It Makes," detailing a crucial step in responsible AI generation.
73 NeurIPS workshops, and not a single one on Causality [R]
The absence of causality-focused workshops at NeurIPS 2026, evidenced by the list compiled by Danyal Jafferji, raises a pertinent question: has the field plateaued beyond venues like UAI, AISTATS, and CLeaR? While these remain excellent platforms, the rapid rise of LLMs and agent-based AI appears to have significantly impacted the visibility of several subfields within top-tier conferences. This shift underscores a broader trend in AI research.
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.
![Comparing embedding models with synthetic query probing [R]](https://preview.redd.it/eauhd4hdyiih1.png?width=140&height=47&auto=webp&s=7594a52bcc580426082f61ebb75cecded686b9a9)
Comparing embedding models with synthetic query probing [R]
Evaluating different embedding models—like transitioning from ADA to Titan—can be surprisingly complex. Direct comparison of embedding spaces isn't inherently possible, so how do you determine equivalency or establish useful thresholds for retrieval? Our research addresses this with Synthetic Query Probing, a straightforward method that compares similarity spaces instead. By analyzing similarity scores across models for paired content, we reveal non-linear relationships and varying ranges, as illustrated in our recent paper.
CIKM 2026 decisions [R]
CIKM 2026 decisions are being announced today, and the resource track outcomes have begun to roll out—did you receive positive news? We understand the anticipation and effort that goes into these submissions. This is a significant moment for the AI research community. For those interested in exploring related advancements, our recent article, "Improved compression of Bad Apple into a Neural Network," delves into innovative approaches using SIREN networks. We’ll continue to share insights and analysis as more results become available.
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.
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."
How to file a complaint about a published CVPR paper? [R]
Concerns regarding unfulfilled data release promises in published CVPR papers are increasingly relevant. If a CVPR paper’s core contribution—a dataset—remains unavailable despite conference requirements and author commitments (such as an empty GitHub repository), a formal complaint is warranted. The process isn’t always clear, but it’s essential to ensure accountability and maintain research integrity. Explore the CVPR website and conference guidelines for specific complaint procedures; a lack of dataset availability undermines the validity of the research.
Discovered Materials is playing AI whack-a-mole to hunt cooler chips
Discovered Materials is pioneering a novel approach to chip development, essentially playing “AI whack-a-mole” to uncover superior materials for more efficient semiconductors. The company recently secured $9 million in funding to accelerate this search for groundbreaking compounds. This innovative strategy addresses a critical bottleneck in chip performance, moving beyond traditional material science. As Situational Awareness demonstrated with their $400M investment in Source Foundry, the pursuit of advanced chip technology remains a high-priority area for strategic investors.
Top 5 Claude Skills for Marketing
Claude presents a compelling addition to marketing workflows, particularly by automating ad and email creation—tasks often handled manually. While its generative capabilities are useful, remember that Claude complements, rather than replaces, essential marketing processes like strategic planning, channel selection, and performance reporting. A key challenge lies in navigating the vast landscape of available data, where dedicated marketing resources are often diluted within larger libraries.
Exclusive: Mirendil inks $100M+ Google Cloud deal to scale self-improving AI
Mirendil, a leader in AI-native spreadsheet technology, has secured a significant partnership with Google Cloud, valued at over $100 million. This expansion will dramatically scale Mirendil’s compute infrastructure, fueling research into self-improving AI systems. The focus? Accelerating scientific discovery and propelling advancements in AI development itself. This investment underscores Mirendil's commitment to a future-focused approach to data management. For a broader look at AI’s impact on personalized experiences, explore our piece on how startups are leveraging AI for e-commerce recommendations.
Jeff Dean and other top AI researchers are leaving Google to launch their own startup
A seismic shift is underway in the AI landscape. Jeff Dean, the legendary Google executive, alongside other prominent AI researchers, is departing to launch a new startup focused on accelerating scientific discovery through artificial intelligence. This ambitious venture signals a progressive push beyond traditional computational methods, aiming to transform how research is conducted and breakthroughs are achieved. For deeper insights into the evolving intersection of AI and the physical world, explore our coverage of "TechCrunch Disrupt 2026’s Real World AI Stage."
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.
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.
Is it too late regain some coherence in the ML research space in our life time? [D]
The rapid proliferation of machine learning research—hundreds of preprints appearing daily—has created a fragmented landscape, akin to a chaotic trading floor. This overwhelming influx of novel terminology and often unreproducible findings obscures genuine breakthroughs and fosters a sense of uncertainty. Is it too late to restore coherence to the field, particularly as frontier research increasingly becomes proprietary?
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."
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."
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.
How Symmetric Are the Insides of a Go Network? [R]
A new study explores a fascinating question: to what degree do superhuman Go-playing AI programs, like KataGo, inherently learn board-independent representations despite lacking enforced symmetry? Published on Lightvector.github.io, the research leverages AI-driven analysis and stochastic data augmentation to investigate how these networks handle spatial orientations. The findings, surprisingly, reveal a nuanced picture of learned versus memorized board states. For those interested in visual reasoning within large language models, see our related article, "[R] CausalVLBench: Benchmarking Visual Causal Reasoning in Large VLMs."
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.
How precise are polls really, a Pew explainer on margin of error
Polls offer a snapshot of public opinion, but how precise are they really? Pew Research Center’s explainer clarifies the crucial concept of margin of error, revealing how it impacts the reliability of survey results. Understanding this statistical measure is essential for interpreting poll findings accurately and discerning meaningful trends from random variation. Explore the nuances of polling precision and learn how to critically evaluate data—a skill vital in today's information landscape. For further reflections on navigating complex data, see "Reflections on Airbnb."