r/MachineLearning

Hello everyone. The new dataset is named MONET, is Apache 2.0 and available on HF: [https://huggingface.co/datasets/jasperai/monet](https://huggingface.co/datasets/jasperai/monet) **MONET is open, Apache 2.0-licensed image–text dataset. It was built from 2.9 billion images and refined to 104.9 million high-quality samples.** We are also publishing [a paper](https://arxiv.org/abs/2605.21272) that explains how the dataset was created if you are curious and 3 compagnions projects * [A umap to visualize the distribution](https://huggingface.co/spaces/jasperai/monet-umap) * [A retreival tool ...

Last month NVIDIA released [SOL-ExecBench](https://research.nvidia.com/benchmarks/sol-execbench), a new benchmark of 235 production CUDA kernels lifted from DeepSeek, Qwen, Gemma, and Kimi. We took several top-ranked AI-generated submissions and tried using them in production workloads. Many of them broke, sometimes in surprising ways. One of those kernels is the fused embedding-gradient + RMSNorm backward pass, which runs at the end of every transformer training step. We took the fastest submission on the benchmark for it, and dropped it into the training loop of a small transformer. The ke...

Looking for communities where people actually dig into ML/AI research, not hype, not "look what I built with an LLM API," but discussions about papers, training dynamics, debugging real models, infra problems, that kind of thing. I'm specifically interested in places where you can post something like "I'm seeing X behaviour in my SSL training, here's the loss curve, anyone seen this before?" and get thoughtful replies instead of generic advice.

Is this normal? I searched it up and last year it was only 8000.

Nathan Witkin critiques METR's Long Tasks benchmark methodology, identifying severe flaws in the widely-cited AI time horizons graph.

Reddit discussion on hyperparameter selection and evaluation metrics for non-contrastive self-supervised learning methods like BYOL and JEPA.

Hugging Face revives PapersWithCode.co with new SOTA tracking features for agents, vision, and time-series; week 1 update.

Community discussion on COLM 2026 review quality and concerns about AI-generated peer reviews.

Disclaimer: I work for Numind, the company behind this open-weight model We just released a 4B model based on Qwen3.5-4B, under Apache-2.0 license. The goal is to make information extraction from complex documents more practical with an open model: PDFs, screenshots, forms, tables, receipts, invoices, multi-page documents, and other visually structured inputs. Try it, we have a huggingface space that is completely free (you don't even have to sign-up): [https://huggingface.co/spaces/numind/NuExtract3](https://huggingface.co/spaces/numind/NuExtract3) If you ever used [NuMarkdown](https://hu...

Reddit discussion: researcher seeking novel VLA directions after discovering concurrent work on equivariant approaches.

Discussion of whether production VLMs use fixed-patch ViTs or adaptive tokenization; explores engineering vs. scaling trade-offs.

OpenAI's reasoning model discovered a counterexample to Erdős's unit-distance conjecture in discrete geometry, disproving a decades-old bound.

Reddit discussion on ML PhD admissions competitiveness and networking requirements across regions.

Hi, I'm interested in geometric deep learning (due to Michael M. Bronstein's book and Maurice Weiler's PhD thesis), and in order not to write projects to nowhere, I decided to keep a technical blog. I started with a short note about machine learning on spherical manifolds, but it's a pretty simple thing. Is there a list of some open problems on the topic of GDL, or maybe some of you are doing something in this direction and can suggest which GDL problems are relevant in the research community.

I've seen TabPFN-3's recent results, and there is a lot of buzz about foundation models for tabular data (TabICL, TabPFN). The performance that those models achieve is really amazing. What makes me a little suspicious about them? They can analyze small datasets only, so a few MB of data, and you need to have a large GPU machine and download a few GB of model to predict on a few MB of data. That doesn't sound rational ... I really miss the old school approach of running a single decision tree or a linear model on the data. What do you think about it? Do you think feature engineering + class...

Proposes separating conference authors and reviewers into independent halves to eliminate reciprocal review bias in AI peer review.

Computer vision researcher reports rejection from IEEE T-PAMI despite three positive reviews, alleging editor misconduct and lack of transparency in peer review process.

Built this over the past few weeks as part of a multilingual research project. Figured I'd share it here. Check it out! \~9.8M web documents across 11 languages — hi, bn, ta, te, mr, gu, kn, ml, pa, ur, en. \~8.4B tokens. CC0 license. 🤗 [https://huggingface.co/datasets/AM0908/indic-hplt-v1](https://huggingface.co/datasets/AM0908/indic-hplt-v1)

**World models** learn compact latent representations for planning without pixel reconstruction. LeWorldModel (LeWM), from LeCun's group at NYU, achieves stable end-to-end JEPA training by enforcing an isotropic Gaussian prior over the full latent space. **The flaw:** real environment dynamics live on low-dimensional manifolds, so a global high-dimensional Gaussian is an overly rigid prior — mismatched to the task geometry. LeWM itself struggles most on low-intrinsic-dimension tasks like Two-Room. **Our fix (Sub-JEPA):** apply the Gaussian regularization inside multiple frozen random orthog...

Hugging Face reviving PapersWithCode with AI agents to auto-parse papers and generate leaderboards; covers Qwen, MTEB, ASR benchmarks.

Undergrad researcher expresses frustration with proliferation of low-quality papers and hallucinated citations degrading AI research culture.

Reddit post alleges Kevin Zhu's Algoverse AI Research program misleads high school students into paid academic misconduct via fabricated NeurIPS publications.

Judea Pearl discusses limitations of data-driven learning, emphasizing hierarchy from correlation to causation to explanation and imagination.

arXiv proposes 1-year publishing ban for papers with hallucinated references and LLM artifacts; community backlash highlights tensions between AI adoption and academic integrity standards.

arXiv implements 1-year ban for papers with unchecked LLM-generated errors like hallucinated references, enforcing author accountability.

Discussion of whether ML papers from 2000-2021 would meet current acceptance standards, exploring if field rigor has increased or just competition.

Van Rooij, Guest, Adolfi, Kolokolova, and Rich [claimed to have proven that AGI via ML is impossible](https://link.springer.com/article/10.1007/s42113-024-00217-5) in *Computational Brain & Behavior* in 2024. The basic idea was to try to reduce a known NP-hard problem to the problem of learning a human-level classifier from data. The purported result, called "Ingenia Theorem" by the authors, made some noise on the internet, including here. My paper showing that the proof is irreparably broken is now [also out in CBB](https://link.springer.com/article/10.1007/s42113-026-00284-w) (ungated ...

Built my own SVM classifier from scratch in Rust. It uses SMO optimization, have linear and rbf kernel, uses grid search to tune the hyperparameters. I tested it on two datasets one using Linear dataset and other using RBF, these were the results: |Dataset|Kernel|Accuracy|Recall|F1| |:-|:-|:-|:-|:-| |Banknote Auth|Linear|96%|94%|95%| |Breast Cancer|RBF|93%|100%|92%| https://preview.redd.it/uw26u1uo0w0h1.jpg?width=720&format=pjpg&auto=webp&s=1784e1d7d310a26fa67efc63fa5191f45433a695 https://preview.redd.it/o0ahkq7p0w0h1.jpg?width=720&format=pjpg&auto=webp&s=dcb1053c3...

Wanted to share our latest paper on an alternative building block for Vision Transformers. [Illustration of our model's accuracy and dense features](https://preview.redd.it/x4acnx478w0h1.png?width=2457&format=png&auto=webp&s=3ce49caf2b0cdea5d35141aebb7297862fdc6a7d) Traditional ViTs utilize dense (***N******^(2)***) self-attention, which can become pretty costly at higher resolutions. In this work, we propose an alternative backbone with a core-periphery block-sparse attention structure that scales as (***2NC + C******^(2)***) for ***C*** core tokens. We further train this usin...

Hello everyone, Presenting at a top-tier conference for the first time and having a very hard time coming up with an appropriate design for my poster. Everything I do seems basic and banal. My paper is more theory-oriented, and apart from putting math formulas in bold in the middle, I am not sure what the best way is to design the poster. Even the sizing choice is complicated as ICML gives 3 different recommendations to pick from, and somehow from my computer, I can’t see how the PowerPoint slide will look like printed on those dimensions. And Printing a poster is nearly $100 CAD, ...

(DISCLAIMER: I accidentally deleted the last post on this subreddit my apologies if this is your second time seeing it) Last year I made a [post](https://www.reddit.com/r/datascience/comments/1lkjxmr/steam_recommender_using_vectors_student_project/) about my steam recommender The last one was great and served its purpose of showing many people new games, But this new version is much more functional! I love making recommendation systems that tell the user WHY they got the recommendation. During a steam sale event, I always find myself trying to look for new video games to play. If I wanted ...

TabPFN-3 was released today, the next iteration of the tabular foundation model, originally published in Nature. Quick recap for anyone new to TabPFN: TabPFN predicts on tabular data in a single forward pass - no training, no hyperparameter search, no tuning. Built on TabPFN-2.5 (Nov 2025) and TabPFNv2 (Nature, Jan 2025), which together crossed 3M downloads and 200+ published applications. What's new: * Scale: 1M rows on a single H100 (10x larger than 2.5).A reduced KV cache (\~8GB per million rows per estimator) and row-chunked inference make this practical on a single GPU * Speed: 10x-10...

PhD student. Need advice. After the ICML abstract deadline, industry coauthors asked to be removed, they missed their employer's internal approval window. They had contributed (discussions and written feedback) but I hadn't explicitly asked before adding them. January: wrote to PC chairs, got written confirmation from all coauthors, got explicit written approval. Chairs said they'd implement. Never happened. Paper accepted four months later with original author list. At camera-ready we followed up. Chairs reversed: blanket policy, no exceptions, keep the list or withdraw. What do you t...

Qwen3.5 0.8B sees 2.88M monthly downloads; user reports semantic understanding, JSON parsing, and latency challenges in production workflows.

An interactive visualisation of Jensen–Shannon divergence - the symmetric, always-finite cousin of KL. Shape two distributions and watch JSD, its ceiling of one bit, and the per-point contribution respond in real time. https://robotchinwag.com/posts/jensen-shannon-divergence-visualisation/ Feedback welcome.

Reddit discussion on whether reproduction/implementation work counts toward graduate admissions—career advice, not technical frontier content.

Reddit discussion thread where ML PhD students share personal work schedules and time management practices.

Katanemo Labs proposes Signals, a lightweight method to identify high-value agent traces without LLM judge overhead, enabling cost-effective agent trajectory filtering.

Reddit discussion on typical publication counts for ML PhD graduates; crowd-sourced meta-analysis of academic output benchmarks.

Engineer critiques transformer architecture limitations for exact reasoning tasks, argues prompt engineering cannot overcome fundamental probabilistic design constraints.

I want to share an interview experience anonymously in case it helps others on the job market. I was approached about a Vancouver ML role that was presented to me as research-oriented. The recruiter told me the team had looked at my research and that I should be ready to discuss my projects, so I expected a conversation about modelling, research ideas, and fit. That is not how the interview felt. It was much more focused on trivia-style and coding-style questioning, with very little real engagement with my research or how I think about problems. The overall process felt much narrower and mo...

DeepSeek V4 full paper details FP4 quantization-aware training, achieving 27% FLOP reduction and 10% KV cache vs. V3.2 with 99.7% recall.

I built a small interactive explorer for building intuition about KL divergence: https://robotchinwag.com/posts/kl-divergence-visualisation/ You control two skew-normal distributions and can see the KL integrand and the KL metric. It’s good for exploring how it changes with a mean offset, skew, truncation and discretisation. It run entirely close side. Feedback is welcome.

Reddit post seeking continual learning research collaborators and paper recommendations.

Undergrad researcher critiques Anthropic's natural language autoencoders approach to mechanistic interpretability, questioning methodological soundness vs. claimed progress.

Researcher reports citation harassment from independent scholar demanding specific wording in academic paper.

Reddit discussion about ECCV reviewer citing author's own prior arxiv work without attribution, requesting comparison to their unpublished version.

Research community reports frequent LLM hallucinations in bibliography generation, with incorrect author attributions despite correct titles, raising integrity concerns.

Reddit discussion about NeurIPS submission volume potentially exceeding 40k submissions.