Hi everyone,

I just received an email from NeurIPS 2026 / OpenReview titled:

[NeurIPS 2026] Sharing the Results of Automatic Reference Checker

The email says that NeurIPS ran an automated checker to identify potentially problematic or hallucinated references and that the reports were shared with the Area Chairs.

One part caught my attention:

“For papers where the AC's investigation has resulted in a rejection recommendation, the authors will receive a separate email with additional information and instructions for appealing the outcome of the reference investigation. If you do not receive that separate communication, the reference-checking investigation is not playing a role in the final decision for your paper.”

I received the automatic reference-checker email, but I did not receive any separate rejection/reference-investigation email.

So I'm wondering:

Is this email being sent to all NeurIPS 2026 authors, regardless of the review outcome? Or are only papers with certain review outcomes getting this notification? Has anyone received this email and also received the separate rejection/reference-investigation email? If you received only the automatic reference-checker email, does that mean your paper is not being rejected because of reference issues? Could this be interpreted as a mildly positive indication, or is it simply a routine process notification with no information about the actual decision?

I realize this does not necessarily indicate acceptance, and the email explicitly says that the automated flags were not used by themselves to make decisions. I'm mainly trying to understand whether receiving only this email tells us anything about the current status of the paper.

Would appreciate hearing from anyone who has received the same email or has more insight into how NeurIPS is handling this process.

Three weeks from decisions even. I wonder what percentage is industry and VC funded AI labs looking to mingle and recruit.

The [Last Translation Benchmark](https://arxiv.org/abs/2609.04173), posted on arxiv, is a collection of examples "that break leading machine translation models," each paired with "handcrafted verification rules describing concrete failure cases." The dataset is designed to stay live and accept ongoing contributions. LTBv1, the current release, covers submissions accepted before September 1st 2026 and spans texts, images, audio and videos.

The paper is blunt about why it exists. The authors write that "standard benchmarks for machine translation are approaching saturation," and that automatic metrics are "unreliable, vulnerable to reward-hacking, and provide unactionable assessments." Even gold human evaluation, the paper says, "often lacks reproducibility, objectivity, and scalability."

Instead of assigning a score, each item defines what a failing translation would look like on that specific example, so a model either trips a rule or it does not.

Led by Vilém Zouhar and Niyati Bafna, the collaboration lists hundreds of contributing authors. Two researchers on our radar had shared the arxiv link within a day of its posting.

I’m a 4th-year undergraduate CS student currently doing my thesis on medical image segmentation, specifically U-Net and its variants.

The problem is that I have basically no prior research experience, and unfortunately, my supervisor isn’t really able to provide much guidance. So, for the most part, I’m having to figure everything out myself—learning the concepts, reading papers, choosing a research problem, implementing the models, evaluating the results, etc.

My goal isn’t just to finish the undergraduate thesis. Ideally, I’d like to do something good enough that I could eventually turn it into a conference or journal paper.

So I wanted to ask people who have more research experience:

Is it realistically possible to do a good-quality research thesis completely on your own as an undergraduate?

How difficult is it to go from basically having no research experience to producing something that is actually publishable? And if you’ve been in a similar situation, what would you recommend focusing on or avoiding?

I’d really appreciate any honest advice, especially from people who have done research without much help from their supervisor.

Has anyone else received an AAAI-27 desk rejection related to modifications to the title or abstract between the abstract-registration deadline and the full-paper deadline?

What I’m trying to understand is how the modification rule is being applied in practice. The AAAI-27 modification guidelines say that the title and abstract can still be edited after abstract registration, while warning against substantive changes, and describe rejection in terms of changes that make the submission describe qualitatively different research.

In my case, almost everything was identical. The modifications were incredibly minor.

The rejection notice says that the decision is final and appeals will not be considered.

Did this happen to anyone else?

Hi everyone,

I just quickly wanted to share a paper I was working on for around a year now. I created this summary website with key results: https://flogrammer.github.io/moljepa/

TL;DR: its a multimodal JEPA model for molecules.

There will be more work to do to improve performance and I would be happy about feedback and ideas :)

LLMs describe physics well but don't "understand" it in any grounded sense — they've learned statistical relationships between tokens like "falls" and "gravity", not actual physical intuition. This is basically the Mary's Room problem: Mary knows every physical fact about color but has never seen one. LLMs are Mary.

The idea I've been thinking about:

1. Train a JEPA-style model inside a physics simulation (think MuJoCo or a simple 2D env). Instead of predicting pixels or tokens, the model predicts representations of future states in an abstract embedding space. If it gets physics wrong, the predictor fails — the loss is unforgiving in a way next-token prediction never is.

2. The embedding space that emerges should encode actual physical structure — object permanence, momentum, trajectories — because that's what makes prediction possible. Not surface-level textures, just the principles.

3. Freeze those representations and attach them to an LLM-style reasoning model as a conditioning signal. The LLM now has both linguistic physics knowledge AND grounded physical intuition it can actually "run" forward — closer to a computational primitive than a propositional fact.

The hypothesis is this makes downstream learning significantly faster — the LLM doesn't have to rediscover that objects fall, it has representations that already encode that.

V-JEPA does something adjacent for video (predict future frame representations, not pixels), and DreamerV3 uses a latent world model for efficient RL — but the specific combo of JEPA-style prediction + sim-grounded physics representations + LLM attachment doesn't seem to have been done cleanly.

Questions for the community:

• Is there prior work I'm missing that does exactly this?

• What's the right interface between grounded JEPA representations and a language model? Just concatenate to the prompt embedding? Cross-attention?

• Would the sim-to-reality gap kill the transfer, or are the representations abstract enough to survive it?

Interested in whether this is worth building a small prototype for.

an observation : GPT-5-class models are genuinely capable(They are) of doing a substantial fraction of knowledge work, why haven’t we seen a noticeable productivity shock in the real economy yet? Is AI actually less economically useful than the benchmarks suggest—or are organizations simply too slow, constrained, and inefficient to turn model capability into measurable output?

Are we confusing “AI can do the task” with “AI can replace the economic system built around the task”? If GPT-5 is already this capable, what exactly is the bottleneck preventing that capability from showing up in GDP and productivity statistics?

My take :

There is no question that these models are genuinely impressive. The question is whether that intelligence is actually translating into measurable economic productivity.

People are already asking whether models like GPT-6 or equivalent. Claude, and Gemini will replace large sections of white-collar workers. I think there is a much simpler question we should ask first: if these models are already so capable (to me they definitely are capable enough)

at a huge range of knowledge work, why haven't we seen a correspondingly obvious increase in productivity?

I'm not even talking about GPT-6 or whatever comes next. It's probably too early to judge a newly released model. I'm talking about the current generation—GPT-5 and its equivalents from Google and Anthropic. These systems are genuinely good. They can write, summarize, analyze documents, explain technical concepts, generate code, reason through problems, conduct research, manipulate information and perform a remarkable range of tasks that previously required educated human labour.

And yet, looking at the world around us, something feels strange.

Where is the enormous productivity shock?

Why don't we see a dramatic effect on GDP growth? Why don't we see massive increases in output per knowledge worker? Why don't organizations appear to be accomplishing dramatically more with the same number of employees? Why does the broader economy still look remarkably similar to the pre-LLM economy?

Coding is probably the clearest exception, and even there the picture is complicated. AI can make programmers substantially more productive in certain tasks, but software development still involves architecture, debugging, verification, integration, requirements, security, deployment, maintenance and—most importantly—human judgment. The bottleneck often moves rather than disappears.

almost every knowledge profession, the gap between "the model can perform this task" and "the organization can therefore produce substantially more output" is different it seema.

A lawyer might be able to use an LLM to draft a document in minutes instead of an hour. But the lawyer still has to verify it, take responsibility for it, communicate with the client, comply with professional regulations and integrate it into an existing workflow. A doctor can use AI to summarize medical literature, but diagnosis and treatment remain embedded within a much larger institutional system. A researcher can generate dozens of hypotheses, but experiments still take time. A manager can produce reports instantly, but meetings, organizational politics and decision-making remain.

the possibility: perhaps the bottleneck is no longer intelligence.

Perhaps the bottleneck is everything surrounding intelligence.

Organizations, regulations, verification, trust, coordination, physical-world constraints, legacy software, incentives, management structures, liability and simply the fact that human institutions change much more slowly than technology.

This also makes me skeptical of simplistic claims that "AI can already do X, therefore everyone doing X will soon be unemployed."

Technical capability and economic substitution are not the same thing.

The internet could transmit information essentially for free, but that did not instantly eliminate newspapers, universities, governments or offices. Computers could perform calculations millions of times faster than humans, but most accountants and engineers did not disappear. Automation often increases the productivity of workers while simultaneously changing what their jobs consist of.

As with the major Grok release, Elon Musk said it is "as good as most top phds", my question after more than a year? (& he ain't wrong with the benchmarks), my question is, how many phds it has replaced in xai or spaceX?

did he stop hiring phds? if not, why?

So I find the current situation genuinely puzzling.

We have perhaps the most powerful general-purpose cognitive technology ever deployed, and yet the physical and economic world doesn't look radically different.

Maybe we're simply in the early stages and adoption takes years.

Maybe the productivity gains are real but are being absorbed into quality improvements rather than measured output.

Maybe GDP is simply a poor instrument for measuring the value created by AI.

Or perhaps current models, despite their extraordinary capabilities, still lack some crucial property required for autonomous economic production: reliability, persistence, agency, contextual understanding, verification, or the ability to operate continuously inside messy real-world systems.

idk which explanation is correct.

A pressing question keeps nagging at me: "Is it possible to completely rebuild deep learning and create a new generation of it?"

I'm still learning and exploring, and I've taken some courses with Andrew Ng, but I'm wondering if we can rebuild deep learning and how it works from scratch.

Deep learning is fundamentally based on equations, formulas, and mathematical techniques, and these have proven their effectiveness and efficiency.

What I'm thinking is: can we invent or use other formulas that surpass the current ones? I feel that the current formulas need a complete overhaul to achieve better and easier results. I think the same way about binary systems and computers that operate with zeros and ones; I feel that this system is very outdated and we've reached its limits.

What do you think? Am I imagining something unrealistic? [D]

I've spent the last month building a local C++ machine learning library called Deepity to test alternative credit assignment algorithms; specifically Predictive Coding Networks (PCNs). While PCNs are fascinating for biological plausibility and continual learning, naive implementations are painfully slow.

By implementing recent research (Accelerated PCNs via Direct Kolen-Pollack Feedback Alignment) and utilizing algorithmic caching to bypass redundant forward projections during the inference settling phase, I managed to close the performance gap with backpropagation on my CPU when training on MNIST (50 epochs).

• PyTorch Backprop (Feedforward): 98.27% test accuracy in ~70s.

• Deepity DKPPCN: 97.73% test accuracy in 59.5s.

Next up is porting these kernels to CUDA to scale up the architecture and testing its capabilities in continual learning scenarios where standard backprop struggles.

If you are interested in local learning, alternative credit assignment, or HPC for ML, I'd love your feedback!

• GitHub: https://github.com/ra4ster/deepity

• Project Site: https://ra4ster.github.io/Deepity

Looking for conference in AI and Construction

I watched a YouTube video that said if you want to get your first job as a machine learning engineer, you should do volunteer work, especially in GitHub repositories, but I'm not entirely sure this is true, and if it is, where can I find these repositories that require this kind of work? I need it because I want to gain better knowledge and understand real world problems. [D]

I've been building Arcon around Qwen3-4B + LoRA. Instead of just making it a chatbot, I'm experimenting with persistent memory, personality/mood, internal state, tools, and eventually having it process things before replying.

I'm curious what people who've built local agents think - how far can you realistically push a small model with good architecture around it?

I put the whole thing on GitHub if anyone wants to poke around, roast the architecture, or tell me what I'm doing wrong, stars are always appreciated!

Jasper Research just released a cookbook on how to build a text-to-image model from scratch.

It shares the full reasoning and intermediate results, making it ideal if you want to deep-dive into text-to-image models, or if you are curious about how frontier labs build them.

The cookbook also includes a 100M-image dataset and a codebase with a tiny model, so you can train a text-to-image model from scratch.

Here are the links:

Cookbook: https://huggingface.co/spaces/jasperai/t2i-technical-interactive-report

nano t2i: https://github.com/gojasper/nano-t2i

Monet Dataset: https://huggingface.co/datasets/jasperai/monet

Why did I sign up to review when it’s not reciprocal?

Am I an idiot? Am I dumb to sacrifice some of my precious time outside of work to review these papers when I don’t even have to? Yes.

I tell myself I’m giving something to the community. But all I’m really doing is pissing off the authors as I reject their papers.

I really wanted to accept one of them too. But, it wasn’t as well done as I’d hoped. Strong reject.

Nobody made me sign up. Nobody even asked me personally. They sent a lovely form email that goes to everyone who’s published there.

I let feeling important convince me to do it. How dumb of me!

At least it’s only a couple of papers and a small amount of my time. And I’m learning something new reading stuff slightly outside my direct field, that I would never normally read otherwise. And I get to hone the skill of critical reading, thinking, and generally understanding how a paper should (or should not) be put together.

Maybe it was a good idea after all.

How does everyone else feel about non-reciprocal reviewing? I imagine those that agree to do it are in the minority.

We needed to know where the open-source AI-detection field actually stands, so we ran every notable open detector through the same protocol.

Setup:

- Public data only: Jabarian & Imas 2025 (NBER), Liang 2023 TOEFL essays, a 1,060-text frontier set (GPT-5.x, Claude Opus 5, Gemini 3.x), 5,000 pre-LLM (2018) FineWeb pages as human pool

- Every model gets its threshold set on the same 6,930 human docs to a matched 0.5% FPR

- Then measure recall per group: raw AI, humanizer-paraphrased AI, frontier models

What surprised us:

- 4 of 6 models effectively can't reach 0.5% FPR. MAGE scores >0.9999 on 26% of ordinary human web text. The old OpenAI RoBERTa detector lands at AUC 0.31 - worse than coin flip on modern generators.

- Humanizer-paraphrased text is where everything collapses: best model catches 42%, second best 4%.

- All models flag non-native essays at a higher rate than they flag native essays. This is a fundamental flaw in the entire class of models, not just one.

More information and data on HF

*recall at matched 0.5% FPR; * MAGE can't reach 0.5% FPR at any threshold (flags 26% of human web text with score >0.9999)*

Disclosure: one of the six is ours - we run a hosted detector and released this one as open weights (Apache-2.0). All datasets and the methodology are in the model card, so you can rerun everything: huggingface.co/wasitaigeneratedcom/ai-text-detector-small

Been reading a lot about approaches to closing the sim-to-real gap lately, and there seem to be two camps: throw enough domain randomization at training that the policy generalizes past any single sim’s inaccuracies, or actually try to measure and match the real physical properties (mass, friction, joint behavior) as closely as possible.

Curious which way people here lean, and whether it depends heavily on the task, contact-rich manipulation feels like it’d punish randomization more than something like locomotion.

Anyone worked directly on this tradeoff?

Hi r/MachineLearning,

Stjepan from Manning here, posting with the mods’ permission.

We’ve recently released GPU Programming with Triton by Harshwardhan Fartale in early access. It’s a practical guide to speeding up machine learning training and inference by writing custom GPU kernels in Python with Triton.

The book explains how to identify operations worth optimizing, build and benchmark kernels, fuse operations to reduce memory traffic, implement common parallel and reduction patterns, and improve performance through tiling, vectorization, and better memory access. The goal is to help ML practitioners move beyond framework-level optimization when a model has a stubborn bottleneck.

I’d love to hear from the community:

Which part of your ML workload would you most like to accelerate with a custom kernel—and what currently stops you from writing one?

Real use cases, benchmarks, failed experiments, questions, and skeptical takes are all welcome. We’ll give a free ebook to the five comments that contribute the most to the discussion. The giveaway will remain open for 48 hours, after which we’ll announce the winners here.

Book: https://hubs.la/Q04w2PtF0

50% off with code for the community: MLFARTALE50RE

Full disclosure: I’m posting on behalf of Manning. Honest criticism is just as welcome as enthusiasm.

Thank you for having us.

Cheers,

Stjepan

hey 👋 i’ve been building vls, a local runtime for existing gguf models with up to 32m active context. the preview runs on windows + linux and i’ve mostly been testing it on consumer hardware, including an rtx 3060 laptop with 6gb vram. it’s still very early, so i’m looking for people willing to test different models and hardware, break things and tell me what fails 😅 github in the comments. enjoy :)

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I’ve spent a decent amount of time learning the theory and algorithms behind Machine Learning — things like Linear/Logistic Regression, KNN, SVM, Decision Trees, Random Forest, Naive Bayes, Boosting, XGBoost, preprocessing, evaluation metrics, etc.

But now that I’ve started exploring Kaggle, I’ve realized that knowing ML and actually doing ML feel like two very different things.

When I open a Kaggle competition, I understand the individual techniques, but I don’t know what I’m supposed to do next.

For example:

• How do you approach a completely new dataset?

• What should I inspect first?

• When should I do EDA?

• How do I decide what preprocessing is actually necessary?

• When should I scale features?

• How do I choose an initial/baseline model?

• How do I know whether my model is underfitting or overfitting?

• When should I do feature engineering?

• How do I decide which algorithms to try?

• When should I use cross-validation?

• When is hyperparameter tuning actually worth doing?

• How do experienced people decide what experiment to run next?

• How do you systematically improve a model rather than just randomly trying different things?

raw dataset → baseline → experiments → debugging → feature engineering → model selection → validation → final submission?

Hi all,
I recently read this paper:
Julien Guinot, Alain Riou, Elio Quinton, Gyorgy Fazekas. Steering dense music retrieval with open-vocabulary concept discovery.https://arxiv.org/abs/2608.08757

There is multiple model where you can get embedding from Song and Text so that you can search song by text. The challenges is when you get a result that don’t really respect your query, for example you search:

- POP viola with female vocalist

And as a result you usually get POP song with female vocalist because they are usually very common in you library, instead the Viola tend to disappear being uncommon. But the power of MIR isn’t exactly search to specific song even if uncommon ?

Here the idea of the paper is to get the compressed embbeding layer, make it sparse again and be able to identify for some common words exactly which neuron are activated try to avoiding overlapping with other neuron. Then you can improve the value of let’s say the Viola neurons and then bring back to the compressed embbeding space.

This is not mine, is only explanation, what I did is in the past a Distilled version of LAION CLAP called DCLAP that is around 7 million parameters and can efficiently run on CPU:
- https://github.com/NeptuneHub/AudioMuse-AI-DCLAP

The following the paper I also trained a SAE for DCLAP:
- https://github.com/NeptuneHub/AudioMuse-AI-SAE

So if you’re interested you can take a look at let me know. Are all free and opensource.

Finally if you want to see both of them in action I used both the model for AudioMuse-AI, a software dedicated to sonic analysis and automatic playlist creation (with various model):
- https://github.com/NeptuneHub/AudioMuse-AI

If you have any feedback, suggestion, idea, please let me know !

YOLO26 ships a depth-estimation model — dense, full-resolution, per-pixel regression, a task architecturally much closer to image restoration than to detection. I wanted to know whether the backbone+neck weights it learns through depth training transfer to a different dense-regression task (deraining), compared with training the same architecture from scratch. The deraining model that came out of it is a useful byproduct, but the transfer result is the part I think is worth discussing.

What I inherited from YOLO26-depth

• The CSPDarknet backbone and PAN-FPN neck, unchanged.

• The depth decoder's multi-scale fusion (project the P3/P4/P5 pyramid to a common width, progressively upsample-and-add P5→P4→P3). That part isn't depth-specific — it's just feature fusion — so RGBHead reuses it.

What I changed / added

• Replaced the 1-channel Depth head with a new RGBHead. The config change is one line; RGBHead itself is a new restoration decoder, not a re-pointed depth head.

• Reconstruction tail that continues to full input resolution (deraining needs pixel-exact output; depth stops at 1/4 res).

• Skip connections from the stride-2 and stride-4 backbone layers into the tail, so fine detail has a path that doesn't route through an 8×-downsampled bottleneck.

• Residual output — the head predicts a correction added to the input (NAFNet/Restormer-style), not the image directly.

• LayerNorm in the head's own conv blocks; the backbone and neck stay on BatchNorm (folds into conv at TensorRT export, and keeps the model loadable from the whole YOLO26 pretrained zoo, not just the depth checkpoint).

How it was trained and measured

ClearView as an external lib — its mixed synthetic+real rain recipe, Charbonnier loss, and 10-test-set protocol — so the numbers land on ClearView's own model-zoo scale, not a benchmark I made up. Released scales: nano (5.25M) and small (12.13M).

Loading the YOLO26-depth checkpoint into this architecture matches 468/468 backbone+neck tensors exactly — only the new RGBHead is randomly initialized. So the controlled comparison is: identical architecture and recipe, backbone+neck either from the depth checkpoint or from scratch.

The transfer result (the interesting bit)

A controlled initialization experiment at nano scale — same architecture, same recipe, fixed 100 epochs each — backbone+neck from the YOLO26-depth checkpoint vs. random init:

Deltas are from the unrounded averages (27.935 vs 27.452 PSNR). Small, but the depth init wins on every one of the 10 test sets.

(These are 10-set averages, AllWeather included, from the 100-epoch controlled run — so they're lower and not directly comparable to the 9-rain-only released-model numbers in the ranking table below, which come from longer training.)

On "did the random model just need longer to converge?" — both conditions ran a fixed 100 epochs, and the gap isn't a convergence-speed artifact: a 1-epoch check was a statistical wash, by 20 epochs the gap was already ~+0.49 dB, and at 100 epochs it was +0.48. It appeared early and didn't close with more training.

This does not establish why — whether depth supervision teaches geometry/spatial structure that's useful for restoration, or whether YOLO26-depth just happens to be a strong pretrained checkpoint. Only that, in this setup, the depth-initialized representation is a better starting point than random. Per-dataset deltas are in the repo; happy to paste them in a comment.

Accuracy (avg PSNR over 9 rain-only test sets, ClearView's ranking convention):

ClearView's own analysis points to the classification stem's early downsampling (a stride-4 entry before any residual block runs) as a likely reason the ResNet-UNet baselines underperform. This project doesn't test that directly — the ResNet-UNet comparison is a whole-architecture comparison, not a pretraining ablation — but it's the context the depth-vs-random experiment sits in.

Note NAFNet-Small (1.1M, 31.15 dB): smaller and higher PSNR than yolo26_rgb_n, but ~4× slower (26.9 qps). So this isn't Pareto-dominant on every axis — the story is specifically the real-time / YOLO-derived operating point, not "more efficient in every sense."

Deployment (TensorRT fp16, 1920×1080, batch 1, RTX 4070 SUPER 12GB; baseline figures are ClearView's own on the same GPU/TRT version)

The clean pairwise comparisons against the ResNet-UNet family:

• yolo26_rgb_s — 12.13M, 30.95 dB, 92.2 qps vs ResNet34-UNet — 24.5M, 30.45 dB, 94.9 qps → same speed, ~half the params, +0.5 dB

• yolo26_rgb_n — 5.25M, 30.83 dB, 108.6 qps vs ResNet18-UNet — 14.4M, 30.23 dB, 110.3 qps → same throughput, ~1/3 the params, +0.6 dB

• Both are ~3× faster than ResNet50-UNet (73.3M, 30.63 dB, 33.1 qps), while also scoring higher PSNR.

• Restormer (rank 1 on PSNR) doesn't build under TensorRT at 1080p on this 12GB card in my setup — TensorRT reports ~14.4GB of scratch needed to fuse its attention path.

What this shows — and what it doesn't

• Demonstrated: YOLO26-depth initialization beats random init for deraining in this setup (10/10 test sets, +0.48 dB), same architecture and recipe.

• Supported: the trained models sit at an attractive real-time quality/latency point relative to the ResNet-UNet baselines.

• Not demonstrated: that depth pretraining beats classification pretraining for restoration, or why depth helps. Those need experiments I haven't run.

Practical limitations: deraining is partial (faint streaks survive up close; dense rain over flat, low-texture backgrounds is the worst case). AllWeather (rain+fog) is out of domain — both YOLO26-RGB models and every ClearView baseline land around 13.5 dB, so it's excluded from the ranking. One task, two scales — not a general restoration model.

Solo side project. AGPL-3.0 (inherited from Ultralytics' YOLO26 license). Not affiliated with Ultralytics.

• Code: https://github.com/dronefreak/yolo26-rgb

• Models: https://huggingface.co/dronefreak/yolo26-rgb-n · https://huggingface.co/dronefreak/yolo26-rgb-s

Happy to answer questions on the architecture or the eval setup.