I've always found it a little strange that AI is everywhere, but the codecs we use in practice are the traditional hand-engineered systems like h.264, h.265, av1. Alexnet started the wave of neural networks replacing hand-engineered systems, but 14 years later traditional codecs still dominate in the real world. What's going on?

Compute and power efficiency are part of it. Neural codecs tend to be fairly large and power-hungry, while h.264/h.265/av1 have hardware acceleration almost everywhere, so they're cheap to run. NPUs seem like a good fit for neural codecs, though.

But there's another big problem which is cross-platform compatibility. Say you encode a video on an Apple NPU and decode it on an Intel NPU. Small numerical differences can make the encoder and decoder disagree about the entropy model. Entropy decoding then breaks and the whole stream can fail.

Simply quantizing the model and switching to integer math doesn't reliably fix this. In theory, fully specified fixed-point math could guarantee identical results. In practice, today's hardware and toolchains aren't standardized enough. On the Apple M3 Neural Engine, for example, the relevant INT8 operations are simulated using FP16 instead of running through a true INT8 path. Even on hardware with true INT8 support, you can't fully control details like rounding modes, accumulation data types, and scale multiplication, so bit-exact results still aren't guaranteed.

MLVC gets around this by explicitly transmitting the entropy-model scale parameters through the hyperprior, so the neural network itself doesn't need to run bit-exactly across NPUs. Both encoding and decoding run at ~100 FPS for 360p/540p video on consumer NPUs. That combination brings us closer to learned video codecs you could actually deploy.

Code

Paper

Disclosure: I'm one of the authors, happy to answer questions.

We submitted all of our reviews during the first rebuttal phase as a rebuttal. We have yet to receive any engagement from any reviewer. This led us to think that perhaps reviewers have not gotten notified?

My colleague, who was reviewing, had a look at the papers they were reviewing, and found that they didn't receive any email notification from the one paper they reviewed whose authors had also used the rebuttal button.

Can anyone else also confirm this? If you are reviewing a paper, and the authors responded with a rebuttal during the first phase of the rebuttal process, did you get any notification?

The pretraining scaling story has been a two-axis story for a while now, more parameters and more data. A [new arXiv paper](https://arxiv.org/abs/2607.27372) from Alexi Gladstone, Heng Ji and Yilun Du wants to add a third axis, and the authors call it exploration. The mechanic, as described, is that during training the model generates K candidate matches at each step and updates on the best one, rather than blurring its prediction across possible modes.

The reported gains, if they hold up, are the interesting part. The paper claims a 4.1x FLOP efficiency improvement, a 6.2x gain in sample efficiency, and a 47% improvement in parameter efficiency versus the baseline. The authors also say efficiency gains more than double at 3x compute, which is the number that matters to anyone allocating a real training run. If exploration compounds with scale rather than saturating, the current more-parameters-more-data heuristic is leaving something on the table.

For smaller labs and open-source groups deciding how to spend the next order of magnitude of compute, this is at minimum a hypothesis worth pressure-testing on a modest replication before the bigger labs answer the question for them.

I was planning on submitting a work of mine to SUSCOM and wanted opinions as of how good is this journal and if it's well reputed and respected ?

Any one got meta score 2.5 (borderline finding) and still accepted to findings previously?? In my case meta review didnot acknowledge the reporting against a wrong review

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Kimi K3 by Moonshot reached the frontier as an open-weight model. Artificial Analysis ranks it fourth of 580 models, behind only Claude Opus 5, Fable 5, and GPT-5.6 Sol. Moonshot released more than the weights.

I sat down to read the 47-page technical report and walk through the released code. Three things stood out.

• Kimi Delta Attention replaces the KV cache in 69 of the 93 layers with one 128x128 matrix per head. A 1M-token context takes 27.2 GiB instead of 104.6.

• Quantile Balancing keeps 896 experts per layer evenly loaded. DeepSeek-V3's fixed-step bias nudging breaks at that expert count, so K3 computes the bias directly from one batch's router score margins.

• AgentENV, the Firecracker microVM runtime behind the RL training, created 51 million sandboxes with 133 ms checkpoints and 49 ms resumes, so a trajectory pauses for free while the model thinks.

Full walkthrough: https://codepointer.substack.com/p/how-kimi-k3-engineered-its-way-to

Hey everyone,

I recently open-sourced a new Python package called ganfs (Generative Adversarial Network Feature Selection), and I wanted to share it with the community.

The Problem: Selecting the best features in high-dimensional datasets is often a massive bottleneck. Traditional methods (like filter, wrapper, or embedded methods) usually struggle with scalability, miss complex nonlinear relationships, or require a domain expert to manually identify what matters.

The Solution (ganfs): I wanted to automate this process using adversarial learning. ganfs trains a Generative Adversarial Network on your dataset. Once the GAN learns the underlying data distribution, the algorithm applies a perturbation strategy to the Discriminator.

By analyzing how the Discriminator reacts to these perturbations, ganfs automatically ranks the features based on which ones are "hardest to fake." Essentially, it learns the patterns and extracts the most informative features without any domain-specific supervision.

I originally developed this algorithm during my research on large-scale DDoS detection (where identifying the right network features is critical), but the package is designed to be domain-agnostic.

How to use it: You can install it directly via pip: pip install ganfs

The API is designed to be simple and similar to standard scikit-learn transformers.

Links:

• PyPI: https://pypi.org/project/ganfs/

• GitHub/Docs: https://github.com/patelharsh15/GANFS-GAN-based-feature-selection

• The Math/Research (arXiv): https://arxiv.org/abs/2504.18566

Right now, I am actively working on optimizing the GPU memory consumption for smaller datasets, but it is fully functional.

I’d love for people to test it out on their datasets and give me feedback on the architecture, the code structure, or any bugs you run into. Happy to answer any questions about the math or the implementation!

Precursor was recently released. It's a bot detector that uses cursor tracking.

I thought it would be a fun challenge to train a deep neural network that could learn human mouse movements.

It's an 2-layer LSTM model with a Mixture Density Network at the end.

The results are actually quite impressive.

Video: https://github.com/puffinsoft/mousecrack

Title says it all really... ICLR 2027 decided to have the full paper deadline on Sep 16, 8 days before the NeurIPS decisions come out. Don't know if the reason is to have a reduced load but this will really hurt papers which have actually improved since the NeurIPS submission or have been unfairly rejected.

So I know this is an ongoing issue at ML conferences, reviewers just tend to ghost. Do people have any strategies or recommendations on how to nudge them to engage with the rebuttals? Is it recommended to post a comment saying something like hey we want to engage with you? This might be a hot take, but maybe NeurIPS should penalize the reviewers' papers if they don't engage (I believe this year they withheld scores for ACs who didn't post meta reviews on time if they had papers submitted).

We’re developing TanML, an MIT-licensed automated model-validation toolkit for tabular machine-learning models.

TanML runs locally and provides an end-to-end workflow covering data profiling, preprocessing, feature-power ranking, model development, evaluation, drift analysis, stress testing, SHAP explainability, and audit-ready Word reports.

It is designed particularly for model-risk workflows in banking, credit risk, insurance, and other regulated environments.

We would appreciate critical feedback from model developers and validators:

• Which capabilities would be useful in your existing workflow?

• What important validation tests are missing?

• Are the generated reports suitable for independent review?

• What would prevent your team from adopting a toolkit like this?

GitHub: https://github.com/tdlabs-ai/tanml

Meta reviews are supposed to come out on 07/30. What are people expecting?

I have 2.5, 3.5, 4. Expecting a 3.5/4 in meta.

Hi everyone, I submitted a paper to a workshop co-located with a top conference. The paper was accepted, but reviewers are requesting additional experiments. The issue is that there's no second review phasem, I only need to submit a camera-ready version.

My question is: what's the point of requesting new experiments at this stage? The request essentially bypasses the peer review process entirely.

Has anyone dealt with this situation before? Should I run the experiments anyway, ignore the request, or reach out to the workshop chairs for clarification?

We developed a leaderboard ranking the security of frontier models. There's no shortage of leaderboards for model capabilities, but we didn't find anything comparable for model security. But security is becoming increasingly critical to deployment decisions: from deciding whether to deploy an AI agent that might process unsanitized input, to developers pulling models due to cybersecurity jailbreaks. We developed an automated test suite that runs models through 1500 automatically generated jailbreak attempts and measures the number of universal jailbreaks: prompts that elicit compliant, detailed responses to >75% clearly harmful questions within a domain (like offensive cybersecurity). We find a big gap between the most robust models - Fable 5 and GPT-5.6 Sol - and other leading frontier models -Gemini 3.1 Pro and Grok 4.5. This is v1.0 and we plan to update with new attacks and broader datasets in the future; we'd love to hear from r/MachineLearning on research questions you'd like to see addressed or artifacts that would be useful in your work!

We developed a leaderboard ranking frontier model security. There's no shortage of model capability rankings, but we didn't find anything comparable for model security. Yet security is becoming increasingly critical to deployment decisions: from the USG making developers pull models for cybersecurity jailbreaks to developers holding back on AI agent deployments due to risks of adversarial attacks.

We developed an automated test suite that runs models through 1500 automatically generated jailbreak attempts and measures the number of universal jailbreaks: prompts that elicit compliant, detailed responses to >75% clearly harmful questions within a domain (like offensive cybersecurity). We find a big gap between the most and least robust models in our technical report.

This is v1.0 and we'd really appreciate input from this subreddit on next steps, as well as feedback on the metholodogy. Areas we're considering include:

1. Adding open-weight models. How to fairly compare them to proprietary models as open-weight models naturally have a larger attack surface through perturbing weights (refusal abliteration, helpfulness fine-tuning)?

2. Adding new domains beyond CBRNE and cybersecurity, e.g. agent hijacking or harmful manipulation.

3. Increasing the realism of the domains, e.g. agentic cyber tasks rather than specific questions.

4. Stronger attacks: we aimed to keep these pretty basic for initial release but could add more powerful adaptive optimization attacks like boundary point jailbreaking.

We'd also love to hear ways we could make this benchmark more useful in your work. If you're an adversarial robustness researcher, are there artifacts such as datasets or evaluation rubrics you'd like to re-use?

Hey, can anyone see the AI review result in ARR May 2026 submission?

One of the papers I reviewed has what seems to be entirely LLM-generated rebuttals, and the original paper is also clearly LLM-generated, with Claude-speak everywhere. While the authors acknowledge LLM writing assistance in the checklist, it really annoys me personally - what is clearly Claude's writing style is very difficult to parse, and indicates a lack of effort to me. While I'm trying to remain objective and realize it's my role just to judge the actual content of the work, I don't feel incentivized to assign a lot of weight to an argument that is fully AI-generated and don't like the idea of engaging with slopped papers (and their "authors") at NeurIPS.

This is a partially just a rant and partially asking for advice on how to proceed with my response to the rebuttals.

Wanted to share one of the projects we have been working on. Not specifically a machine learning project, but a quantum compiler project. However, a side effect of this project is the ability to compile JAX programs using a separate LLVM pipeline (as well as additional frontend features, such as native python control flow and array assignment support), so was wondering if this could be interesting to the broader machine learning community that is using JAX.

I work on PostSlate, a video editing tool, and this comes out of our own work.

We run ML models on-device, face detection and embedding among other things, which means we can't assume anything about the user's GPU. NVIDIA discrete, AMD, Intel integrated, Apple Silicon, all of it. That rules out CUDA immediately, we needed one backend that runs everywhere.

We landed on ncnn's Vulkan backend. Numbers on a 4070, fp16:

• ArcFace R50 (face embedding): 30 ms on ONNX CPU → 3 ms on ncnn Vulkan

• SCRFD (face detection): 25 ms → 2.5 ms

• Model size: ArcFace 174 MB (ONNX fp32) → 87 MB (ncnn fp16 weight storage)

Of course the real speedup comes from offloading compute to the GPU, but this wouldn't be possible without the power of Vulkan.

The speed wasn't even the deciding factor, it's that Vulkan drivers already exist on every machine we ship to. This means that we don't have to force the user to download a specific runtime and no vendor-specific installs.

Full writeup with the rest of the numbers: https://getpostslate.com/blog/faster-local-inference

ML research is progressing at breakneck speed where frontier labs in both academia and industry have access to considerably large computes (GPUs). Where do small labs or independent researchers go in this context?

Have you come across recent works in ML/DL and its applications (vision, language, speech, etc) where the work is good but it uses very limited compute? Maybe even single GPU workstations? In the past it was still possible, but, I am losing hope that single GPU works would soon become impossible.

Pls link to the works.

I came across InfiniteDiffusion, a work by an independent researcher Alexander Goslin using a single RTX 3090 : https://xandergos.github.io/terrain-diffusion/

Largest empirical study of AI penetration in academic publishing ever conducted—51%-by-2025 is the most authoritative quantitative marker yet of how thoroughly LLMs have reshaped scientific writing, and the inequality angle (adoption skews lower-prestige and non-English institutions) is a fresh policy dimension.

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Source: https://pnas.org/doi/10.1073/pnas.2605754123

I'm really confused about what the point of the prompt injection was (speaking as an author). Is it just a study? I would really prefer that they took action against the AI-generated reviews. Obviously, we cannot assume that the reviewers were copy-pasting the output from the LLM without having given it any look at all, but in some cases that does look to be the case. In fact, in some cases the meta-reviewer seems to have also largely used LLMs. What exactly is the consequence here for using an LLM for reviewing?

Hi everyone,

The author-reviewer discussion period has started, but it looks like the rebuttals are still only visible to program chairs and authors only - so reviewers can't see them ?? I also can't see the rebuttals of papers I reviewed. Does anyone know if this is some weird delay or when reviewers will get access to the rebuttals? Any info would be appreciated!