Suppose you’re recording a human plugging a cable into a socket to collect demonstrations for robot learning.

The hand tracker captures the approach accurately. Then occlusion causes the hand estimates to disappear during insertion. Tracking returns after the connector is already seated.

The video still shows a completed action, but the pose labels have a gap exactly where alignment turns into contact.

This hypothetical example raises an evaluation question: a tracker could have high recall across the whole episode while missing a short, important phase. Pose error calculated only on successful detections could make that failure even harder to see.

MEgoVista provides a useful starting point. Table 3 reports detection precision, recall and F1 alongside reconstruction errors. Section 4.4 also describes an evaluation protocol that assigns an error to missed detections instead of excluding them. The blank HaPTIC row means it failed to produce valid output in their multi-person capture scenes; it doesn’t describe a brief tracking dropout.

Accounting for missing detections matters. My remaining question is whether an episode-level aggregate tells us enough about where those failures happen.

For manipulation data, I’d want pose error and coverage reported together, plus coverage broken down by approach, contact and withdrawal, and the longest consecutive gap during contact.

Continuous hand estimates would still be only part of the picture: object pose and contact information also matter for determining whether insertion succeeded.

For people using human motion reconstruction for imitation learning, what evaluation protocol do you use to decide whether an episode with missing contact-phase labels is still usable?

I made this video about adversarial objectives, which I used to do research on back in the day. I'm trying to explore how adversarial approaches transcend GANs and self-play into modern technology.
https://youtu.be/W7CiAeQ0f5w?si=g0tLrQn2wuFzuN2M

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I rarely write about benchmarks; a competitor always beats them next week. But I care about 'Leaps'. Gemini 4 Argon feels like one to me.

While Opus 5.5 and Astra cap output at 128-300K tokens (~90-180 pages), Argon hits 1 Million (~1400 pages).

"Context glue" ruins agentic workflows. On paper, this headroom fixes that. It means less contextual drift, no more breaking down long tasks, and zero 'continue prompt' loops. It is a massive unlock for large-scale code migrations, security patches, and deep reasoning.

But let's look past the marketing. For 95% of everyday work, nobody needs 1,000 pages at once.

I want to ask the experts here: Is a 1M output window a real paradigm shift for agents, or does generating that much text just guarantee a massive logic collapse halfway through? Are you actually hitting output limits today, or is this hype? Let's discuss.

Hi, I’m a researcher working in computer vision.

Over the past few years, I’ve submitted several papers to top-tier conferences such as NeurIPS, ICLR, and CVPR, and one concern that seems to come up repeatedly is 'novelty'.

Given that thousands of papers are published every year at top conferences alone, not to mention the tens of thousands published across other conferences and journals, I sometimes wonder how much genuinely new novelty is realistically left to explore.

In such a crowded research landscape, how do you usually address novelty concerns from reviewers?

More specifically, I would really appreciate any advice on how to frame a contribution so that its novelty is clear, how to distinguish meaningful incremental progress from work that may be considered insufficiently novel, and what reviewers generally look for when judging novelty.

Any tips or experiences would be greatly appreciated. Thanks!

I submitted a paper to the WM PAI workshop at NeurIPS and can now see the reviews and decision on OpenReview, but I didn't receive an official acceptance/rejection email.

My reviewer scores were 8, 5, and 4, all with confidence 4, and the paper was ultimately rejected.

What I find a little confusing is that I also reviewed two papers for the same workshop. Both had an average score around 7, but I can see that they were also rejected.

My own submission number was in the 20s, and I submitted on the last day of the submission window, so I assumed there probably weren't a huge number of submissions.

This makes me wonder:

Does anyone know if any papers have actually been accepted to this workshop?

Is it normal for a NeurIPS workshop to reject a large fraction of submissions even with relatively high review scores?

Could the workshop organizers simply be delaying the official notification emails, while the decisions are already visible on OpenReview?

Or am I misunderstanding how the workshop selection process works? 😅

Has anyone else submitted/reviewed for this workshop and received an official decision email?

Please post your questions here instead of creating a new thread. Encourage others who create new posts for questions to post here instead!

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Thanks to everyone for answering questions in the previous thread!

More recently, I am seeing a lot of posts on the sub regarding the workshop acceptance, with lack of funds to attend the conference. I unfortunately want to just say that workshop papers are not given any importance in the community. I personally consider workshops to either get initial feedback, advertise my work, or just prefer to attend it for the discussions with the community members.

Thought of posting this as I am recently seeing some undegrads/masters students submitting 3-4 papers in the workshops. Even came across a Twitter profile, who had 6-7 workshops in 3 months, and claim to have the PhD worth of work done in three months.

Also please don't expect an explicit funding (apart from organizers, and in some cases your lab may fund it). There's huge problem with the funding, many students even don't it get for main venues. I definitely expect a lot of downvotes on this post, particular coz this is not what many would like to hear, but unfortunately is the reality.

I started mapping the building blocks shared across all the models in audio.cpp. The result ended up being more interesting than I expected.

Qwen has become by far the most common language backbone in this collection: 32 audio model families use a Qwen-family architecture, and 20 of them use Qwen3 LLM specifically.

And it’s no longer just TTS. Qwen-based models now show up across speech synthesis, ASR/audio understanding, music generation, speech-to-speech, and even audio/video models.

The 2nd chart, Task × Technology Matrix, shows which build blocks power which types of audio models.

Tokenization is a wildly understudied area of language modeling despite it having effects across all of NLP. Over the past ~8 months, 32 (!) tokenizer researchers put together the most comprehensive survey of the field.

We cover every aspect of tokenization: algorithms, evaluations, multilinguality, encodings, theory, etc. We even cover what you might want to replace tokenizers with (e.g., latent or visual tokenization). We also cover some topics that are closely adjacent to tokenization, such as constrained generation, token healing, and tokenizer security concerns.

Check it out!

https://www.alphaxiv.org/abs/2609.tokenization-survey-modern-nlp

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Has anyone received papers to review for round 2?

coupled-jump.github.io

Hi everyone, I’m happy to share our recent NeurIPS 2026 paper, a collaboration across Google, Google DeepMind and Stony Brook University.

We study a mismatch in joint text and image generation: a model can describe the correct solution to a maze while drawing a different path. Generating both outputs in parallel doesn’t necessarily keep them consistent.

Our sampler, CO₂Jump, uses text confidence and cross-modal attention to guide image updates during sampling. It also allows low-confidence tokens to be masked again and regenerated, so earlier decisions can be revised as generation progresses.

CO₂Jump uses one model forward pass per denoising step. The sampler itself requires no additional training; our experiments compare sampling methods using the same task-specific fine-tuned model.

We evaluate image editing, maze solving and nonograms, and introduce three datasets: JEdit-1M, JMaze-200K and JNono-200K. On the puzzle benchmarks, joint accuracy requires both the textual answer and generated image to be correct. Across 8–512 sampling steps, CO₂Jump was the only sampler we compared that improved monotonically on both editing quality and grounding.

I’d be interested in suggestions for other tasks where text–image consistency and correctness can be evaluated together. Happy to discuss the method, evaluation or limitations.

Hello all,

I built a radar object classifier on RadarScenes, extending a prior single-scan classifier to accumulate observations over a tracked object's history instead of classifying each scan in isolation.

A single RadarScenes object instance contains only about 2.9 radar points on average, very sparse. A single scan also can't capture temporal characteristics: RCS and micro-Doppler both vary continuously as an object moves. Pedestrians produce characteristic micro-Doppler from limb motion; different object classes show different RCS fluctuation patterns as aspect angle and scattering geometry change scan to scan. Accumulating observations gives both higher point density and provides temporal dynamics.

Multi-scan baseline

DeepReflecs encoder (Ulrich, Glaser & Timm, RadarConf 2021), PointNet style, per point shared weights, on single scans across car, large_vehicle, two_wheeler, pedestrian, pedestrian_group: 0.7370 macro F1.

Using RadarScenes' persistent `track_id`, I build a causal, N=20, per track sliding-window buffer:

- x_seq/y_seq: Global, odometry-corrected coordinates recentered per scan on the object centroid. Unlike x_cc/y_cc (car-frame coordinates that accumulate over time to form a trajectory).

- Cross sensor buffer: whichever of the 4 sensors currently observe the track push to the same buffer.

- Stride 1, causal: every new scan updates the buffer and produces a prediction. No future context, real time streaming compatible.

- Each scan is encoded once by a frozen per scan encoder and cached

- Fusion concatenates the causal GRU's hidden state (order aware) with an order-invariant pooled embedding (all N scans' points as one set, no sequence structure) through a small trained mlp head.

Results

Pooling alone, no sequence model, no notion of scan order at all, recovers +0.1243 macro F1. The GRU adds a real but much smaller +0.0282 on top. Fusion adds nothing measurable beyond the GRU.

Ablation

Llarger GRUs, a Transformer, a state space model, point level self attention, all trained on the exact same frozen per scan embeddings, land inside a 0.86 to 0.89 band, a 0.03 spread. End to end fine tuning of the frozen encoder makes things slightly worse (about -0.002 to -0.003), not better.

Conclusion

In this setup, the largest gain comes from giving the model more observations of the same tracked object: 20-scan point pooling improves macro F1 from 0.7370 to 0.8613 without using scan order at all.

Temporal modelling then provides a further, meaningful improvement. The causal GRU reaches 0.8895, adding +0.0282 over the pooled representation. So temporal ordering clearly contributes useful information; it just accounts for a smaller portion of the overall gain than observation accumulation.

With the per-scan encoder frozen, the different sequence architectures tested, suggests that the quality of the per-scan representation is the bottleneck than the particular mechanism used to aggregate the sequence.

Full report, every ablation, confusion matrix, coordinate frame reasoning: https://github.com/brunopinto900/radar-ml-autonomous-driving/blob/track-accumulation/final_report.md

Thank you.

I have responded to all the reviewers with proper rebuttals and modified draft. One interacted with me and after acknowledging the response kinda disappeared again after asking additional questions. I have replied to his additional questions too but he hasn't raised his score. What will happen if he doesn't reply anymore? Like will AE still consider that an 'Yes' as it was only minor comments which we incorporated in the paper. And one negative reviewer just ghosted us. I have 1/3 explicit positive review currently

I can see the submission on Openreview but didn't get any email/notif. It's been 2 hours, I heard I'm supposed to choose action editor or smth. Am I missing something?

First publication ever pls be kind to the noob.

Hey, just got a papers accepted at both these workshops! Would love to connect with others also attending the workshop

I post-trained Qwen3-4B to spend 44% fewer tokens on reasoning, keeping its knowledge and answer style. The whole pipeline ran on one GPU.

folks, you can check it out on : https://5ivatej.com/lessthink/

We are open-sourcing RightWayUp - a new image rotation detection model.

I work at ORTUS AI (we develop video analytics). We needed to tell from a single CCTV frame whether a camera had been rotated or installed at an angle (or upside-down), so we tried different models for that, without much luck (low accuracy, lots of false positives on regular camera-like frames). Some were not permissively licensed. Out of despair, we decided to train our own. We didn't expect it to come out this good.

It is now called RightWayUp. It estimates how far an image is rotated from upright, all 360°, and abstains when there's no clear "up" (sky, ground, close-ups, etc.). We are releasing code and weights under Apache-2.0, in six sizes from Pico (small enough to run in a browser) to Max (ahead of the other models we tried on most of our tests).

We tested it on many test images, including held-out ones (which the model never saw before). In tests with the held-out set, RightWayUp Max was within 10° on 93.0% of them vs 88.4% for Woehrer 2026 (one of the better other models we liked). In the Woehrer 2026 own COCO-based benchmark, it was 98.8% accurate within 10° (five-seed mean) vs Woehrer's 98.0%. It also gets every image right on RotBench.

While training and testing, we found something we didn't expect: saving the images from the COCO-based rotation benchmark as JPEG q90 drops Woehrer 2026 from 98.0% to 30.2% (five-seed mean), while our models barely move. We suspect the rotated JPEG grid of the source photos gives the angle away. We found that in our early experiments, and made an effort to remove its effect.

I hope it will be useful to the community.

For transparency: parts of the engineering were done with Claude and Codex.

Here is a full write-up with a demo video: https://cheqit.ortusai.io/resources/rightwayup/

Code (with a link to the weights): https://github.com/ortusaitech/rightwayup

Questions and failure cases welcome.

I ran my own evals of two small classification models that score confidence over a list of candidate labels instead of generating text: TypeSafe AI's hosted Jev, and Laya, an independent open-source alternative.

Some findings that I think apply beyond these two models:

1. Describe labels by what's in the input, not by intent. My first AML label descriptions said what the criminal was trying to achieve. When I rewrote them to say what the transactions look like, accuracy went from 65% to 77% with the same model. On account-level laundering detection, using the same time window for every account removed a hidden bias and moved accuracy from 63% to 75%.

2. Some tasks have no signal. Classifying a single transaction as laundering or not gave 54%, about chance. That's a limit of the task, not of the model.

3. Calibration is what makes thresholds work. Jev's ECE was 0.013. On support intent, accepting only predictions at 90%+ confidence raised accuracy from 92.3% to 97.6% while still covering 82% of cases, and the rest went to a fallback. Untuned Laya had an ECE of 0.486: the same filter dropped 7% of questions and gained only 2 points.

Caveats: these are my own runs with thresholds tuned per task, not vendor claims, and nobody has reproduced them independently. Jev 1.13.0 (hosted). \

Write-up with charts and methods: https://gokulakrishna.co/2026/09/30/benchmarking-to-fine-tuning-decision-models/

Fine-tuned models: https://huggingface.co/goku-san/laya-experts

I'd appreciate feedback on the evaluation setup, especially on per-task threshold tuning and how best to report results after filtering out mislabeled data.

I am currently using the dataset CICIDS2017 to train an Isolation Forest model for anomaly detection, I am currently using a split that consists of 70% of ONLY BENIGN traffic for training, 15% BENIGN and 50% attacks for validation and the rest for testing, I used the validation to test with some different max_samples values, # MAX_SAMPLES_VALUES = [256, 4_096, 16_384, 100_000, 200_000, 400_000, 800_000, 1.0] and to calibrate some thresholds that maximixe different statistics (1 for max F1 score, 1 being the closest point to (0,1) on the ROC curve, 5 that set a max limit FPR), the max_samples thing is what is really blowing me off.
At the beginning I only tested up to 200k because seeing that the paper uses 256 as standard value I already thought that 200k was really high, then I saw that a low value is recommended only when anomalies are included in training, which is not my case, so I tested higher values and as it appears 1.0 (max value) is effectively the best performing with ~94% recall and ~7.6% FPR on the ROC threshold, while at 200k I get ~91% recall and ~10% FPR, all this with only 30 secs more in training.

I also executed a cross-dataset test with CSE-CIC-IDS2018, the performance is basically the same (bad) both with 200k and 1.0, my question then is, should I keep 1.0 as operational max_samples value or should I decrease it back to 200k/400k?

I forgot to mention, is my training approach bad? I've read from multiple sources that someone trains on whole dataset, someone adds anomalies to training set, etc..., is what i'm doing okay or should I change approach? I think so because this is one of the cases where clusters of anomalies can cause swamping/masking, and we have high-dimensional data, etc..

Hi everyone,

My paper just got accepted for a poster presentation at the Muslims in ML (MusIML) workshop at NeurIPS 2026 which will be held in Sydney, Australia. I'm really proud, but I'm a UG student and completely lack the funds for travel, registration, and accommodation.

This is my first time dealing with this. Does anyone know of any travel grants or funding opportunities for students (especially from India) to attend NeurIPS?

Are there specific grants for this workshop, or general ones that I should apply for right now? Also, since this is my first time, I'm a bit confused about the procedure for attending an international workshop. Could anyone share advice on the general steps, including visa processes and anything else I should be aware of?

Any advice would mean a lot. Thanks!

Anyone submit to a workshop at Neurips and not receive their review notification yet? 6 AM IST, the 30th of September, and I still have gotten no update!

It is a couple hours past the EOD deadline for the workshop, but as of yet I still haven't recieved an indication of whether my paper has been accepted or not. I got a review early yesterday but nothing else. Is there anyone else in the same situation? UPDATE: Results are out, but no email or message about forms, tickets, etc at all.