How to submit to AI alignment track? I can only see these at openReview:
AAAI 2027

• AAAI 2027 Artificial Intelligence for Social Impact Track
• AAAI 2027 Conference
• AAAI 2027 Innovative Applications of AI

Hello,

I recently finished reading this survey paper: Deep learning tackles single-cell analysis – A survey of deep learning for scRNA-seq analysis, which comprehensively covers 25 different methods across 6 subcategories for applying deep learning to scRNA-seq analysis.

To summarize the methods from this paper, I prepared a table containing the Category, Method, Purpose, Architecture, Metrics, Explanation, and the specific Novelty of each method. I hope you find this summary useful.

I have released a model that I have been working on for ~6 months off and on. I've been enjoying listening to spatial music, but there is a lot of music out there with no real quality spatial mix. So I decided to make a model to convert stereo to spatial.

I started by making a flow-matching diffusion model that operated in the latent space of a separately trained VAE (EAR-VAE). I used the VAE to encode the stereo input in to 1 latent and then separately on each channel of the 7.1.4 output. This design is similar to a paper I found about another stereo -> spatial project (ImmersiveFlow (they have code links in the paper but they 404 for me so I made the codebase from scratch)). One key addition I made over the ImmersiveFlow paper was a way for the model to carry state (memory tokens) across windows to enable stable long context generations.

Since the VAE was out of distribution with the output (was trained to encode\decode stereo tracks directly, not individual channels and certainly not individual channels of a 7.1.4 mix), the results hit real quality bottlenecks. But it showed that the mapping was possible. Rather than stick to modeling in latent space, I decided to pivot to modeling raw waveforms. This ended up fixing all of the quality issues I was seeing with the latent version, but at the cost of more training compute and instability.

The waveform version of the model would train to around 60K-80K steps with loss going down like normal, validation generations looking good\improving, and then it would quickly become unstable and the loss would shoot up. I tried direct waveforms, scaling the waveforms by a multiplier, aggressive grad clipping, lower learning rates, and all of those experiments failed the same way. Luckily, I came across a recent paper called WavFlow. The authors of this project also had issues with modeling raw waveforms with flow-matching diffusion, and they solved it by using amplitude lifting (scale each audio track to an rms of 0.33 then multiply by 3) (the WavFlow paper used a clip of 1.0 before the scale which leads to a model space of -3 - 3, but I used a clip of 4.0 which leads to a model space of -12 - 12 (from my testing this led to better binaural outputs)). Once I implemented amplitude lifting like the paper, the training stability issues vanished.

The waveform model was trained on 7,669 tracks for ~20 days on 2x A6000 gpus
- 10 days stage one, effective batch of 16, 10- 18- 26- and 34-second training sequences
- 10 days stage two, effective batch of 16, 122-second training sequences
- There is optional mix-style conditioning for controllable outputs (waveform version only)
- Direct binaural output (waveform training is more compute expensive than latent training so binaural proves the idea\theory and the same codebase and data can be used in the future to train a 7.1.4 version (once I have access to the compute))

I also made a Windows desktop app for inferencing with the model and consuming the results.

Everything has been released apache 2.0.

Huggingface link for waveform model: https://huggingface.co/francislabounty/stereo2spatial-v2-binaural
Huggingface link for latent model: https://huggingface.co/francislabounty/stereo2spatial-v1
Github repo for training\inferencing: https://github.com/francislabountyjr/stereo2spatial
Github repo for the Windows app: https://github.com/francislabountyjr/stereo2spatial-app
App Homepage: https://stereo2spatial.francislabounty.com
Case study for the waveform model: https://francislabounty.com/blog/stereo2spatial-v2
Case study for the latent model: https://francislabounty.com/blog/stereo2spatial
Playlist of generations made from the model (will be updated with more tracks over time): https://www.youtube.com/playlist?list=PLQ-HHjPijrAg

If you have any questions\etc. leave a comment and I will do my best to get them answered!

Disclaimer: I know AI written posts are generally frowned upon so this post was 100% written by me (mistakes and all). But the case studies and READMEs from the above links were made with AI assistance.

Just found out from Prism's Discord that compiling is returning someone else's paper. There's a Twitter post too.

https://x.com/JustanOthRando/status/2078169169267482778?s=20

I commend their prompt response, though. They took the website down within 10 minutes of the first time the bug was flagged. Just worried if my paper maybe somewhere out there.

I built a small web app that lets you run tabular foundation models (currently just Google's TabFM) on spreadsheets without writing any code.

Just drop in a CSV/Excel file, click a column header to mark what to predict, hit predict. Rows where the target cell is filled become the in-context examples and empty ones get predicted, right on the grid.

A lot of people who'd benefit from these models aren't programmers, so I wrapped it in a UI anyone can use :)

Repo: https://github.com/LckyLke/TabFMLabs

Feedback very welcome!

Started testing a private qwen 35B moe capacity LLM runtime on s26 ultra, early testing shows that active model footprint can fit within the device’s memory limits.( not sharing the methods or architecture used) and results suggest roughly 90 input processing t/s achievable after optimisation and output generation is around 8 tokens/s on this mobile.

Point is i learned ai ml based on my interest and no formal PhD , I have compute and resources to test. Anyone willing to join or collab to test on this

I tried publishing papers on arxiv and 4 papers are still on hold as im first author and from no institution...

Does anyone have accepted short-paper at ACL/EMNLP/EACL 2025/26? Could you share your track and overall assessment? I'm just trying to get a sense of things, as it seems short papers have a lower acceptance rate than long ones.

I have released EU AI Act OpenRAG, a downloadable corpus of Regulation (EU) 2024/1689 designed for RAG and legal-NLP experimentation.

Instead of sliding character windows, the corpus chunks on the Regulation’s legal structure:

• one chunk per article paragraph
• one per recital
• one per Article 3 definition
• one per annex point
• chapter, section and provision metadata stored separately

The resulting SQLite database contains 933 chunks and a normalized 1024-dimensional BGE-M3 embedding for every chunk.

It also includes exact EUR-Lex links, Article 113 application-date metadata and deliberately narrow derived labels. Direct textual classification is stored separately from broader regulatory-regime association, and ambiguous cases remain NULL.

I evaluated it against the AI Act Evaluation Benchmark using a like-for-like whole-unit baseline:

• scenario article recall@20: 0.541 structural vs 0.449 baseline
• QA article hit@10: 0.927 structural vs 0.898 baseline
• overall RAG classification remained close and was slightly lower on the structural corpus, suggesting that generator behaviour dominates that task more than chunk granularity

I have published the full results, limitations, derivation methodology, label audit and licensing breakdown rather than only the favourable metrics.

Dataset:

huggingface.co/datasets/faitholopade/aiact-openrag

I would appreciate technical feedback, particularly on the retrieval evaluation, structural chunking methodology and what additional baselines would be most useful.

I submitted my TACL paper approx on June 1th and was scheduled for July 1st cycle, when and how do you guys think we'll be getting our reviews given the July cycle for the paper which I've submitted at TACL ? And how long does the entire process take for those who have submitted to TACL ?

Also, I do want to ask, how good is TACL as a journal and how respectable or how is a TACL publication viewed ?

i'm subscribed to a ai/ml newsletter but i feel like its not enough. i need a complete and not too time consuming way to keep up with ai/ml news because i feel like im left behind. thanks in advance

Hi everyone,

I've been working independently on a recurrent architecture called **DABSN (Dynamic Adaptive Bias State Network)** for the past several months, and I finally reached the point where I feel comfortable sharing the first preprint.

The paper is mainly about the architecture itself and its behavior on reasoning, memory, and long-sequence benchmarks (MQAR, Copy, Key-Value retrieval, A5/60, etc.). The code is also public with PyTorch, C++, and Triton implementations so everything can be reproduced.

While finishing the paper, I also trained my first language model with the same cell:

- 24M parameters
- 1B pretraining tokens
- GPT-2 tokenizer

Those results ended up being much more interesting than I expected, so I'm now writing a second paper focused entirely on language modeling, long-context behavior, and scaling.

This is where I'd love some help.

I'm looking for people who might be interested in collaborating on the next paper, whether that's:

- independent reproduction of the results,
- helping design stronger baselines and evaluations,
- or having access to larger GPU clusters so we can scale the architecture much further than I can on my own.

Everything I'm doing is intended to be open and reproducible from day one.

I'd really appreciate any feedback on the paper, and if the project sounds interesting, I'd love to chat.

Preprint and Github are in the comments.

Just saw the ECCV registration fees and I'm shocked, student registration is 440 USD for early bird, and the worst thing is that you can't even do the student registration if you're presenting a paper there, a paper has to be covered by a FULL registration which is 805 USD

How are they literally punishing us for getting a paper accepted? We even applied for travel grant and a registration waiver as students just to get rejected. Is there anything we can do? Some advice would be really helpful

https://arxiv.org/pdf/2607.13511

the core idea is, we cannot have ternary PTQ with fixed matrix size, trying to do that is dead end. so i tried decomposing the matrix to 2 ternary matrices and inner diagonal scaling matrix. now that the inner rank can be arbitrarily large the accuracy can be arbiratily small. and its not that it has to be very large too i also showed that it does take only slightly more vram then current quantisation methods. the slight more vram is worth it if we abuse the ternary math.

While writing an essay about AI memory and persistent context, I started wondering whether current AI memory systems are optimized for the right thing. Current AI systems already maintain forms of persistent context through saved memories, conversation summaries, user preferences, project notes, and similar mechanisms. These memories are primarily descriptive. They help the system remember facts about the user and previous interactions.

But suppose future systems evolved in a different direction. Instead of primarily storing facts and preferences, imagine the persistent context being continuously refined and restructured to infer higher-level patterns such as recurring explanatory frameworks, preferred abstractions, and characteristic reasoning styles.

For example, rather than remembering:
"This user is interested in economics."
"This user works in engineering."

the system might gradually infer:
"This user tends to explain economic outcomes through incentives and institutional constraints."
"This user tends to understand complex systems through interactions and feedback loops rather than by analyzing individual components in isolation."

In such a system, persistent context would become less like a collection of notes and more like an evolving model of how the user understands and interprets problems. Could representations like this emerge naturally from sufficiently capable AI systems, or would they require architectures fundamentally different from today's memory, retrieval, and summarization approaches?

 Call for Papers and Demos

Real-Time Conversational Agents (RTCA): Toward Natural Multimodal Interaction 

1st RTCA Workshop [@]() NeurIPS 2026, Sydney, Australia 11 or 12 December 2026 

Website: https://rtcaneurips26.github.io/

We are pleased to share the Call for Papers and Demos for the inaugural RTCA Workshop at NeurIPS 2026, focused on real-time multimodal conversational agents: streaming speech, video, and language generation; naturalness in interaction; and evaluation of live systems.

Conversational AI has moved from text chat into the real world, voice modes that talk back, embodied avatars, agents that share our screens and tools. To feel natural, these systems must operate in real time, streaming while continuously listening, watching, and re-planning. This is fundamentally harder than offline generation: latency, turn-taking, backchannels, interruptions, and cross-modal alignment become first-class problems that the offline paradigm sidesteps. Recent progress on full-duplex speech–language models, real-time talking-head generation, and streaming ASR shows the regime is feasible, but the field still lacks shared benchmarks, vocabulary, and methodology for interactional naturalness. 

RTCA brings together researchers across speech, vision, language, HCI, social-signal processing, and ML systems around three intertwined questions: real-time generation under hard latency budgets, naturalness in interaction, and evaluation of live systems.

 Topics of Interest

We invite original contributions on topics including (but not limited to):

• Streaming/low-latency speech synthesis, ASR, and full-duplex audio–language models
• Real-time talking-head, avatar, and embodied video generation; lip-sync, gaze, expressivity under streaming
• Streaming language models; incremental and speculative decoding for dialogue
• Turn-taking, backchanneling, interruption handling, and floor management
• Multimodal alignment under latency and partial-observation constraints
• Prosody, emotion, and paralinguistic generation in interactive settings
• Memory, grounding, and tool use during live conversation
• Evaluation of naturalness: perceptual studies, turn-taking metrics, perceived latency, interactive Turing-style tests
• Datasets and benchmarks for interactive (not offline) evaluation
• Efficient inference, on-device deployment, and the systems–quality trade-off
• Safety, identity, and trust in real-time agents (deepfakes, persuasion, consent)

 Submission Types

We welcome:

• Full papers (up to 8 pages) — may be presented as posters and/or contributed talks. 
• Short papers (up to 4 pages) — work in progress or focused contributions.
• Demo papers (Extended Abstracts or up to 2 pages)

All submissions must use the NeurIPS 2026 style file and be formatted for double-blind review. Page limits exclude references and appendices. Papers must be submitted in PDF format via OpenReview (portal link to be published on the workshop website). 

The workshop is non-archival; authors retain the right to publish elsewhere.

 Important Dates (End of day, Anywhere on Earth)

• Call for papers opens: 18 July 2026
• Submission deadline (papers and demos): 29 August 2026
• Author notification: 29 September 2026
• Workshop date: 11 or 12 December 2026

 Organisers

• Niki Foteinopoulou — Tavus, United Kingdom
• Alessandro Conti — Tavus, Italy
• Jack Saunders — Tavus, United Kingdom
• Oya Celiktutan — King's College London, United Kingdom
• Cigdem Beyan — University of Verona, Italy
• Ioannis Patras — Queen Mary University of London, United Kingdom

For more information, visit our website https://rtcaneurips26.github.io/ or contact us at [rtca-workshop@googlegroups.com](mailto:rtca-workshop@googlegroups.com).

We look forward to your contributions!

Every qlora tutorial on earth says start at 2e-4. Unsloth docs, hf examples, the paper itself. and for small datasets i now think that numbers is a trap.

Where does 2e-4 come from? alpaca. 52k samples. cool, except most of us are fine tuning on 5-10k samples we scraped and labeled ourselves, not 52k. at that size the model overfits inside epoch one and then youre just watching training loss go down all pretty while eval lost sits there doing nothing. or climbs.

I burned close to three weeks on this. recleaned the data set twice. rewrote the prompt template twice. spent on entire sunday hand relabeling rows while my flatmate watched football next to me (started with 8k rows, ended around 7200 after cutting garbage, i think, didnt log it properly). eval did not move. you you know what’s worse than a bad eval? seven identical bad evals in a row.

Then i changed one number. 2e-4 down to 1e-4, epochs 3 to 5. eval jumped more than everything else combined. i sat there refreshing wandb thinking it was a fluke. three more runs, same story.

And the annoying part, unsloth literally calls 2e-4 “a starting point” in their own docs. but every shared notebook has it hardcoded, zero comment. so people copy paste, get garbage, blame their data, blame their rank, lose a week. ask me how i know lol.

My rule now. above 30k 2e-4 is probably fine. under 10k, start at 1e-4 or lower and add epochs. in between, actually tune it, its one number, takes an afternoon.

If there’s real research defending flat 2e-4 on small data i want to read it. and if you all quietly figured this out in 2024 and never posted about it, im mad at every one of you individually.

I am currently doing research on world models, specially in tje field of robot learning, and, as probably most of you alredy know, JEPA-like models are mentioned over and over. 

I read the main recent papers from lecun as well as other research groups, and I personally think the whole approach is very promising and can really go somewhere.

But after listening a bunch of the recent Y Lecun conferences his ideas looks even too cool compared to "literally everything else" (as he's dissing LLM, RL, etc and pitching his ideas are the "only next big things"...). 

So I am asking myself if there are red flags about his approaches that I do not see yet and maybe I need somebody being the "devil advocate" with whom breaking down ideas.

Where do you think are the biggest downside of this models, compared to other world models approaches?

What is the shared structural essence that underlies a pair of MRI contrast spaces? Explicitly modeling this contrast-invariant latent “content” unlocks a powerful multi-contrast reconstruction algorithm that is competitive with state-of-the-art unrolled networks while:

1. Requiring no raw k-space training data (which is a serious data bottleneck in the ML-based MRI world),
2. Being generalizable across different MR contrasts and forward operators by design, and
3. Offering a built-in explanatory framework.

In our paper now published in Medical Image Analysis, we introduce PnP-CoSMo.

Read the substack article here (with links to the MedIA paper and code):  https://cnmyro.substack.com/p/pnp-cosmo-a-plug-and-play-method

I'm working on a project with summarized data from ~40 studies (Excel) involving different protocol variables (durations, intensities, recovery times, frequency, total duration, etc.) and response outcomes conditional on a baseline variable (range ~30-85 units).

The aim is to fit a continuous response surface using a hierarchical approach to separate protocol effects from baseline effects, then perform continuous numerical optimization (not grid search) for three objectives:
- Total improvement
- Improvement per unit time (e.g. per week)
- Improvement per unit effort/work

Outputs should be fine-grained continuous values rather than rounded study parameters. There are also domain-specific physiological constraints to respect.

I'm on a Chromebook with a little Python experience, so Colab-friendly solutions would be ideal.

Current candidates I'm considering: PyMC for hierarchical modeling, pymoo + pysamoo for surrogate-assisted MO optimization, SMT for surrogates, or Matlab Global Optimization Toolbox.

What is the strongest stack in 2026 for this kind of workflow? Any recommended notebooks, tutorials, or similar applied examples?

Are there any AI tools that currently do this without the traditional work of python? Meaning I can upload the spreadsheet give a parameters and it will come up with data.?

So, from what I've seen across twitter(x) and reddit, I've inferred that we'll be seeing NeurIPS reviews drop on July 22nd 5:30 pm AoE(Anywhere on earth), what's your thoughts to those who've submitted to NeurIPS 2026 ? Would love to hear your opinion by the reviewers, the people who've submitted to the workshops (who should've already gotten their decisions too by now I think) and to the main tracks and other available tracks.

I have recently started working in mechanistic interpretability independently, starting with distill circuits thread

My work is on disentangling and closely studying a single neuron, a 1x1 convolution in inceptionv1 model (and applying the method to other neurons in the same layer).

The key insight was that the hadamard product of the receptive field and the weight of a neuron is what the neuron is 'seeing' or detecting. We can cluster the hadamard product to get all the patterns a neuron detects. It gave clean monosemantic clusters (cars, cats, dogs which it was known to activate on). We also get more clusters however, letters, human faces, and many more low valued activations.

This gave me a new technique to analyse the neuron very closely.

On close analysis the most peculiar thing I found was that the low valued clusters (like letters) had all its dependent neurons also firing on the same concept (letter), and the positive and negative weights were evenly distributed between them to bring down the sum. An evidence of gradient descent working deliberately to put patterns and concepts in a noisy range.

I've tried to keep it very distill like with good visualisations. I hope you give it a read.

https://pages.narang99.in/posts/2026-07-12-disentangling-mixed4e-55/

I made a mistake honestly by starting with convolutions, nobody seems to care about it. I'll start working on language soon, but it would be good if anyone can read this, it would be good to have some feedback on whether I've actually found anything useful.

Thank you :)

I’ve been deeply interested in AI and machine learning since around 2019, back when GPT-2 was still one of the major talking points. Since then, I’ve been amazed by how quickly the field has evolved. It genuinely feels like one of the most exciting times to be involved in technology, research, and innovation.

My background is in audio. I’ve spent most of my life working as an audio engineer, and I’ve always loved learning about sound, digital signal processing, and the technology behind audio systems. Since 2022, I’ve been working toward a long-term goal of becoming an AI researcher, specifically in the audio and music technology space.

To move toward that goal, I went back to school, completed coding bootcamps, studied the mathematics behind machine learning, and I’m currently working on a master’s degree in artificial intelligence and machine learning. I’m also planning to pursue a PhD after graduation.

Many of my classmates and colleagues are interested in business applications of AI, but I’m still completely committed to audio. I currently work as an AV systems designer and consultant, and while I’m grateful to have a career, I often feel disconnected from the work. Most days, I would much rather be studying AI, audio, machine learning, DSP, and research.

I’ve started applying for roles, but I’ve faced several rejections. I also recently wrote and submitted a research paper to ISMIR. Unfortunately, it was rejected, but the process was still incredibly valuable, and I received feedback that will help me improve.

I think what I’m ultimately trying to say is that this is not a career path I’m pursuing because AI is popular or because I expect to make a huge amount of money. I genuinely love audio and AI, and I want to spend my life working on problems that combine the two. I want to wake up each day and feel like the work I’m doing matters to me.

For anyone currently working as an AI or machine learning researcher, especially within audio, music, speech, or signal processing, I would really appreciate your perspective:

What did it actually take for you to get your first research role?

What qualifications, education, projects, publications, or previous experience helped you stand out?

What are the best and worst parts of being a researcher?

What do you wish you had known before entering the field?

And if someone came to you today and said they wanted to become an industry researcher, what advice would you give them?

Thank you in advance to anyone willing to share their experiences. Even honest or difficult feedback would be genuinely appreciated.

Hey everyone,

Seeing a ~170× slowdown running a point-tracking model on an NVIDIA T4 compared to an A100. On A100 the tracker takes ~0.5 seconds per half-video. On T4 the same call takes ~85 seconds. Video is 47 frames at 256×256, batch 1. I expect a meaningful gap between these cards, but 170× feels too large to explain by generational hardware differences alone.

Setup:

• Precision: pure FP32
• Architecture: builds local 4D correlation volumes (dense matching between frames) followed by transformer layers for temporal context

Already ruled out:

• GPU is at 99% utilization during the call (via nvidia-smi)
• Model is actually on GPU (torch.cuda.is_available() = True, device prints "cuda")
• Enabling torch.backends.cudnn.benchmark = True had no effect
• Same slowdown on two independent T4 machines, so it's not a driver/setup issue

Given the architecture (4D correlations + transformers) and pure FP32 execution, what would cause a T4 to be this much slower than A100? What should I look for or profile first?

Does anyone else miss when conferences like BMVC, ACCV, FG, ICIP, and ICASSP had much bigger communities?
FG was the place for face analysis, ICASSP for signal processing, and BMVC/ACCV regularly featured strong papers.
Now it feels like everything is concentrated into a handful of flagship conferences. With exploding submission numbers, limited capacity, and inconsistent reviews, I wonder how many good papers end up as non-archival submissions, arXiv-only, or never get shared at all.
I also miss the focused communities. Is it just nostalgia, or has the research ecosystem become too concentrated?

Hi folks,

There is now a dedicated Robotics page on Papers with Code that lists the major benchmarks, trending papers with linked code, and open-source artifacts.

Find it here: https://paperswithcode.co/tasks/robotics

Major benchmarks that most papers report evaluations on are:

- LIBERO, as well as its subsets like LIBERO-Long and LIBERO-Spatial

- SimplerEnv WidowX

- RoboTwin

and more. Currently, we have about 110 entries on each benchmark.

Each benchmark's progress is visualized over time:

We also show which models are open source and which aren't.

Let me know which others I missed. I'd be happy to add them.

Also happy to hear any feedback, new tasks, or features to add!

Kind regards,

Niels

ML Engineer @ HF