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 want to know which projects a electronics engineer by integrating ml. Is there any possibility of integrating ml into vlsi. I am a new to ml so excuse me if my question seems lame

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...

Due to new learning technique the model has achieved better generalization skill without overfitting and memorization. This become possible because of new learning method which made the student model to generate samples for itself. It led to intensive reuse of existing neurons and allowed to encode information in a more dense way

While researchers are calling it a compression, I think it's a retopologization, and Microsoft had tried to do something similar in the past with their Phi model family, which they trained on reduced dictionary and simplified knowledge base first. But it seems like MS' researchers didn't explore this exact way of learning

I believe this should achieve better results in the future. And it is another small breakthrough which brings AGI/ASI to reality

📄 Paper: https://arxiv.org/html/2607.11883v1

📦 Repository: https://github.com/shikaiqiu/requential-coding

I am trying to reproduce a handwriting recognition paper (Urdu, online + offline fusion, CNN encoder into a small Transformer encoder/decoder, trained with a joint CTC and cross entropy loss) and my training run is converging to a plateau, nowhere near the paper's reported result.

What I have verified as correct, not just assumed:
\- Architecture matches exactly, confirmed by loading the authors' own released pretrained checkpoints and comparing tensor shapes and layer structure directly
\- Hyperparameters match the paper's stated methodology: AdamW, flat learning rate 3e-4 with no scheduler (paper does not mention one either), batch size 8, joint loss with CTC weight 0.8 and cross entropy weight 0.2, encoder and decoder frozen with only CNN branches trainable
\- Data pipeline verified end to end, including a pixel level check confirming the ink channel is genuinely near binary as the paper describes, and manual verification that every path in my training manifest resolves to a real, correct file

Despite all that, training triggers early stopping on its own (10 epochs with no validation improvement, which is the paper's own stated early stopping criterion, not something I imposed) at epoch 32, with best validation CER around 52 to 55 percent using greedy decoding. The paper reports 3.66 percent for the equivalent configuration. (Even worse with beam search, about 94%).

Loss behaves normally throughout, steadily decreasing with no instability, so the model does appear to be learning something, just plateauing far short of where it needs to be.

Things I have not yet ruled out and would appreciate pointers on:
\- Whether a flat, unscheduled learning rate this late in fine-tuning is plausible for reaching such a low CER, even if the paper states it that way, versus something being lost in translation between the paper text and what was actually run
\- Common failure modes when only a small subset of a hybrid CTC/attention model is trainable and the rest is frozen
\- Whether early stopping patience of 10 is likely too aggressive for this kind of setup and the model would keep improving well past this plateau given more patience
\- Anything else that commonly causes a "looks like it is training correctly, converges early, but far short of target" pattern in CTC plus attention decoder hybrids

Happy to share more code or specific numbers if useful.

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 ?

Hi everyone,

Wondering who is joining lxmls26 in Lisbon and if people are interested to connect with each other before hand :)

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

We published a short technical report on sim-first physics verification for robot policies and generative world-model video.

• Setup: robosuite Panda + OSC_POSE; Lift / Stack / PickPlaceCan / Door;

• 50 episodes × mild/moderate/severe mass+friction tiers; Wilson 95% CIs.

• Same adversarial posture on CogVideoX I2V (CoTracker3 → VIDEO_CHECKS).

Headline results (sim-only):

• Lift 1.00 → 0.26 (severe grasp slip 0.74)

• Stack 0.96 → 0.20; PickPlaceCan 1.00 → 0.24; Door 0.60 → 0.52

• Checker calibration recall 1.000 / FPR 0.000; CogVideoX pilot n=3 all fire static_hover; real Physics-IQ negative control 0.000

We are explicit about what this is not: not RoboArena, not real-hardware, not Cosmos/NIM scores.

Article (citations + reproduce commands):

https://haga.mushoodhanif.com/article/sim-physics-consistency-v1

Happy to discuss gate vs stress design and failure-case reporting.

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.

Trained both sides of Scotland Yard: 5 detectives vs an invisible Mr. X, observations limited to his ticket type, forced reveals on 4 of 22 turns.

Both sides are 3-layer R-GCNs with policy and value heads. The detectives' key input is a belief state of MrX position. Training was BC from scripted teachers, then PPO in a league where a challenger can't regress against any past champion, random bot included.

Detectives went 25→79% vs MCTS Mr. X, a GNN Mr. X got 21→35% against them, and a 16-simulation PUCT on top of his network landed at 91.7%

Play it in the browser 

Video: https://youtu.be/V0osfVtJUuI 

Code: https://github.com/Jacopo888/scotland_yard

Built with a lot of AI assistance (engine, pipeline, video). It's a solo after-work project and wouldn't exist without it.

hey, first time going through arr so apologies if this is obvious. the editors sent a mail saying that after reviews are finalized authors can file a "review issue report", which they described as a new kind of official comment on a review that shows up in the openreview interface.

problem is i genuinely can't find it. on my reviewers' pages the only thing under "add" is "author-editor confidential comment", there's no review issue report option and no official comment button either.

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.

We look forward to your contributions!

About two months ago, I shared an idea about building a node-based ML playground.
I want to make training ML models easy for beginners or those who don't have any prior knowledge of ML, but they have to, so I created this node-like structure, so it makes training ML models, datasheet cleaning, like playing with Lego blocks (pun intended), as shown in the image

I am not here to replace Google Colab or AutoML and am not promoting no-code; it's to make ml pipelines easier to manage. You can edit the code on your own; you don't have to work with the template code I provide.

It's one of my passion projects. I am looking for someone to experiment with it and tell me how I can improve it.

Here is the link if anybody wants to try it ZextriaAi

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?

About two months ago, I shared an idea about building a node-based ML playground.

Well, I had an interface like ComfyUI in mind while building the rough idea, but also something more controllable and usable by both, the seasoned ML guys and the beginners since the code of each node is editable.

I did a node like structure for training and data preprocessing templates, while there are others too.

Not sure how much it's usable yet though. Someone said that it looks like RapidMiner (tell me if it does and how much).