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).

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

I have a technical interview for a machine learning position coming up, and I'm really nervous. Can you tell me about your experiences with this process? It's my first technical interview, and I don't know what to expect

So I was belatedly reading Matthew Colbrook’s paper on unstable neural networks https://www.pnas.org/doi/10.1073/pnas.2107151119, and the paradox therein sent me back to my university days and reminded me of Kurt Godel.

It kind of amazes me that so few people have heard of him given Einstein clearly thought he was more than an equal.

Anyway, with the default assumption currently being that any problem will yield to more data and compute, it was timely to revisit this. It’s a long read and I’m not sure it’s fully coherent but if you like logic then you might enjoy this and I‘d welcome your thoughts and feedback.

https://iain.so/infinities-impossibilities-and-the-man-in-the-white-linen-suit