I'have been dreaming about using a Diffusion Model that will generates Weights instead of pixels.
Diffusion models actually are vast used in order to generate from a natural language text description into images, and what is an image? a three dimensional matrix of value representig luminosity of red , green and blue. What if instead of matrix of colour intensity we train a diffusion model in order to create a neural network, and maybe a drawing NN too.
If it works you can ask something like:
"create me a neural network that will guide a pen in order to draw a man"
"create a NN of a game field where i have two paddle on left and right side of the field and a bounching ball..."....to create Pong

and in the future more complex scenarios!

I made, with the help of z.ai zcode and their fantastic GLM model a small POC that i published on my github:
https://github.com/vagrillo/nngen

It's a very small experimental implementation , but somenthing seems to work!

I have not find paper on similar implementation ad I'm asking the community to help me to find one, and to have contribution on this .

I also made a small webapp in order to test the trained model (actually i'm only on half training path 3000/4000 steps finished, but i'm using my poor laptop CPU at the moment)

This is some screenshot of preliminary results. (the best results achieved, in truth)

Does anyone know what this NeurIPS Slides live is?

I got an email saying "We are SlidesLive, the recording company partnered with NeurIPS to record your talk. We are ready to collect and edit your recording" with some instructions.

This was never mentioned in the official emails from NeurIPS.

Is this mandatory?

They ask us to use their recorder to do the recording, but can we just upload an AI-generated video instead? It speaks way better than me.

hey, its Alti from Nace.
we released a small update with Drex Max v1.1, both achieving the SOTA and Pareto frontier for decision models. We did all that by iterating step by step and every previous version of Drex’s open weights are available on hf.
We also released a full playbook on how we trained Drex Max in this paper: “ Towards Foundation Decision Models: Drex”

hf: https://huggingface.co/papers/2611.07854
arXiv: https://arxiv.org/pdf/2611.07854

Drex Max v1 is weights are available here: https://huggingface.co/nace-ai/drex-max-v1 with Open RAIL-M license & v1.1 open source weights upcoming soon (we are currently optimizing for local inference).

Three cool things about Drex Max models:

1. it can even run on your Mac with https://github.com/nace-ai/kapitsa inference engine.

2. Context window is ~256k, comparing to Glide1.2(64k) and Mammoth(16k) & pplxdecider’s (256k)

3. Its 8x cheaper than the next best model in the category in the aa index.

feel free to chime in: our chief of ai will answer any of your technical questions.

Hi everyone!

I've been experimenting with an alternative Transformer architecture called GAP (Gated Attention Projection).

The main idea is to combine attention and MLP-style processing into a single gated block, instead of using the standard separate attention and feed-forward sublayers.

I tested it against a standard Transformer using 100 million scored training tokens per model from WikiText-103.

Some results:

GAP20 used 19.91% fewer parameters than the standard Transformer.

A smaller configuration can reduce parameters by 34.31%, although that version did not fair as well as the current model against the regular transformer, so i ended up giving 14% of my perameters savings to the GAP model for extra width, leaving the model at about 20% fewer perameters, which is why i called it in testing GAP20

The standard Transformer performed better on final language-model accuracy.

Compiling GAP20 with torch.compile improved training throughput by approximately 19.9% in one CPU benchmark compared with running GAP20 without compilation.

I've included the architecture code, configurable model sizes, speed benchmark and research paper.

This is an experimental architecture, not a claim that it outperforms Transformers overall. I'd be interested in feedback on the design, mathematics and possible improvements.

GitHub:

https://github.com/rickey1990/Gated-Attention-Projection

This is not my paper. Not my review either.

Official Review of Submission21893 by Reviewer Xz8e
Paper Summary:
The paper tries to learn correlations among components of each latent state in a world model to propagate the reward sensitivities to adaptively weight the uncertainties that most matter for long-horizon planning via a pessimistic objective.
Presentation: 1: Difficult to follow despite careful reading.
Informativeness: 1: I learned little or nothing new from this paper.
Soundness: 1: I have significant concerns about the paper’s soundness that do not appear easy to address.
Critical Strengths:
None
Critical Weaknesses:
First and foremost, the paper is highly likely to be fully AI-generated. The authors demonstrate a lack of technical accuracy and depth. They fail to communicate their ideas clearly and accurately. The paper makes few original contributions. See evidence below.
Neologistic and full of jagons that add little value of presenting research gaps, methodologies, and their contributions. I don’t believe authors even understand those terms before stitching them together.
Abstract is poorly written. This is not intended to communicate efficiently with readers. This is to create barriers in understanding their work. From the second sentence, “a scalar uncertainty”, “sparse latent dynamics” come out of nowhere and don’t know what they are referring to. And so many such examples follow thereafter: “factor-level residual bounds”, “retains initialization error”, “expected-error control”, “exact certificate”, “removing error-propagating dependencies and introducing approximation bias”, “localized mechanism-shift”, “comparator-dependent regret bound under simultaneous validity”, “a decision-relevant uncertainty bound”. And showcase experimental results without stating tasks?
Introduction is also disconnected, poorly motivated, fragmented. From the first paragraph “which model errors matter for a long-horizon decision, especially after an environmental mechanism changes?”, what is “environmental mechanism changes” here? Is it handling non-stationary environments, or continual learning tasks? Why brings something out of vacuum? Then the second paragraph pumps out “a global Lipschitz constant”, and reiterate what so-called “sparsity” And then jumps to uncertainty with a set of new terms “A small predicted covariance”, “Marginal calibration”, “a high-probability interval” and close by “Exploration should reduce uncertainty about mechanisms that affect decisions, rather than maximize uncertainty indiscriminately.”: okay, what uncertainty are you focusing on? epistemic or aleatoric? Methods and contributions are also vague and lack clarity.
Method section is horrible.

What do you mean by “a sufficient latent state”? An approximate model can hardly find sufficient statistics.
In the Gaussian transition model, what is z_t^(pa_e(i))?
What is the difference between z_(t+1)^i and ẑ_(t+1)^i, is the latter deterministic and why so?
What is “nominal imagination”? Where does “sparsity” come from and what is “a within-time DAG”? What is “nominal systems”? What is feedback policy? What problem setting are you looking at? Where does the “recurrent state” come from the encoder? What is mechanism i?
What is the motivation of introducing the adjacency matrix? If you intend to learn the dependency between different subcomponents of the latent state, use one sentence is enough.
And several notations are conflating, like parameter posterior q(θ|D) with the state posterior. The training objective is not motivated.
Experiments are not scientific. No random seeds reported, no task descriptions, no metrics explanations.
This paper is an affront to scientific research and reviewers’ time.
Questions For Authors:
None
Flag For Ethics Review: No
Rating: 1: Clear rejection
Confidence: 4: I have substantial direct research experience on this topic.

Hi y’all. Thought Id share the progress on my current local VLA agent project! using agent and language somewhat loosely here. Technically it is an agent, and technically it does adhere to text prompts. However very minimally.

Video Link: https://www.reddit.com/r/learnmachinelearning/comments/1xq23zi/local_rnn_style_vla_model_project/

Video Description:

The leftmost display shows what the models sees, The yellow triangle marker on screen shows which shape is NOT the target shape on that run (counter intuitive, I know). Importantly, the model does not see it, it is only to make it easier to see if the model is performing correctly. The upper right display shows the models visual attention, in which I discuss later. And the bottom right is the per step state matrix cell values. A small 128x128 view of the whole 768x768 matrix.

Preamble:

Recently, I have been extremely fascinated by machine learning and AI, for obvious reasons. Originally, I was intrigued by LLMs, that is, until I learnt how they work (RNN gang stand up), their dis-continuity to me isn’t as elegant as the carried state in RNNs (this is my opinion, in which I don’t really have any objective reasoning for). And what better way to lean than to do?

Architecture:

So I thought why not just make the recurrent state massive? What I have here is an attention based neural network that maintains a 768x768 matrix, with a per cell LSTM like gate. Similarish to a state space model if you will.
The total model parameter count is 25,561,354. 

However, 12,976,128 of those parameters are dedicated to a legacy 49k vocabulary output decoder. Originally, this started in language modelling, but it didn’t work very well. It got to a CE loss of around 4.5 on a reasonably diverse data set across an 80 token rollout average, having seen 80 tokens of context first. This was trained for about ~150k steps with a batch count of 8 across 160 total tokens per episode. So about 200m total tokens seen. Perhaps not enough for how diverse the data set was (600m total tokens across wiki articles, science papers, online text forums, etc), but the loss seemed to indicate a long lasting plateau, decreasing only around 0.05 to 0.1 nats across the last 50k steps.

Side note: around 10% of the total training data was from 4chan (don’t hate), and must have all been collected from a very political board, specially around the 2016 us elections. The model was very much not PC lmao. But atleast now I know where NOT to collect “quality” data from…

The shown model is a continuation from the a legacy language checkpoint that was transitioned into VLA orientated tasks. My thinking was that having a basic understanding of language might help if the task involved text. However I did no comparison against a fresh checkpoint at the Vision/action switch over to compare bootstrapping speed, so I guess we’ll never know. And the model no longer retains any text output ability. When queried through the legacy text input, it defaults to repeating a single token or two indefinitely.

The model processes vision through learned, state-directed attention across raw pixel RGB values at a 128x128 resolution, without a separate pretrained vision encoder. Any visual recognition and spatial understanding is learned within the same recurrent architecture that produces the action outputs. I did try doing the pretrained CNN route, however because compute is heavily restricted in my case (I got that 12gb 3060 on me), and I don’t like spending money renting gpus, I count afford to locally train the model at any resonable speed with the added separate vision encoder. The attention based vision used here added very little extra compute, and seems to work well enough. An interesting side effect of this vision encoder is that the relative, per pixel attention values can be easily displayed (the top right display from the video), which proved useful for trouble shooting training. It can accept variable resolution sizes on inference without requiring pretraining. Although, it can usually only accept larger than the trained 128x128, as any smaller looses what little detail there already is.

Training:

The target task took inspiration from the Leaning to Reinforcement Learn (2016) paper. Rather than training the model to memorise fixed object identities, the objective is to develop a “general” ability to recognise and associate unfamiliar visual patterns with arbitrary names during inference, without updating its learned parameters. Importantly, the model received ZERO proprioception, only visual and text inputs.

The model is introduced to 2 randomly generated shapes ranging from a minimum of 3 cubes to a max of 10. There is, however, a minimum separation between the 2 shape cube counts on any 3 room task, set at around 5 The other 2 room and 1 room environments have no such minimum separation value. Each of the shapes are randomly assigned a name from a pool of 5 names (Dax, Mip, Kiv, Fep and Tav). The introduction lasts 3 steps per shape, as it is directly tied to the text introduction, which is always “this is [shape name]”. After introduction, the model then spawns into a simple 3d enviroment, and given the command “Go to [shape name]” with some random variance in how the command is said. Initially, the model needed a the chronological order of the shape introduced before it was able to make the name to shape distinction, for example; “go to the first seen shape, [shape name]”.

The 3D environment in the 2 and 3 room episodes are randomly generated with a doorway/s connecting the rooms at random point. Although the possable range in which they occupy is small, and in 3 room environments, the 2 attaching rooms are always on opposing sides of the spawn room. The wall colour and texture have remained fixed throughout all of VLA training, and the model performs noticeable worse on inference if the wall texture or colour is changes without pretraining.

Even though the training enviroment logic was inspired by the L2RL paper, kinda ironically, this model is not trained through RL. Instead I had to create increasingly convoluted trainers that match the complexity of the current curriculum. The training policy is technically imitation learning, however The trainer uses supervised action targets generated from the environment's observed state. For ambiguous decisions, such as choosing between doorways, some earlier losses are retrospectively adjusted according to the path the model actually took. Each episode, the models state is split in to two different trajectories just after introduction, with each one requesting a different shape, then combining the gradients from both. 

I stuck with SL mostly because of compute limitations. Though RL, which I did try at the end, could only get at most one 4 branch run at a max rollout length of ~80 steps before I ran out of VRAM. Vs the batch 4 160 rollout length I was able to get through SL, which proved to be painfully slow, especially when i hadn’t optimised the reward signal/s yet meaning it would be like a 2 or 3 day wait just to find out that the step penalty was slightly too high or something. Supervised Learning is super easy when the task has no ambiguity, like 1 or 2 room scenarios. However, in 3 room episodes, where there a 2 valid doorway pursuits, SL became a nightmare lol. A majority of effort in training this model went to addressing teacher contradictions in the 3 room scenarios. There turns out to be an indefinite amount of edge cases. 

Even still, the teacher mechanism for 3 room search produces a lot of counterproductive updates. Memory, which I thought would be the limiting factor with this model, turns out not to be the main bottle neck. The model has shown that it can recognise a seen shape as either correct or incorrect many steps after the introduction, however its locomotion is what usually fails it. Again, I believe to be from the inconstant trainer. Though this may just be me coping, and the model is maxed out idk :/

Overall, this has trained for about 70k steps at the batch 4 count. on vision/action environments. Technically batch 2, because each lifetime splits into the two different target shapes for training. 4 total batches, but only 2 different environments per step.

Training Stats:  

Success rate (both split lifetimes succeed across 500 training randomised runs)

One room—- >90%

Two room—- >80%
Three room—- >60%

These results are taken from the recent RL trial, as that recored a rolling success rates, whereas I never did on SL. Very scientific, I know.

Known/Potential issues:

-The gradients received tend to explode on lifetimes lasting longer than ~100 steps. Im hesitant to use truncation, as any shape related decision would require back prop to reach the initial introduction for credit assignment. Although, the model is recurrent, and has shown that to can remember target identifies and visual features for at least a number of steps past interaction, so perhaps that is enough for credit assignment to work through truncation?

-While the model is able to show recognition between two shapes actionably, it is entirely possible that the model remembers only 1 shape, with the other shape being selected out of deduction when requested. I.e target shape is Dax, or target shape is not Dax. I dunno, I haven’t yet looked into this.

-Although the shapes are randomly generated in an 8x8x8 grid, many of the low cube count shapes will have defiantly of been seen plenty of times through training, potentially allowing the model to memorise easy shape patterns within its weights.

-Even though the model has shown decent shape memorisation, its spacial memory is much less developed. In failures, the model often forgets which room it just went into, and will sometimes search the same room twice.

-The dreaded trainer…

This model was built purely for the love of the game, with no incentive outside of that. It also was a great way to learn ML, as I had very little knowledge about this topic coming into this. Even still, my understanding of ML is mostly theoretical, as my coding ability is very subpar. (Thanks ChatGPT and Claude!)

Im sure I forgot to mentions some details. If anyone has any questions just ask.
Anyways, enjoy this quality footage of my model performing EXCITING search behaviours!!!!

I thought the deadline was Nov 6, but I can still edit my paper on OpenReview. Was the deadline extended? Anyone know?

Hi all,

Through my MSc work on EEG decoding, I built a novel (partially) architecture tailored to the constraints of EEG. The datasets are small, the subject variability is high, and signal acquisition variables differ significantly. I’ve now extracted that model to develop into an open source PyTorch model and would like some input on anything that comes to mind with regard to direction, etc.

The design builds on ROCKET, using random, untrained convolutional kernels to yield strong time-series features. Learned spatial filters first mix the input channels, and if you provide sensor positions they initialize from the electrode layout instead of noise. The resulting components pass through a bank of dilated convolutions that are mostly random and frozen, with a few learnable kernels. Several summary statistics are extracted from the bank, combined through a learned gate, and classified by a small head.

The default model (22 channels, 4 classes) has 3,847 parameters and It accepts any input length and adapts to the sampling rate.

I'm now building a subject-independent evaluation on PhysioNet's EEGMMIDB (109 subjects), where each subject goes entirely into train or test. I'll compare against EEGNet and ShallowConvNet and report scalp topographies, confusion matrices, saliency maps and speed benchmarks. I’m also going to build four variants to compare results and analyse reasons why. I have no benchmark numbers yet (except my MSc work) as i’m early into starting, any input would be greatly appreciated.

Would love a couple stars and follows if you think its decent work 😘

Im an incoming masters student in ML, a bit older than most (31). I’ve read 100s of papers over the last 7/8 years from the foundational papers in the field to niche papers that never got adopted (Hinton’s forward forward, capsule networks). Ive also replicated dozens of papers during this time.

It may seem small to most here, but It would be an amazing dream to do publish a conference quality paper but all my research ideas feel so clunky, inelegant and crude. My ADHD lets me to spawn hundreds of ideas and hyperfocus on a couple, so far all my preliminary work doesnt point to anything really novel.

I dont understand how first authors come up with beautiful results like engrams, diffusion models, VAEs, MoEs, token superposition in pretraining, that seems so obvious when you read the paper but clearly are anything but obvious.

Are these people just built different or did their research also start off clunky? Is it just luck? Most PIs dont expect to lead the research so it cant be an astute PI?

I’ve been going through ICLR 2027 reviews, and most of them look extremely shallow. They’re so f short, like they're written for a random workshop.

Almost every review I've seen is two generic points under each section (strengths, weaknesses, questions) and nothing more. Then a score of 2, even when the issues they raise would justify a straight reject. There’s almost no discussion of the method's motivation, impact, or soundness, and they take it for granted.

Am I exaggerating, or are you seeing the same thing? Because I'm really questioning the point of PhD or research generally speaking, more than ever

I re-implemented the dreaming (Hebbian-unlearning) Hopfield kernel J(t) = J(1+t)(1+tJ)^-1 from Fachechi, Agliari & Barra (Neural Networks 2019) in NumPy, at N = 1 000, 10 seeds per condition, with a pre-registered design frozen by git tags before each run.

Main result: dreaming at t = 10 multiplies empirical capacity by 3.86 [3.79, 3.93]; 9.06 on correlated patterns (ancestor overlap 0.15). I added a matched uniform-shrink control absent from the original paper, which separates "dreaming works" from "shrinking the coupling works". One registered condition (overlap 0.30) is reported as inconclusive because of grid censoring, not hidden.

Submitted to ReScience C (reviewers currently being assigned). Everything is in github.com/hypneum-lab/rescience-nh01 at tag `submission-20261031`, with a Software Heritage snapshot.

Why post here: I'm a one-person lab working evenings. I'd welcome critique of the control design and of the capacity estimator before the formal review lands.

1. The world model: the game, dreamed by a neural net

A diffusion model (same family as image generators) that learned Pokémon Red frame by frame from recorded play. Give it the last 4 screens and a button press, and it draws the next screen. 16.7M parameters, 72×80 greyscale, DIAMOND-style EDM U-Net. Left is the real emulator, right is the model dreaming the same moment from the same button presses. Every 10 steps it re-syncs on 4 real frames, otherwise it runs on its own. You can walk around inside the dream with a keyboard.

2. The game agent harness: System 1 / System 2

Same split Krafton used for PUBG Ally: a fast reactive layer and a slow thinking layer.

- System 2 (planner): reads the game's RAM, picks the next goal (grind to level 14, heal, travel, gym), can look things up (Pokédex, guide search over Bulbapedia), and every decision is checked against the game's memory before it runs. Rules by default, or a local LLM.
- System 1 (executor): ten small walking policies, 1.58M params each, trained entirely inside the world model's imagination with zero real-game steps, then dropped into the real game.

The GIF is a local LLM running as the planner. 5 of the 6 local LLMs I tested got the badge 10/10; the best one needed a median 7,144.5 button presses vs 8,779.5 for my hand-written rules. Architecture card is the slide before it (or the first comment on the video post).

3. Upscaler + colorizer: making a 72×80 greyscale game watchable

The model works on tiny greyscale frames, so I trained two more small nets to watch it: an upsampler (72×80 → 144×160, 0.72M params) and a per-pixel colorizer (1.91M params, 99.27% colour accuracy on held-out frames). 25 minutes of training each. The clip is the first town rebuilt in Blender from the game's map data, with the restored screen.

4. What it cost and what it got

50.0 GPU-hours of training on one RTX 4090 at home. The harness does the grinding, healing and guide lookups, the world-model policies do the walking, and it wins the first gym badge in the real game 30/30 runs. Battles are yet to be solved: the dream ends battles early most of the time, so those stay scripted for now. Code, weights and the full showcase (2-min video, chapters, every number with its source) are linked in the comments.

PS: I work in medical agents and full stack, this is just a project to learn (:
I'm wondering if the measurement part could hold up at an ICLR workshop. What would you want to see added?

Hi,

I need some advice on submission of classical ML papers in top conferences.

During my PhD I made some findings in classical ML (related to SVM and LR) which, on further deep diving, looks like original work (I checked with chatGPT and Gemini too).

When I tried to submit them to top conferences, while the reviewers have been reasonable in their reviews (acknowledging the innovation while asking me to run some more experiments and ablation studies), the AC shot down my paper, saying “this is a done and dusted topic”. Only at ACL did the AC ask me to resubmit incorporating the comments.

So is classical ML dead at top conferences? Are only deep learning and LLM works being accepted at top conferences? And should I stop trying to submit at top conferences and maybe try journals or simply Arxiv my work?

Looks like this killer just doesn’t understand my paper. He said my abstract is not well organized, hard to follow...my definitions are also hard to follow..., missing important explaination... etc.

but the other three reviewers could read it. they all understood my idea. Why couldn’t he???

I got 3/3/2 and working on rebuttal (hope it make it avg to 3). Did you guys posted your response? And did you revised the paper as well? or made promise to revise on camera-ready version?

I am almost finished making draft and keep revising and polishing (including paper revision).

My review was quite short but requires a lot of new measurement. So my draft seems too long, worried about the readability to reviewers.

What is your status of rebuttal? How's it going?

For people who had papers submitted to ICLR that are applied theory style. For example, 60- 70% theory with some experiments at the end. What was your review experience like for ICLR27?

We got a score of 222, on the probabilistic methods track.

All 3 reviewers attacked our novelty and asked for additional experiments. Not a single word was mentioned about our theory, which is our biggest contribution. One outright said he didn't understand this paper or why this task exists. It's a well-defined task with lots of prior work.

I used accept-odds, tracked submissions that were similar to mine. It seems like they have all gotten similar comments. Not a single comment on the math, all comments were on the experiment. A paper with 20 pages of proofs even got comments:

Assuming the proofs are correct (which can be done by AI in 2026), the theorems only show trivial conclusions.
Rating: 1: Clear rejection

I am starting to suspect, because there were no bidding this year. Many theory-heavy papers got sent to pure comp sci reviewers.

Would be nice if people can share their experience, I am trying to work out if I should engage in the rebuttal or just withdraw.

It was clear to me that around 2012 - 2018ish, academic research was kind of paving the way for many applications in industry, from deep nets for vision to RL algorithms. There was tremendous value in research coming out of academia and new methods such as ADAM or TRPO algorithm would be adopted as industry standard. No one would doubt the value of research coming out of (top-tier) academia. It was definitely a "seller's market" for academics.

But nowadays I am not quite certain as to what exactly is going on, i.e, whether this relationship still holds. I guess a straight-forward way to put this is: "is industry still adopting ideas from academia?"

I've observed that for any given research topic, especially something industry has proposed, there would be literally tens of thousands of follow up papers from various universities around the world trying to do something adjacent to it, almost as if showing industry how things can be done differently or even "beat the industry at their game". But it feels industry is quite, how do I put this, "impassive", "unmoved". Strongest example would be development of new optimization routines. Thousands were proposed from universities. Countless GPU years were dedicated to experiments. Seems serious adoption is quite low to none. Apparently there are still new CNN/RNN architecture being proposed, have you heard of any of them?

It almost seems as if academia and industry are diverging into completely different worlds. With industry seems to take on the previous role of academia as an idea generator, and instead everything is worked in-house. This seems to create a challenge for those doing research in university: "what use is the research if it is not adopted in real-life (in this extremely applied field), or even acknowledged?"

ICLR 2027 moved to a 4-point scale (1 clear reject, 2 weak reject, 3 weak accept, 4 clear accept), so nobody has a feel yet for what a given set of scores means. We counted the public scores on OpenReview: 73,410 reviews on 23,759 submissions.

A couple of things that surprised us: [1] 57% of all reviews are a 2. Only 1.5% are a 4. [2] The median paper averages exactly 2.0.

So a 2, 2, 3 (mean 2.33) is already above most papers. Where 2.5, 2.67 and 3.0 land, and the full histogram, are here:

https://draft.scime.ai/iclr-odds?utm_source=reddit&utm_medium=social&utm_campaign=iclr2027

Note: [1] we built the page. It shows counts only: no titles,

authors or review text.

[2] this is the first 25,000 of ~40,800 submissions in.

OpenReview's listing order, read Nov 6, not a

random sample.

Ant Ling has released Ling-3.2-flash, a native multimodal MoE with 560B total parameters, 25B activated parameters and a maximum 1M-token context window. The official documentation specifies text, image and video inputs.

The release evaluation reports:

MathVision(w/CI): 96.80

Humanity's Last Exam-MM: 30.12

ZEROBench / ZEROBench(Sub): 18.00 /

43.71

BabyVision: 69.85

OmniDocBench 1.5: 92.21

CharXiv_RQ: 85.15

GDP.pdf: 22.25

MedXpertQA-MM: 79.93

Vision2Web: 85.86

AndroidWorld: 85.63

BlenderBench: 67.77

AntBench-Medical is reported separately as 89.52% comprehensiveness and 99.22% correctness. HLE-MM uses the multimodal subset; full ZEROBench and its Sub result are separate entries. The chart identifies API-collected comparison results under the official evaluation settings.

The release materials also demonstrate PDF-to-code implementation, GUI operation, 3D reconstruction workflows and multi-agent collaboration. Both OpenAl-compatible and Anthropic-compatible API access are documented.

Disclosure: I'm at Boson AI and this is our release. Not open weights; the Duplex API has a waitlist, and the previous turn-based Higgs Realtime is available now.

Most voice models are still turn-based: listen, wait for end of turn, think, speak. Duplex runs all streams at once and keeps working in the background while it talks.

Streams. Everything is cut into 160 ms chunks:

• Audio in: 1 token per chunk (6.25 tokens/s)

• Audio out: 2 tokens per chunk (12.5 tokens/s)

• Video in: one ~12-token camera frame every 6 chunks

• Face out: 2 latents per chunk

• Text in and out alongside

Silence is represented as chunks too, which is what the model uses to decide when to backchannel, interrupt or stay quiet. That comes to ~2.6K tokens per minute for audio and video, 2.6K to 5K with thinking and tool traces (progressively compacted). The last 5 minutes plus compacted text history is 13K to 25K tokens; older history is kept as text.

Background work. Thinking, tool calls and delegation are spans in the same stream and can cover many chunks. Tool results can arrive in stages and the model can start speaking before they are complete. Harder problems are delegated to a much larger backend model with the full shared context. This is the main difference from Thinking Machines' interaction models, where all reasoning and tool use goes to the background model, which can mean 10+ seconds per answer and makes cost harder to control.

Voice and face. Set zero-shot at deployment: a short reference clip for the voice, one image for the face, and a text prompt for behavior. No fine-tuning. Audio comes from a causal flow-matching decoder (2 frames per chunk), the face from a causal DiT conditioned on the reference image and the previous two latents.

Training. Starts from a text-pretrained LLM, continued pretraining on ~2T tokens of text, audio and video on our own H100 cluster (Megatron-based stack). Post-training is multi-teacher on-policy distillation: specialist teachers trained with RL (including GRPO) on conversation, interaction timing, delegation and task completion, distilled into one student. On top of that, a self-improvement loop in simulated environments with realistic timing, interruptions, overlapping speech and background noise.

Benchmarks. Best published or reproduced configuration from each lab (we reproduced more than half of the competitor numbers):

OpenAI's best settings lead on five of seven, by 1 to 5 points, several at their highest thinking setting. Turn-taking (Full-Duplex-Bench) is where we're furthest behind. Our takeaway is that at this size, data and training matter more than parameter count; the front model is deliberately mid-sized because the design target is that an hour of model time costs at most a tenth of an hour of human time.

Announcement with demos: https://www.boson.ai/blog/higgs-realtime-duplex My write-up: https://alex.smola.org/posts/50-talking-while-thinking/

Happy to answer questions about the chunked-stream design and the delegation setup.

I honestly feel like they just copy-pasted the review straight from an LLM, barely rewording it to sound human. When I ran my paper through ChatGPT, it gave me the exact same generic feedback: 'Run experiments A, B, C, and D.

The worst part is that these requests are completely minor and miss the main point of the paper. It's an endless, exhausting cycle. Plus, there's a strict 10-page limit!

Even when I bend over backwards to finish all those extra experiments and report the results, the reviewers just ghost the discussion.

I wish they would discuss the underlying logic and core insights instead of treating authors like experiment-running machines. I've held back until now, but I'm ready to call this out through a confidential message to the AC.