Benchmark scores (GPT-6 uses a harness for ARC-AGI-3, and is at about 60% without one):

https://openai.com/index/gpt-6-astra/

Prior to the launch, OpenAI President Greg Brockman said "I think it’s not unreasonable to feel that we are now in the AGI era".

Three weeks from decisions even. I wonder what percentage is industry and VC funded AI labs looking to mingle and recruit.

Has anyone else received an AAAI-27 desk rejection related to modifications to the title or abstract between the abstract-registration deadline and the full-paper deadline?

What I’m trying to understand is how the modification rule is being applied in practice. The AAAI-27 modification guidelines say that the title and abstract can still be edited after abstract registration, while warning against substantive changes, and describe rejection in terms of changes that make the submission describe qualitatively different research.

In my case, almost everything was identical. The modifications were incredibly minor.

The rejection notice says that the decision is final and appeals will not be considered.

Did this happen to anyone else?

LLMs describe physics well but don't "understand" it in any grounded sense — they've learned statistical relationships between tokens like "falls" and "gravity", not actual physical intuition. This is basically the Mary's Room problem: Mary knows every physical fact about color but has never seen one. LLMs are Mary.

The idea I've been thinking about:

1. Train a JEPA-style model inside a physics simulation (think MuJoCo or a simple 2D env). Instead of predicting pixels or tokens, the model predicts representations of future states in an abstract embedding space. If it gets physics wrong, the predictor fails — the loss is unforgiving in a way next-token prediction never is.

2. The embedding space that emerges should encode actual physical structure — object permanence, momentum, trajectories — because that's what makes prediction possible. Not surface-level textures, just the principles.

3. Freeze those representations and attach them to an LLM-style reasoning model as a conditioning signal. The LLM now has both linguistic physics knowledge AND grounded physical intuition it can actually "run" forward — closer to a computational primitive than a propositional fact.

The hypothesis is this makes downstream learning significantly faster — the LLM doesn't have to rediscover that objects fall, it has representations that already encode that.

V-JEPA does something adjacent for video (predict future frame representations, not pixels), and DreamerV3 uses a latent world model for efficient RL — but the specific combo of JEPA-style prediction + sim-grounded physics representations + LLM attachment doesn't seem to have been done cleanly.

Questions for the community: - Is there prior work I'm missing that does exactly this? - What's the right interface between grounded JEPA representations and a language model? Just concatenate to the prompt embedding? Cross-attention? - Would the sim-to-reality gap kill the transfer, or are the representations abstract enough to survive it?

Interested in whether this is worth building a small prototype for.

Hi everyone,

I just quickly wanted to share a paper I was working on for around a year now. I created this summary website with key results: https://flogrammer.github.io/moljepa/

TL;DR: its a multimodal JEPA model for molecules.

There will be more work to do to improve performance and I would be happy about feedback and ideas :)

I’m the author of a new preprint on repeated-query auditing of LLM brand recommendations, and the founder of Rankfor.AI.

The practical question: how many times should we repeat a prompt before comparing results?

The paper applies generalizability theory: estimate variance components from a pilot, then calculate the repeat count needed for a chosen reliability target.

Tested the reliability predictions on three independently collected corpora covering political-orientation questionnaires and benchmark stability. Across 39 prediction cells, 37 met the prespecified replication criterion and two were partial matches.

The fixed iteration thresholds did not transfer. Other preregistered tests, including parts of the drift diagnostics, also failed. Those results are reported in the paper.

An important limitation i see is that these external corpora do not contain brand recommendations. They test the statistical machinery outside our original application which is independent replication on repeated brand-recommendation data remains outstanding.

I’d particularly welcome criticism of the pilot-based variance estimates and the reliability validation design. Does anyone know an independently collected brand-recommendation dataset with repeated identical prompts?

Preprint: https://arxiv.org/abs/2609.04047

External validation materials: https://github.com/Rankfor/rankfor-open/tree/main/research/dice-roll-method/external-validation

Just uploaded the full 5.94 billion TikTok video dataset to Hugging Face. It’s fully open source:
https://huggingface.co/datasets/kuben-developer/tiktok-videos-4b

This dataset was collected using a TikTok mobile app reverse-engineering method I developed a few years ago. The method allowed me to extract billions of videos, profiles, comments and replies, hashtags, sounds, and more.

Full write-up and code here:
https://tiktok-api.seeksocial.io

Disclaimer: The TikTok app exposes 24 endpoints that can be accessed without a TikTok account, so the data itself is publicly accessible. But accessing it this way is probably still against TikTok’s ToS. Also, the full code is not free, I charge a small fee for access to it.

I honestly am so confused rn as the ml community is overburst with people only caring about building rag modules and agentic ai for larger corporations.

I have a passion for machine learning but honestly it feels really confusing as to what really counts today. I would love some advice on what exact learning counts in this field like what exact topics, tech and tools?

I've spent the last month building a local C++ machine learning library called Deepity to test alternative credit assignment algorithms; specifically Predictive Coding Networks (PCNs). While PCNs are fascinating for biological plausibility and continual learning, naive implementations are painfully slow.

By implementing recent research (Accelerated PCNs via Direct Kolen-Pollack Feedback Alignment) and utilizing algorithmic caching to bypass redundant forward projections during the inference settling phase, I managed to close the performance gap with backpropagation on my CPU when training on MNIST (50 epochs).

• PyTorch Backprop (Feedforward): 98.27% test accuracy in ~70s.
• Deepity DKPPCN: 97.73% test accuracy in 59.5s.

Next up is porting these kernels to CUDA to scale up the architecture and testing its capabilities in continual learning scenarios where standard backprop struggles.

If you are interested in local learning, alternative credit assignment, or HPC for ML, I'd love your feedback!

• GitHub: https://github.com/ra4ster/deepity
• Project Site: https://ra4ster.github.io/Deepity

Why did I sign up to review when it’s not reciprocal?

Am I an idiot? Am I dumb to sacrifice some of my precious time outside of work to review these papers when I don’t even have to? Yes.

I tell myself I’m giving something to the community. But all I’m really doing is pissing off the authors as I reject their papers.

I really wanted to accept one of them too. But, it wasn’t as well done as I’d hoped. Strong reject.

Nobody made me sign up. Nobody even asked me personally. They sent a lovely form email that goes to everyone who’s published there.

I let feeling important convince me to do it. How dumb of me!

At least it’s only a couple of papers and a small amount of my time. And I’m learning something new reading stuff slightly outside my direct field, that I would never normally read otherwise. And I get to hone the skill of critical reading, thinking, and generally understanding how a paper should (or should not) be put together.

Maybe it was a good idea after all.

How does everyone else feel about non-reciprocal reviewing? I imagine those that agree to do it are in the minority.

Jasper Research just released a cookbook on how to build a text-to-image model from scratch.

It shares the full reasoning and intermediate results, making it ideal if you want to deep-dive into text-to-image models, or if you are curious about how frontier labs build them.

The cookbook also includes a 100M-image dataset and a codebase with a tiny model, so you can train a text-to-image model from scratch.

Here are the links:

Cookbook: https://huggingface.co/spaces/jasperai/t2i-technical-interactive-report

nano t2i: https://github.com/gojasper/nano-t2i

Monet Dataset: https://huggingface.co/datasets/jasperai/monet

Disclosure up front: I'm the original first author of CABiNet (ICRA 2021), so I'm not a neutral party. Everything below is reproducible from the repo.

Background

CABiNet is a dual-branch CNN for real-time semantic segmentation: a high-res spatial branch, a lightweight context branch (global aggregation + local distribution) over a MobileNetV3 backbone, fused with a small FFM. Published 2021, then it went quiet.

I came back this year, rebuilt the repo (PyTorch 2.x, Hydra, AMP, EMA, poly-LR, OHEM loss, CI + tests), and used it to ask one question on **UAVid**, the aerial dataset the original paper targeted: how does a purpose-built 2021 efficient architecture compare to a 2026 general multi-task model with a dedicated semantic-segmentation variant?

What's actually controlled (and what isn't)

Both models run off the same converted dataset and splits, the same ENet inverse-log class weighting (`cls_pw=0.5`), EMA weights for eval, and the same evaluation protocol: single-scale, no test-time augmentation. What is not matched:

| Axis | CABiNet | YOLO26-sem | Potential advantage |
| --- | --- | --- | --- |
| Initialization | ImageNet-pretrained MobileNetV3 backbone; seg layers random | full net pretrained on Cityscapes + ADE20K | potentially favors YOLO |
| Epoch budget | 5000 (early stop, patience 100) | 500 (early stop, patience 50) | potentially favors CABiNet |
| Optimizer / schedule | SGD + poly decay, decoder LR ×10 | SGD + cosine | different |
| Loss | OHEM-CE + aux deep supervision | CE + Dice + aux | different |
| Extra augmentation | none | mosaic 0.8, copy-paste 0.15 | potentially favors YOLO |

So this is not an architecture-only ablation. It's a controlled benchmark: the data representation, class weighting and evaluation are standardized, while each model keeps a model-specific training recipe. None of the rows above is an isolated experiment, so I haven't measured how much any single one is worth.

Results — UAVid test split, 1024×1024, single-scale

| Model | mIoU (%) | Params (M) | FLOPs (G) | FP16 latency* | FP16 FPS |
| --- | --- | --- | --- | --- | --- |
| 
**CABiNet (MobileNetV3-L)**
 | 
**67.14**
 | 9.17 | 54.8 | 4.44 ms | 225 |
| 
**CABiNet (MobileNetV3-S)**
 | 65.25 | 5.36 | 44.1 | 3.09 ms | 324 |
| YOLO26x-sem | 64.41 | 40.16 | 430.9 | 13.09 ms | 76 |
| YOLO26l-sem | 63.28 | 17.87 | 192.4 | 7.54 ms | 133 |
| YOLO26m-sem | 61.98 | 14.32 | 152.3 | 5.71 ms | 175 |
| YOLO26s-sem | 61.69 | 6.50 | 44.4 | 2.52 ms | 396 |
| YOLO26n-sem | 58.17 | 1.63 | 11.4 | 2.23 ms | 449 |


*\*RTX 4070 SUPER, batch 1, pure model forward pass (no pre/post), 200 iters after 30
warmup, measured by me. Params are architecture-only; FLOPs are analytic forward-pass at
1024² (thop for CABiNet, Ultralytics profiler for YOLO26; both report FLOPs = 2×MACs).*

The dashed line is the accuracy/latency Pareto frontier: YOLO26n and YOLO26s sit on it as legitimate lower-latency points, while YOLO26m/l/x are dominated, each being both slower and less accurate than at least one CABiNet variant. CABiNet occupies the higher-accuracy end of the frontier.

Three things worth pulling out:

1. Near-iso-compute: CABiNet-S vs YOLO26s. ~44 GFLOPs each (44.1 vs 44.4), CABiNet-S has slightly fewer params (5.36M vs 6.50M), and they're within 0.6 ms on this GPU, yet CABiNet-S is +3.6 mIoU (65.25 vs 61.69). YOLO26s is still the faster model, so this is a clean accuracy/latency trade, not a universal win.
2. Higher-accuracy end: CABiNet-L vs YOLO26x. CABiNet-L is +2.7 mIoU and ~3× lower forward latency (4.44 vs 13.09 ms). It's not that CABiNet is the fastest model (YOLO26n/s are faster); it's that it reaches higher accuracy without moving into the latency/compute regime of YOLO26m/l/x.
3. Not universally better. On VDD and AeroScapes (same matched eval), YOLO26 s-and-up pull ahead of CABiNet-Large, which lands mid-pack there. Numbers and configs in the repo.

MobileNetV3's depthwise convs are FLOP-cheap but not GPU-latency-cheap, which is why the frontier looks the way it does. The story is accuracy per millisecond at the higher-accuracy end, not "smallest and fastest."

Qualitative — CABiNet-L vs YOLO26x-sem

Where the +2.7 mIoU comes from. Per-class IoU on the UAVid test split, matched single-scale:

| Class | CABiNet-L | YOLO26x-sem | Δ |
| --- | --- | --- | --- |
| Human | 28.3 | 21.1 | **+7.2** |
| Static Car | 57.2 | 51.3 | **+5.9** |
| Moving Car | 71.9 | 66.8 | **+5.1** |
| Tree | 80.3 | 78.2 | +2.1 |
| Vegetation | 64.1 | 63.3 | +0.8 |
| Road | 80.3 | 79.8 | +0.5 |
| Clutter | 67.8 | 67.3 | +0.5 |
| Building | 87.1 | 87.4 | −0.2 |

The gap is almost entirely the small / thin classes: people and vehicles. On the big region classes the two are within half a point, and YOLO26x is marginally ahead on Building.

Two UAVid test frames, both single-scale; columns are input · YOLO26x-sem · CABiNet-L · ground truth. Row 2 shows a failure mode behind the Static-Car number: YOLO26x collapses the parking-lot structure into one Static-Car/Clutter mass and bleeds Building into the lot, while CABiNet-L tracks the ground truth more closely. These two frames were chosen to illustrate the per-class differences above, not as a representative random sample.

Scope / limitations

• UAVid only (see point 3 above). The VDD / AeroScapes numbers and configs are in the repo; I'm leading with UAVid because that's where the result is clean, not hiding the rest.
• Single training run per config: no seed sweep, no variance estimate. The observed ~2.7 mIoU CABiNet-L vs YOLO26x gap is large relative to the smaller differences in this table, but I haven't established statistical significance. I wouldn't over-read anything under ~1 point.
• Latency is a clean-room forward pass on one consumer GPU. No TensorRT/ONNX, no Jetson, no full-frame sliding-window cost (UAVid source frames are 4K; CABiNet tiles, YOLO resizes, so end-to-end numbers would differ). Read these as model-level GPU measurements, not deployment throughput.
• The initialization is asymmetric: YOLO26-sem starts from Cityscapes + ADE20K pretraining, CABiNet only from an ImageNet-pretrained backbone. This likely gives YOLO26 a transfer learning advantage on aerial data, though its magnitude isn't measured here. CABiNet reaching higher UAVid accuracy from the less domain-specific start is part of what makes the result interesting, but it stays a confound.

Open-sourced

• Weights (CABiNet-L/S + all YOLO26-sem sizes) on Hugging Face, with model cards + metrics
• Full Hydra configs, converters and eval scripts; every row above is reproducible
• Live demo (UAVid): run any checkpoint on your own aerial image at https://huggingface.co/spaces/dronefreak/uavid-aerial-segmentation
  

Links

• Code + leaderboards: https://github.com/dronefreak/CABiNet
• Demo: https://huggingface.co/spaces/dronefreak/uavid-aerial-segmentation
• Paper: CABiNet, ICRA 2021 (DOI 10.1109/ICRA48506.2021.9560977)

The criticism I'd most like: is standardizing the data representation, class weighting and evaluation, while letting each model keep its native training recipe, a useful way to compare architectures from different lineages? If not, what would you standardize or change instead?

We needed to know where the open-source AI-detection field actually stands, so we ran every notable open detector through the same protocol.

Setup:

- Public data only: Jabarian & Imas 2025 (NBER), Liang 2023 TOEFL essays, a 1,060-text frontier set (GPT-5.x, Claude Opus 5, Gemini 3.x), 5,000 pre-LLM (2018) FineWeb pages as human pool

- Every model gets its threshold set on the same 6,930 human docs to a matched 0.5% FPR

- Then measure recall per group: raw AI, humanizer-paraphrased AI, frontier models

What surprised us:

- 4 of 6 models effectively can't reach 0.5% FPR. MAGE scores >0.9999 on 26% of ordinary human web text. The old OpenAI RoBERTa detector lands at AUC 0.31 - worse than coin flip on modern generators.

- Humanizer-paraphrased text is where everything collapses: best model catches 42%, second best 4%.

- All models flag non-native essays at a higher rate than they flag native essays. This is a fundamental flaw in the entire class of models, not just one.

More information and data on HF

\recall at matched 0.5% FPR; * MAGE can't reach 0.5% FPR at any threshold (flags 26% of human web text with score >0.9999)**

Disclosure: one of the six is ours - we run a hosted detector and released this one as open weights (Apache-2.0). All datasets and the methodology are in the model card, so you can rerun everything: huggingface.co/wasitaigeneratedcom/ai-text-detector-small

Hi all,
I recently read this paper:
Julien Guinot, Alain Riou, Elio Quinton, Gyorgy Fazekas. Steering dense music retrieval with open-vocabulary concept discovery.https://arxiv.org/abs/2608.08757

There is multiple model where you can get embedding from Song and Text so that you can search song by text. The challenges is when you get a result that don’t really respect your query, for example you search:

- POP viola with female vocalist

And as a result you usually get POP song with female vocalist because they are usually very common in you library, instead the Viola tend to disappear being uncommon. But the power of MIR isn’t exactly search to specific song even if uncommon ?

Here the idea of the paper is to get the compressed embbeding layer, make it sparse again and be able to identify for some common words exactly which neuron are activated try to avoiding overlapping with other neuron. Then you can improve the value of let’s say the Viola neurons and then bring back to the compressed embbeding space.

This is not mine, is only explanation, what I did is in the past a Distilled version of LAION CLAP called DCLAP that is around 7 million parameters and can efficiently run on CPU:
- https://github.com/NeptuneHub/AudioMuse-AI-DCLAP

The following the paper I also trained a SAE for DCLAP:
- https://github.com/NeptuneHub/AudioMuse-AI-SAE

So if you’re interested you can take a look at let me know. Are all free and opensource.

Finally if you want to see both of them in action I used both the model for AudioMuse-AI, a software dedicated to sonic analysis and automatic playlist creation (with various model):
- https://github.com/NeptuneHub/AudioMuse-AI

If you have any feedback, suggestion, idea, please let me know !

Hi r/MachineLearning,

Stjepan from Manning here, posting with the mods’ permission.

We’ve recently released GPU Programming with Triton by Harshwardhan Fartale in early access. It’s a practical guide to speeding up machine learning training and inference by writing custom GPU kernels in Python with Triton.

The book explains how to identify operations worth optimizing, build and benchmark kernels, fuse operations to reduce memory traffic, implement common parallel and reduction patterns, and improve performance through tiling, vectorization, and better memory access. The goal is to help ML practitioners move beyond framework-level optimization when a model has a stubborn bottleneck.

I’d love to hear from the community:

Which part of your ML workload would you most like to accelerate with a custom kernel—and what currently stops you from writing one?

Real use cases, benchmarks, failed experiments, questions, and skeptical takes are all welcome. We’ll give a free ebook to the five comments that contribute the most to the discussion. The giveaway will remain open for 48 hours, after which we’ll announce the winners here.

Book: https://hubs.la/Q04w2PtF0

50% off with code for the community: MLFARTALE50RE

Full disclosure: I’m posting on behalf of Manning. Honest criticism is just as welcome as enthusiasm.

Thank you for having us.

Cheers,

Stjepan

YOLO26 ships a depth-estimation model — dense, full-resolution, per-pixel regression, a task architecturally much closer to image restoration than to detection. I wanted to know whether the backbone+neck weights it learns through depth training transfer to a different dense-regression task (deraining), compared with training the same architecture from scratch. The deraining model that came out of it is a useful byproduct, but the transfer result is the part I think is worth discussing.

What I inherited from YOLO26-depth

• The CSPDarknet backbone and PAN-FPN neck, unchanged.
• The depth decoder's multi-scale fusion (project the P3/P4/P5 pyramid to a common width, progressively upsample-and-add P5→P4→P3). That part isn't depth-specific — it's just feature fusion — so RGBHead reuses it.

What I changed / added

• Replaced the 1-channel Depth head with a new RGBHead. The config change is one line; RGBHead itself is a new restoration decoder, not a re-pointed depth head.
• Reconstruction tail that continues to full input resolution (deraining needs pixel-exact output; depth stops at 1/4 res).
• Skip connections from the stride-2 and stride-4 backbone layers into the tail, so fine detail has a path that doesn't route through an 8×-downsampled bottleneck.
• Residual output — the head predicts a correction added to the input (NAFNet/Restormer-style), not the image directly.
• LayerNorm in the head's own conv blocks; the backbone and neck stay on BatchNorm (folds into conv at TensorRT export, and keeps the model loadable from the whole YOLO26 pretrained zoo, not just the depth checkpoint).

How it was trained and measured

ClearView as an external lib — its mixed synthetic+real rain recipe, Charbonnier loss, and 10-test-set protocol — so the numbers land on ClearView's own model-zoo scale, not a benchmark I made up. Released scales: nano (5.25M) and small (12.13M).

Loading the YOLO26-depth checkpoint into this architecture matches 468/468 backbone+neck tensors exactly — only the new RGBHead is randomly initialized. So the controlled comparison is: identical architecture and recipe, backbone+neck either from the depth checkpoint or from scratch.

The transfer result (the interesting bit)

A controlled initialization experiment at nano scale — same architecture, same recipe, fixed 100 epochs each — backbone+neck from the YOLO26-depth checkpoint vs. random init:

Deltas are from the unrounded averages (27.935 vs 27.452 PSNR). Small, but the depth init wins on every one of the 10 test sets.

(These are 10-set averages, AllWeather included, from the 100-epoch controlled run — so they're lower and not directly comparable to the 9-rain-only released-model numbers in the ranking table below, which come from longer training.)

On "did the random model just need longer to converge?" — both conditions ran a fixed 100 epochs, and the gap isn't a convergence-speed artifact: a 1-epoch check was a statistical wash, by 20 epochs the gap was already ~+0.49 dB, and at 100 epochs it was +0.48. It appeared early and didn't close with more training.

This does not establish why — whether depth supervision teaches geometry/spatial structure that's useful for restoration, or whether YOLO26-depth just happens to be a strong pretrained checkpoint. Only that, in this setup, the depth-initialized representation is a better starting point than random. Per-dataset deltas are in the repo; happy to paste them in a comment.

Accuracy (avg PSNR over 9 rain-only test sets, ClearView's ranking convention):

ClearView's own analysis points to the classification stem's early downsampling (a stride-4 entry before any residual block runs) as a likely reason the ResNet-UNet baselines underperform. This project doesn't test that directly — the ResNet-UNet comparison is a whole-architecture comparison, not a pretraining ablation — but it's the context the depth-vs-random experiment sits in.

Note NAFNet-Small (1.1M, 31.15 dB): smaller and higher PSNR than yolo26_rgb_n, but ~4× slower (26.9 qps). So this isn't Pareto-dominant on every axis — the story is specifically the real-time / YOLO-derived operating point, not "more efficient in every sense."

Deployment (TensorRT fp16, 1920×1080, batch 1, RTX 4070 SUPER 12GB; baseline figures are ClearView's own on the same GPU/TRT version)

The clean pairwise comparisons against the ResNet-UNet family:

• yolo26_rgb_s — 12.13M, 30.95 dB, 92.2 qps vs ResNet34-UNet — 24.5M, 30.45 dB, 94.9 qps → same speed, ~half the params, +0.5 dB
• yolo26_rgb_n — 5.25M, 30.83 dB, 108.6 qps vs ResNet18-UNet — 14.4M, 30.23 dB, 110.3 qps → same throughput, ~1/3 the params, +0.6 dB
• Both are ~3× faster than ResNet50-UNet (73.3M, 30.63 dB, 33.1 qps), while also scoring higher PSNR.
• Restormer (rank 1 on PSNR) doesn't build under TensorRT at 1080p on this 12GB card in my setup — TensorRT reports ~14.4GB of scratch needed to fuse its attention path.

What this shows — and what it doesn't

• Demonstrated: YOLO26-depth initialization beats random init for deraining in this setup (10/10 test sets, +0.48 dB), same architecture and recipe.
• Supported: the trained models sit at an attractive real-time quality/latency point relative to the ResNet-UNet baselines.
• Not demonstrated: that depth pretraining beats classification pretraining for restoration, or why depth helps. Those need experiments I haven't run.

Practical limitations: deraining is partial (faint streaks survive up close; dense rain over flat, low-texture backgrounds is the worst case). AllWeather (rain+fog) is out of domain — both YOLO26-RGB models and every ClearView baseline land around 13.5 dB, so it's excluded from the ranking. One task, two scales — not a general restoration model.

Solo side project. AGPL-3.0 (inherited from Ultralytics' YOLO26 license). Not affiliated with Ultralytics.

• Code: https://github.com/dronefreak/yolo26-rgb
• Models: https://huggingface.co/dronefreak/yolo26-rgb-n · https://huggingface.co/dronefreak/yolo26-rgb-s

Happy to answer questions on the architecture or the eval setup.

After following various arXiv papers and researcher discussions on X/bluesky about latent reasoning and continual learning, one idea which resonates strongly is that path forward (towards AGI) may depend less on generating ever-longer chains of thought and more on finding architectures that can reason beyond the token stream.

LLMs routinely reach correct answers through flawed or fabricated CoT steps, and produce perfectly logical steps that end in wrong answers (Kambhampati, 2025). The trace doesn't track the computation which clarifies that verbalized CoT is an imitation of reasoning and not the mechanism itself.

The alternative mechanism which gets the most attention is latent reasoning: instead of verbalizing every intermediate result, the model repeatedly transforms its continuous hidden state and decodes only the answer. 

I’m breaking latent reasoning down into at least five distinct families:

1. Continuous thoughts in autoregressive LMs: Coconut (Hao et al., 2024) feeds the model's own final hidden state back in as the next input embedding while Soft Thinking (Zhang et al., 2025) reasons in a continuous concept space. Theory here argues a single continuous state can hold several search frontiers at once and expand them in parallel (Zhu et al., 2025) .
2. Compressed discrete non-linguistic tokens: Abstract-CoT (Ramji et al., 2026) swaps verbal rationales for a short sequence from a learned vocabulary. It is non-linguistic, but still serial and externally decoded, the discrete end of the spectrum .
3. Recurrent depth and looped models: recurrent-depth LMs (Geiping et al., 2025) and looped Transformers (Saunshi et al., 2025; Zhu et al., 2026) reapply a shared block to a latent state. Mostly framed as parameter efficiency and test-time-compute scaling, not as a new reasoning interface .
4. Task-trained recursive solvers: HRM (Wang et al., 2025) and TRM (Jolicoeur-Martineau, 2025) recursively refine latent and candidate-answer states. Their ARC pipelines are transductive: evaluation-task demonstrations get augmented into optimization with learned per-puzzle identities, so an unseen task needs a backward pass before it can be answered .
5. In-context recurrent latent solvers: this is where BDH-CQ (Engdahl et al., 2026) sits, built on the Dragon hatchling architecture (Kosowski et al., 2025). Demonstrations write directly into a recurrent memory at inference time, and  new test inputs are then solved by iterative computation in a separate continuous latent space. The authors report a point beyond the previously published cost–accuracy Pareto frontier on public ARC-AGI-1, as well as early pretraining experiments show transformer-like scaling laws upto 600B parameters while preserving the latent reasoning behavior.

Two distinctions seem especially important: how a system acquires a new task (through context, memory, or gradient-based optimization or finetuning) and where its intermediate computation happens (through language tokens, abstract tokens, or continuous latent states)

Lmk if I have missed any family or papers. More importantly, if latent reasoning wins on efficiency, what happens to the readable traces on which much of industry's interpretability and evaluation work currently depends? Was CoT legibility a temporary consequence of how we scaled LLMs or is it a safety property worth paying an efficiency penalty to keep?

Hi everyone,

2nd-year PhD candidate here staring down my first A* submission deadline (AAMAS 2027). I could really use some perspective on theory expectations, especially since I think I’ve methodologically painted myself into a corner.

The setup

My project started with a clean hypothesis: if architecture X is more robust than Y to perturbation A, and B is a strictly harder version of A, then the X > Y ordering should hold under B as well. I isolated three variables I suspected were driving the effect, ran experiments, and… got results that only partially support the hypothesis, with clear boundary conditions.

Where I got stuck

Trying to explain the “why” mathematically sent me down a theory rabbit hole. I ended up with two bad options:

1. Claims tied to specific training outputs rather than structural/architectural properties, or
2. Weak, hand-wavy speculations that feel like post-hoc rationalizations.

I’m pretty sure I fell into HARKing.. I started building theory after seeing the results instead of deriving predictions beforehand.

Furthermore, my codebase is built on an undocumented public repo, and I recently found a bunch of hidden parameters set to wrong values for my setting. I’m currently re-running everything, which is why I’m being vague about specifics. My “insights” from the first round are probably garbage.

My actual questions

• For those who’ve reviewed for or published at AAMAS (or similar A* venues): how much formal theory is actually expected for an empirical MARL paper? Is “here’s the phenomenon, here’s the controlled experiments, here’s a plausible but incomplete theoretical sketch” a death sentence?
• If the theory ends up being training-dependent rather than structural, is that a sign I should pivot to a lower-tier venue, or can strong empirical characterization + limited theory still fly at A*?
• How do you recover from HARKing mid-project when you’re under pressure to publish in year 3/4 of a 4-year contract?

Any advice on how to salvage the timeline or reframe the narrative would be hugely appreciated.

I'm exploring Hidden Markov Models (HMMs) as a baseline method for "dataset exploration/discovery" where I have a bunch of unstructured data with no annotations, and wish to gain insights about the structure and semantics of the data within. I was wondering if there are more modern (deep learning based or otherwise) approaches which have completely superseded HMMs for such tasks.

Hey everyone,

My brother and I just released TontaubeV1, a 2.9B-parameter open-weight TTS model focused on expressive speech, long-form generation/narration, and low-latency local inference. It is primarily aimed at English and German and supports zero-shot voice cloning from up to one minute of reference audio. It builds on DualCodec, a multi-codebook discrete audio codec. It was trained on 7 languages and ~200k hours of audio (mostly tested in English and German).

I wanted to make a post to highlight two choices that worked well for us and seem less common in current TTS models:

1. Character-level tokenization

We start from a Qwen3-1.7B checkpoint for our semantic codebook model. Many modern, and especially LLM-based, TTS models use the tokenizer from the backbone model, add special/audio tokens, and train the model on predicting the next token. We experimented early on with character-level tokenization and found that it generally worked better than using the original BPE tokenizer from Qwen.

We still use the tokens emitted by the Qwen tokenizer, but force it to tokenize spoken text as a sequence of individual characters. When experimenting with forcing Qwen to predict text in this mode, we found that it was still able to answer questions correctly, which suggested to us that language understanding was retained even with this unusual representation.

We did this because a) context length usually is not as much of an issue for TTS as it is for regular LLMs, since we do not require huge reasoning budgets and use chunking, and b) it makes the character-to-sound mapping much simpler internally. Speech is a lot about syllables and short character sequences.

When using the regular BPE tokenizer, we found that the model went out of distribution more often and was more likely to encounter a sequence of tokens that was rare or absent from the TTS training data. Complex sequences of special characters can be particularly confusing because they may tokenize into combinations that the TTS model has barely seen. This is amplified by the fact that TTS training covers far fewer text-token combinations than the full pretraining of an LLM.

2. Chunking and position scheme

The important part here is not just that we split long text into chunks. The chunk boundaries are part of the token layout and position scheme used during training.

The model processes several rows in one flat sequence: text, semantic audio, and the completed lower acoustic codebooks. If we simply used normal sequential position IDs, tokens representing the same moment in the audio would end up far apart because the rows are serialized one after another. Instead, the physical sequence order determines which tokens the model can see, while we assign separate logical position IDs. Codec tokens for the same audio frame share a position across codebooks, and text and audio are placed on the same approximate timeline.

Text advances by one position per character, while audio advances at 12.5 frames per second. These rates are fairly close for normal speech, but not identical, so the two streams usually need slight realignment after every chunk. We use paired text and audio split markers that share the same position. We also reserve an additional 25 character positions at each boundary. This prevents the generated audio positions from leaking into the next chunk’s text positions, while keeping the position IDs monotonic and approximately linear across the passage.

For every chunk, the semantic model sees the previous text and audio chunk, the current text, and a short lookahead into the next text. Once a chunk is finished, the oldest text-audio pair is discarded and the window moves forward. This keeps the model context bounded even for very long passages, while still retaining nearby text and audio context. The higher acoustic codebook models work on one chunk at a time and do not carry acoustic state between chunks.

DualCodec’s decoder is forward-looking, which makes directly decoding and joining separate chunks problematic. For streaming, we therefore decode overlapping DualCodec windows, re-encode them into the VibeVoice acoustic space, keep the stable middle sections, and decode them with a shared causal VibeVoice decoder state. This reduces audible seams between chunks and lets us emit audio before the full passage has been generated.

Here is figure 1 from our technical report:

The current release requires a GPU with at least 24 GB of VRAM for the low-VRAM and balanced profiles, or 32 GB for the high-throughput profile. A substantial part of the current VRAM requirement comes from vLLM’s KV-cache reservation and our multi-engine serving setup, which are designed for high concurrency and low latency. We plan to release quantized versions aimed at much smaller memory capacities and on-device use, as well as fine-tuning support.

We also ran a 400-passage LLM-as-a-judge audiobook benchmark. On prosody, TontaubeV1 scored 50.1% against ElevenLabs Flash v2.5 and was preferred over Fish Audio S2 Pro, Gradium, and Cartesia Sonic 3. The methodology, caveats, and confidence intervals are described in the report.

Human listening tests remain the gold standard, so take these results with a grain of salt. We were not able to conduct a large-scale human study before release, but we plan to submit TontaubeV1 to TTS Arena V2 and the Artificial Analysis Text to Speech Arena.

Links:

- HF model page: https://huggingface.co/TontaubeAI/TontaubeV1

- HF demo: https://huggingface.co/spaces/TontaubeAI/tontaube-v1-tts-demo

- Inference code: https://github.com/craitech/tontaube

- Technical report: https://tontaube.ai/papers/tontaube-v1-technical-report.pdf

Let me know if you have any questions!

LLM agents increasingly modify their own prompts, tools, middleware, resources, and execution harnesses at runtime. Such self-evolution can improve capability, but a successful mutation may leave persistent effects that cannot be safely reversed in states different from the one in which it was created.

We introduce EvoUndo, a framework for representing, synthesizing, diagnosing, and independently verifying recoverability of model-generated self-modifications across counterfactual states. Across 600 unseen one-shot self-evolution tasks, we identify 197 capability-improving mutations that fail recoverability verification. Under the original recovery representation, conventional repair strategies recover 0/197 of these natural failures. Deterministic oracle analysis recovers 48/197 under the original recovery language L0, while the extended recovery calculus increases empirical oracle recovery to 191/197.

A protocol-locked 2×2 grounding-by-expressivity intervention then separates two bottlenecks: exact state-address grounding increases successful recovery from 0/48 to 38/48 (79.2%) when the original language is sufficient, while extending the recovery language enables recovery on 142/143 (99.3%) failures in the oracle-defined S1 stratum.

On the primary gpt-oss-120b backbone, adding exact-address diagnostics to the richer language reduces recovery to 133/143 (93.0%); a Qwen3.8-27B replication preserves the grounding and expressivity effects but not this negative interaction, indicating that the latter is model-dependent.

These results indicate that reliable agent self-evolution requires co-designing verification, state grounding, witness semantics, and recovery-language expressivity rather than relying on iterative prompting alone.

Paper: https://arxiv.org/abs/2608.28363

This is the time of year when the number of cold emails I receive about PhD positions tends to ramp up quite a bit. In many countries, this cold emailing is essentially part of the normal recruitment process, so there is nothing inherently wrong with doing this. However, there are a few things you definitely shouldn't be doing:

• Massive emails. The probability of me reading your email is inversely proportional to its length.
• Emailing everyone. Find supervisors that work in areas you are actually interested in. I do relatively foundational ML research (i.e., not associated with a specific application domain), but the majority of emails I get from prospective students are essentially "I want to apply ML to domain X". In many cases this does not constitute an ML research direction; you'd be better off finding a supervisor with expertise in domain X, which is where most of the impact will be.
• Generic research interests. If the most specific research interests you can give are "Machine Learning, LLMs, and AI" then I assume you only have a surface-level familiarity with the field, and are not ready for a PhD.
• Passing off workshop papers as conference papers. This has become a much more common thing in the last couple of years. It's a big red flag; I am not going to take on someone who is dishonest.
• Excessive AI use. Using them for fixing up grammar is fine, but if you outsource your thinking to LLMs then your research direction will be the same as everyone else who outsources their thinking to LLMs. This tends to result in something that would be an okay bachelor's thesis project, but nothing more than that. I get a lot of LLM emails, so determining if you are in this cluster is very easy.
• Summarise my paper. I already know what's in it, I don't need a summary. I care more about how you think you could build on it, or do something related. Don't use LLMs for this; see above point.
• Ignoring instructions on my website. Check prospective supervisors' websites for how you should be getting in contact with them. Often they will ask you to include something in the subject line to make sure your email goes to the right place. Ignoring this will send you straight to spam.

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

Thread will stay alive until next one so keep posting after the date in the title.

Thanks to everyone for answering questions in the previous thread!

Sliding Window Attention with sinks, one of the simplest existing fixes for the quadratic-cost problem in LLMs, holds up as well or better than the linear-attention variants labs have been spending post-training compute to produce. That is the claim of a [new arXiv preprint](https://arxiv.org/abs/2608.28444) by Alexia Jolicoeur-Martineau, Rhea Sanjay Sukthanker, Pashmina Cameron and Emy Gervais.

On the long-context reasoning benchmarks the paper singles out, the gap is not close. "SWA achieves massively higher performance (2 to 10 times higher than linear attention)," the abstract reports, naming Needle-in-a-Haystack and BABILong as the two tasks.

The pitch is that the whole post-training-to-linear pipeline has been benchmarked against the wrong thing. "This line of research has not been properly compared to simpler baselines," the authors write. Their alternative needs no post-training, runs fast, and holds memory low.

The recommendation is blunt: "we strongly recommend switching to SWA instead of post-training linear models." Linear attention, the abstract concedes, "may have shown some promise, but they likely require to be trained from scratch or extensive post-training in order to even match SWA."

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