TL;DR: AI written reviews, and AI-writing in papers

I was going to give this a title "An Obituary of the Review Process", but in the interest of not sounding too defeatist, I will just say "Critical Condition" because of AI writing, both on the author side and the reviewer side.

I am a reviewer for AAAI 2027 and ICLR 2027. Since Phase 1 and Phase 2 was completed, I can now see other reviews. I can bet good money that about half of the reviews in the papers I'm seeing are AI-generated completely, or edited after AI-generation. This is subjective opinion and I cannot prove it but I will try to convince you - at least convince you that it's a problem that needs much much more attention than is being currently given.

I have been reviewing for top conferences (NeurIPS, ICML, AAAI, ICLR) since 2022, in total about 10 times. I have not felt the same way about reviews, or even about papers, ever, except until recently.

Author side: There was only one paper in the 3 ICLR submissions that felt 100% human-written (now if that isn't either - that'll make me very sad). The others had some elements that smell like AI but you can't prove it e.g. excessive use of very descriptive headings with colourful, marketing-like language e.g. a heading called "The theory predicts performance, and empirical evaluations confirm it", or very dense long sentences where you lose track of the thought where instead of already used terms their synonyms are used and then later even more synonyms, and so on.

Reviewer Side, talking about the reviews I suspect: There is very little variety in the language, between the reviews, and within the same review. One review puts a field "questions for authors" in the weaknesses section, which I suspect is because the field name is changed this time around to "specific points for rebuttal". They've put "N/A" in the new field. The paragraph lengths are almost exactly the same. As if an LLM was told to give 5 strengths, 5 weaknesses, 5 questions. There are also signs of extra effort that a human rarely does e.g. instead of just saying "provide peer-reviewed references instead of arXiv", it also adds the arXiv number; or another review which not only puts doi link (this much is fine), but then in an extra piece just writes "(doi.org)" which seems like an attempt by an LLM to give a source. Reviewers are usually pressed for time, they usually try to communicate so the authors understand, not that everything is exact. These reviews are written as if for public consumption. Then there are smart quotes - which one can think are there because maybe Word or something was used to write them; but seen in light of everything else, now these quotes also smell like AI to me. (I used to not see smart quotes this often in earlier years. Word made quotes smart back then as well.) Unicode symbols instead of LaTeX for things such as \tilde{d}, dashes that are not hyphens - in one review even for a minus sign, and so on.

There are at least two AAAI submissions where there are only two reviews and I strongly suspect the other review to be AI-written.

Everything is broken. I am sorry I have no concrete ideas how to fix this, so far. Do you?

Except one maybe, disallow AI usage even for polishing styling and tone? Just completely disallow AI writing. The author feels it's a change in tone and style only but the reader can sometimes very quickly _feel_ it. Before these powerful LLMs we were not judging papers based on language anyway. I know it's an honor system, but I think that's okay. Saying "you can use AI to polish" perhaps also creates an expectation to polish using AI.

I’ve uploaded a minimal, self-contained implementation of manual autograd for a transformer-based classifier in PyTorch. It can help build intuition for what autograd is doing under the hood and is a useful hands-on reference for low-level differentiation in Transformer models, such as writing custom backward passes and tracing how gradients flow through attention blocks.

🐙 GitHub:

https://github.com/ifiaposto/transformer_custom_autograd/tree/main

📓 Colab:

https://colab.research.google.com/drive/1Lt7JDYG44p7YHJ76eRH_8QFOPkkoIwhn

Chain-of-thought transformed AI reasoning, but it is limited to words.

What's the physical-world analogue?

Introducing Ψ-0.6, one model that reasons in *streams of thought*: words, depth, motion, camera pose, and imagined scenes. And it learns when to use which!

• Ψ-0.6 surpasses leading open-weight models on complex spatial reasoning (MindCube, zero-shot).
• On FlowBench, it outperforms other world models at predicting how objects move. Trained on simulated rooms, it transfers to real robot tabletops.
• One model as perceiver, VLM, world model, reasoner, and policy. RL learns which representation to reason through.

Thread (with video): https://x.com/khai_loong_aw/status/2115099953433600011

• Paper: https://arxiv.org/abs/2610.32177
• Website: https://neuroailab.github.io/psi-website/blog/psi-0-6.html
• Code: https://github.com/neuroailab/psi-0.6

Hi guys,

Self-distillation methods, see Self-Distillation Fine Tuning SDFT, usually use an EMA (Exponential Moving Average) teacher to get synced and stable targets. I wanted to ask the community for research papers/ works that consider different types of teachers that also hold these properties (they eventually also improve along with student) but are stable (prevent student from drifting).

any links to repos/ papers would be appreciated.

thanks!

Hi everyone,

I wanted to share an open-source repo collecting 20 exploratory studies focused on Next-Concept Modeling (NCM), mechanistic interpretability (like IOI in GPT-2), and model controls.

Link: https://github.com/Quantix-eng/ncm-experiment-notes

This is an independent project documenting empirical experiments on how transformers process and edit internal features. No performance breakthroughs here—just honest documentation of observations alongside rigorous controls.

The repo covers NCM on structured tasks (like Sudoku), mechanistic interpretability in GPT-2, and toy parallel models. I've included all source code, saved results, and interpretations for every run.

For full transparency, I’ve categorized every outcome, including the mixed and completely negative results. AI tools were also heavily used during the planning and coding phases.

Would love to get your feedback on the setups or interpretation limits.

Hi, just [sharing a project](https://blog.christianperone.com/2026/10/pandora-m1-a-training-monitor-box-64-nodes-x-8-gpus-support/) I started a few months ago in case anyone is interested. The main idea is that you will have a desktop front-panel where you can monitor distributed training across multiple nodes.

Self-distillation methods usually rely on an Exponential Moving Average (EMA) teachers to get synced and stable critics. However, this doesn't scale well for large models since holding both teacher and student (trained) on GPU increases latency, zero3 communication, etc.

We can mitigate this with LoRA finetuning: the backbone is shared & frozen, and we need to maintain two light adapters: one for the student (trained) and one for the teacher (EMA of the student adapter).

Getting this to work cleanly under TRL and ZeRO-3 took some fixes, so I packaged it as a plug-and-play repo. It supports:

-> robust SDFT implementation that runs under ZeRO-3

-> ZeRO-3 + LoRA integration with adapter switching for teacher/student passes

-> Saving and resuming EMA teacher checkpoint

-> working deepspeed configs that fix memory spikes when GPU memory is tight

I tested on Qwen3.5 and ToolAlpaca.

Repo: https://github.com/ifiaposto/sdft_zero3

Feedback/ more examples/ issues welcome!

Hey! We published a set of 6-step LoRAs, one without the upsampling step: https://huggingface.co/PrunaAI/P-Q-Image-2.1

- Generate or edit images in just 6 steps instead of 40. Use 6-steps for preserving inputs when editing and 6-steps-upsampled for improved visual quality and shorter end-to-end generation.
- 6-step distillation provides improved quality with few generation steps. 6-steps-upsampled is the first distillation that removes the need for a separate prompt-upsampling step. This means 41 to 80 seconds saved, depending on your prompt style.
- The LoRA adapters load directly on top of Qwen-Image-2.1, so the pipeline stays unchanged. Few-step distillation cuts the diffusion cost, and the upsampled adapter also removes the prompt-upsampling step before image generation.

A 3×8 array of clock faces is one of the examples in the SenseNova 6.8 Code2Video showcase. Other entries focus on letterforms, including A to Z and FRAME / FORM / FLOW.

The scientific scenes include Earth and spacecraft clocks, molecular diagrams, and a ball-and-spring setup with separate energy displays. These bring repeated objects, labels and spatial relationships into the same collection as the character-animation examples.

https://github.com/OpenSenseNova/SenseNova-6.8-Code2Video

I've been thinking about AI agent safety, and I built a test harness to check my own results. I think it may be useful to people who study models too.

It records where each tool argument came from before the tool executes, measures whether the same input gives the same decision, and tests whether a model takes arguments from someone else's text instead of its user's.

If you modify the harness, it can probably be used for much more.

https://github.com/Jang-woo-AnnaSoft/execution-state-preflight/blob/main/hooks-example/README.md

It's a 50 hour course for beginners in AI/ML (obviously it's fully free). Starts out with linear algebra, derivatives, statistics and then linear regression, neural networks and backpropagation etc...

kinoml.tech

For my master’s thesis I built an accelerator for diffusion-based action generation in robotics policies. Offline, it looks great: the median absolute deviation between the accelerated output and the original model’s output is extremely small, so I concluded it reproduces the model’s behavior faithfully.

When I deployed it on a real robot for a task (picking up a book and placing it on a shelf), it did not work as well as the original model. The offline numbers did not predict the real-world result.

I want to submit this to MLSys. My offline results are strong, but the deployment result is negative. My questions:

1. Is it acceptable to submit with only the offline results, or should I include the failed deployment?
2. If I include it, how do I frame it so it adds to the paper instead of sinking it?
3. Why might a tiny median deviation still lead to task failure? My guesses are tail errors and error compounding in closed loop, but I would like other views.
4. What additional metrics would you report (percentiles, per-step error, closed-loop success rate in simulation)?

Any experience from people who have submitted systems papers with negative real-world results is very welcome.