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I trained a small transformer to fly a boids flock just by watching, then checked where it keeps the three rules
▲ 93 r/MachineLearning+1 crossposts

I trained a small transformer to fly a boids flock just by watching, then checked where it keeps the three rules

i wrote a small boid simulator (12 birds), recorded it flying then saved it, and trained a transformer to predict each bird's next move without it knowing about any boid rules.

(built in Python and PyTorch: a transformer written from scratch, one token per bird, two attention layers, about 414k weights, trained on a laptop CPU)

what I found:

  • it flies the flock. R² 0.990 to 0.994 on clips it never trained on, across 4 full runs. It also flies 50 birds (it has only trained on 12).

  • the three rules were easy to read out of its hidden states with a linear probe, and some could even be read before any training.

  • the rule: alignment (line up with your neighbours) was readable after the first attention layer, but if I make the second layer hear only itself, it is gone across all runs.

what i wasn't expecting: my first ever model was not using the flock at all. With 8 ticks of history, the previous answer was already in the input, and a small network that saw only one bird beat the whole transformer.

what i am still not sure about: making a layer attend only itself is something the model never went through during training. is that a fair test, or is some of the decay due to strange input fed to the model?

site: https://kreptiliri.github.io/murmuration

code: https://github.com/kreptiliri/murmuration

u/rage_81 — 4 days ago
▲ 43 r/MachineLearning

Has machine learning research gotten more "wordy"? [D]

I feel like I am unable to digest most machine learning research these days. The primary reason is because I feel like they have gotten tremendously wordy. I find it not uncommon to find research papers consisting of 20+, 30+, 40+ pages of pure texts. An example is https://arxiv.org/pdf/2302.13971 I understand this is in the context of LLM but I've also noticed this trend outside of LLM, even in things like optimization.

Also because the papers seem to have gotten wordy, it is possible to come across some undefined (suspected) new terminology due to word usage. An example (and not a research paper per-se) is https://transformer-circuits.pub/2021/framework/index.html Ok, if you just scroll down you do find that blog is interspersed with mathematical formulas, but the writing just feels off. The authors mixes colloquial conversational structure into a scientific publication "a lot of ...", "we don't feel....", "it feels like...". This makes thing unreadable in my honest opinion.

At the same time, I feel mathematical explanation are becoming rarer in some research areas. There seems to be an over-reliance on "block diagrams", except diagrams misses a lot of details and virtually impossible to translate into math/code without a bunch of guess works.

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u/NeighborhoodFatCat — 4 days ago
▲ 77 r/MachineLearning

Are .ipynb notebooks already outdated in the agentic era? [D]

I am a data scientist who started working in the industry before the LLM revolution. Back then, Jupyter Notebooks were a perfect fit for the classical DS pipeline: EDA -> data prep -> fit -> eval -> tune -> save model artefact and notebook.

Lately, I have been thinking a lot about how agentic development and LLMs are changing the way data scientists work. Especially in classical ML applications, where you still need to explore data, run experiments, check different hypotheses and decide what to do next based on the results.

I mean, Claude/Codex can already write most of the code for us. But why do we still need to organise the whole workflow around code cells? What if instead we move from code -> output to another cell abstraction like prompt -> result?

Curious what others think. Are ipynbs still good enough, or are we just used to working this way?

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u/Economy_Vacation_504 — 5 days ago
▲ 14 r/MachineLearning

What's up with google scholar citations ? [D]

Early-stage researcher, and yes, I get excited about people citing my work.

I was browsing through arXiv for accepted NeurIPS papers, and I saw multiple papers citing my work. These papers have been there for months, but Google Scholar still has not updated its citations, and it has occurred before, but I ignored it and thought it was a one-time error.

Does it usually happen? How long do you wait before you get your citations? I know it is naive, but still.

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u/ade17_in — 3 days ago
▲ 5 r/MachineLearning

NeurIPS 2026 Paris complimentary registration already full. Did any Top Reviewers get a spot? [D]

Hi everyone,

I was selected as a NeurIPS 2026 Top Reviewer and awarded complimentary registration.

As far as I understand, registration opened in batches: first for SACs, then ACs, and finally reviewers.

However, when I received the email and tried to register, I found that the Paris venue was already full and could no longer be selected.

Has anyone else experienced this? Did any reviewers actually manage to secure a spot in Paris, or were all the available places already taken during the earlier registration rounds for SACs and ACs?

I'm wondering whether I was simply too late or whether Paris was already full by the time registration opened for reviewers.

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u/Independent-Farmer30 — 4 days ago
▲ 0 r/MachineLearning

Is a second MSc worth it? [D]

I’m an AI research engineer based in Africa with about 3 YoEs in RL, LLMs, post-training, and systems efficiency (CUDA/vLLM). However, on a smaller scale because of compute limits. My undergrad and Master’s are both in communications engineering. I did a 5th-year Master's at my local university while working full-time at a telecom company, focusing on RL in telecom. Publication-wise, I have one paper published in a peer-reviewed mid-tier AI/telecom conference, a patent, and another paper that was accepted but never published because we couldn't fund it.

Over the past year, I’ve hit the below walls:

PhD Applications (~10 mid-tier labs): Rejected by all. I read their recent papers, put together proposal ideas, and reached out to PIs, but didn't get traction.

Research Engineer Roles (~100 applications): Applied directly, reached out to engineers in similar roles for resume reviews or referrals, but didn't pass a single initial screening.

Cold Emails for Internships (~400 sent): I read labs' papers, asked targeted questions, and chatted with authors. They’d often be interested in the technical ideas, but as soon as I brought up doing an internship, I almost always got ghosted (got 3 actual replies, all saying no capacity).

I’m starting to think the location and university brand are major bottlenecks, so I'm considering doing a second CS Master’s abroad to get my foot in the door. But I really don't want to spend two years going in the wrong direction if there's a better way.

What other paths or changes would you suggest?

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u/Fit_Caterpillar4434 — 4 days ago
▲ 5 r/MachineLearning

Sliced Wasserstein embeddings + HNSW for color-scheme search over 48k paintings [P]

Palette Atlas (https://itzik123.github.io/PaletteAtlas/) searches public-domain paintings by color scheme. Each image is reduced to 128 colors of equal weight, so the natural distance is the Earth Mover's Distance, which is too slow for nearest-neighbor search over 48,695 items (about 6 ms per pair).

The embedding: project the colors (OKLab) onto 8 directions on a Fibonacci hemisphere, sort, and average into 16 quantiles. L1 between two vectors is the 1D W1 averaged over the directions, a lower bound on the true W1.

Against the exact EMD on 240 queries (overlap with the exact top 10, and how much farther the top 10 lie):

- sliced 8x16, L1: 78%, 1.8%

- sliced 8x16, L2: 69%, 3.4%

- soft color histogram, Hellinger: 45%, 13.5%

- mean color: 3%, 161%

The index is HNSW written from scratch (M=16, efConstruction=200): recall@10 of 1.000 at ef=32, 0.25 ms against 14 ms for brute force. The vectors' intrinsic dimension is about 11, so the hierarchy still helps (see Munyampirwa et al. 2025 on when it doesn't). One change from the paper: the top layer holds 8 k-means landmarks instead of randomly drawn nodes. The search cost is the same (2,558 vs 2,556 distance computations on average), and the top layer becomes one typical painting per color family.

Write-up: https://github.com/itzik123/PaletteAtlas/blob/main/docs/searching-by-color.md

Code: https://github.com/itzik123/PaletteAtlas

u/Potential-Barber8658 — 4 days ago
▲ 386 r/MachineLearning

Nvidia’s erroneous paper accepted as ICML’s spotlight [D]

Here is the story:
Nvidia has published this work (with source code available) called dreamDojo which is a world model for robotics based of their prior work Cosmos 2.5 which is cited about 100 times and got ICML’s spotlight.

Authors are very well known and respected in the field with too many peer reviewed papers already published.

The work doesn’t have much novelty (which I don’t care) but it is yet another foundation model. The gist is that they collected about 44k hours of human data (data are NOT open sourced, which I don’t care) and used that for pre-training of DreamDojo initialized from Cosmos 2.5 .

Table 4 of the paper though shows a very marginal improvement over cosmos 2.5. Believe or not, just about 0.5 db PSNR improvement. Its fishy, is not it? You use 44000 hours of human data and few hundred hours of other kind of data and robot data and 256 H100 gpu and you got just a very marginal improvement.

Anyway I went to give it a try, started post training on their GR1 released data, and could produce their results. Meanwhile a colleague with the help of Claude found a bug in their post training code, which essentially make all the post training code wrong.

Then we checked their github issues, and noticed that two other bugs are reported which effect the whole pre-training! Especially this bug . So essentially based on the code their released everything in pre training phase, post training phase and evaluation is buggy and is wrong.

The code doesn’t seem AI written cause its (very) bad written. And now after seeing all those bugs the results in paper makes sense.

My question is:
WTF!
How it did not ring a bell for the authors. Did not they ask we spend a hell lot of resources and huge amount of data and essentially got nothing?
Did not it ring a bell for reviewers?

Openreview

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u/Amazing-Fox-7295 — 7 days ago
▲ 0 r/MachineLearning

What ai is best to operate a system from screen video [p]

I have working programmatic control over a system, I can send inputs reliably and capture the screen as visual feedback. The control/feedback loop is already done.

I need something that can learn from visual information, remember what it has seen and been taught, and get better at operating the system over time.

recommendations? the AI I have tried so far it's memory is not good enough and it often doesn't consult the visual information before making decisions which is very frustrating.

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u/angrykirby — 4 days ago
▲ 2 r/MachineLearning

Clarification ARR Commitment [D]

I want to commit my ARR paper to EACL 2027 and I see I have to attach a pdf.

Should I address the reviewer comments/suggestions (minor things) in this version already or wait for camera ready if accepted?

I thought I just had to provide the link to the reviews but now I am confused.

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u/Mundane_Slice_8621 — 6 days ago
▲ 38 r/MachineLearning

Talus: a 23M-parameter diffusion model for game terrain, evaluated against a real-vs-real noise floor, running in the browser on WebGPU [P]

Talus generates 64x64 heightmaps (4 km, up to 1,200 m) conditioned on a terrain type and any subset of five measured properties: mean elevation, relief, mean slope, water fraction and spectral slope. I trained it from scratch on one RTX 5060 (8 GB), about 4.5 hours in total across versions. The data is 45,000 maps from my own procedural generator (fBm/ridged noise, stream-power erosion, hillslope diffusion, thermal erosion). Model

Pixel-space U-Net, v-prediction, cosine schedule, 50-step DDIM with quadratic spacing, classifier-free guidance 2.0

Each property has a learned "unknown" embedding and is dropped independently during training, so any subset works at inference

Relative heights: the model generates the map's shape normalized by its relief, and the sampler places it at the requested mean elevation and relief (borrowed from the 16 nearest training maps when unspecified). This fixed grainy plains: the plains distance ratio went from 3.98 to 1.23

Evaluation: Every distance (W1 over 25 per-map terrain metrics, radially averaged power spectrum, height and slope distributions) is divided by the same distance between two disjoint halves of real maps, so 1.0 means indistinguishable at that sample size. Checkpoints are chosen on VAL; TEST is scored once on the pick, and memorization is checked against the training set. Current model on TEST: metric W1 1.51x the floor, spectrum 9.1x, slopes 1.65x.

Browser: ONNX export with fp16-stored weights (cast to fp32 at load), ONNX Runtime Web on WebGPU, about 3 s per map on my RTX 5060, CPU fallback. A JavaScript reimplementation of the sampler matches PyTorch to within 0.6 m on reference samples. Demo: https://talus.tersa.tech Code, weights, scorecard (Apache-2.0): https://github.com/osfv/talus

Open problems: ridges and the finest spectral band, mountains too smooth and plains too grainy. If you've closed a spectrum gap like this in a small diffusion model, I'd like to hear what worked.

u/Old_Cow_6636 — 5 days ago
▲ 17 r/MachineLearning

I built MaRN: a PyTorch library for training neural networks through low-dimensional parameter mappings [P]

I built MaRN (Mapping Networks), a PyTorch library that lets you optimize a compact latent representation instead of directly training every model parameter.

Some results from my current benchmarks:

  • MNIST CNN: Reduced a 537,748-parameter CNN to 4,080 trainable parameters (131.8× reduction), with accuracy decreasing from 99.07% → 98.10% (−0.97 pp). A smaller 107,998-parameter CNN was reduced to 1,872 trainable parameters (57.7× reduction), with accuracy decreasing from 98.83% → 97.18% (−1.65 pp).

These results come with trade-offs: mapped models can train substantially slower, and performance varies by task. The benchmarks are exploratory, using synthetic data for some tasks, and aren't evidence of general superiority over direct training.

The library includes global and layer-wise mappings, regularization options, and pruning/LRD integrations.

Code: https://github.com/arjunmnath/MaRN
Docs: https://marn.readthedocs.io/

I'd value feedback on the approach, benchmark design, and where this kind of parameter-efficient optimization could be useful.

u/Less_Dream_6331 — 6 days ago
▲ 98 r/MachineLearning+1 crossposts

Uploaded 5.6 billion TikTok videos metadata on Hugging Face, spanning from 2014 to October 2026 [P]

Dataset: https://huggingface.co/datasets/datasocial/tiktok-5.6B-videos

If you want to explore the data without downloading billions of rows, you can query my ClickHouse database directly. It includes:

Creators table - 4.5 billion rows
Videos table - 5.6 billion rows
Sounds table - 633 million rows

Comment below and I’ll DM you the database credentials.

Im self-hosting my database so please don't run heavy queries and crash my server.

u/DataShack — 7 days ago
▲ 20 r/MachineLearning+1 crossposts

I built ALHR: A tree based sparse attention system that achieves sub-quadratic inference while retaining accuracy. [P]

ALHR - Adaptive Learnable Hierarchical Routing, uses static binary trees and learnable functions to minimize the amount of keys to be read.

It does use a dense teacher while phase 1 of training however.

MQAR TEST AT 1024 TOKENS - Average keys read per query by dense - 512 Keys

Average keys read per query by ALHR - 30 keys

Top - 1 accuracy of dense - 94.9%

Top - 1 accuracy of ALHR - 92.1%

KV Compression of dense - 1x(100% read)

KV Compression of ALHR - 35.3x(2.83% read)

Peak VRAM of dense - 57 MB (Scales quadratically)

Peak VRAM of ALHR - 422 MB (scales linearly)

Cache compression of ALHR - 100%

The true log and Kaggle cell used to run it are in the logs folder in the repo

limitations: Full scale tests are still not completed, The training of this model would still be quadratic but the inference would be NlogN (as indicated in the logs in the repo)

Would love your opinions

ALHR Repository: https://github.com/vdev-ctrl/Adaptive-Learnable-Hierarchical-Routing-

u/Alarming-Emotion-894 — 4 days ago
▲ 17 r/MachineLearning

Real-time neural weather restyling for Minecraft on a GTX 1650 (1.4M-param U-Net, distilled from FLUX.2 klein, 30-40 FPS realtime on budget GPU) [P]

  • Teacher: FLUX.2 klein 4B paints ~3k frames (snow / wet / night at 3 strengths).
  • Student: 1.4M-param U-Net with FiLM sliders, 512×288, ~26 ms/frame via ONNX Runtime inside a Fabric mod; the HUD stays untouched.
  • Pixel loss (L1+MSE) gave a washed-out “average” because the teacher places snow/puddles differently per frame. A PatchGAN fine-tune on the same pairs fixed it: fat snow, real-looking reflections.
  • Failure cases: night (teacher painted sunsets), already-snowy biomes (never in the training data).

Happy to share details.

u/BlueCeAnd — 5 days ago
▲ 12 r/MachineLearning

ARR Oct Discussion [D]

Are there actually fewer submissions this cycle or are people waiting for ARR Aug Meta to be out? I saw an 1.8k submission ID yesterday.

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u/StriderKing27 — 6 days ago
▲ 11 r/MachineLearning

Whatever happened to BABA is AI from 2024? [D]

> We test three state-of-the-art multi-modal large language models (GPT-4o, Gemini-1.5-Pro, Gemini-1.5-Flash) and find that they fail dramatically when generalization requires that the rules of the game must be manipulated and combined.

This is the slap to the face in bold.

The catch here is that this statement was written in a paper in 2024 that was brought to ICML conference that year. (the conference was held in Austria)

In OCT 2026, our situation is agentic swarms powered by models in the tera-parameter class. They can ace ARC-AGI-3, FrontierMath tier 4, crack Clay Institute Navier-Stokes theorems, and wander on to the internet to commit felonies.

The smallish key-door puzzles in this paper seem like they could be completed from a person's PC, with a suitable harness through a cloud subscription.

However, if this is not true, then this paper's importance has only compounded since its presentation at the 2024 ICML conference. The authors of this work are several MIT researchers and a person from Virginia Tech. If it is still true that they can have a key-door puzzle cause SOTA LLMs to "fail dramatically", then this should be (shouted from rooftops). At the very least, it should be relayed to Francois Chollet and the ARC Foundation, as a suitable candidate benchmark for ARC-AGI-4.

I am personally leaning heavily on these puzzles being perfectly solvable with an agentic swarm. In any case, whatever happened to BABA-is-You and BABA-is-AI?

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u/moschles — 6 days ago