Knowledge catalogue
Sharing my ML learning repo — NumPy to Transformers, 5 months, daily commits, all notebooks public. Covers the full stack: - Classical ML (scikit-learn, XGBoost) - Deep Learning (TensorFlow/Keras — AN
I have this idea I'd like feedback on. most camera footage for training, is sunny daytime, because that's what cameras record most of the time. The edge cases models actually struggles with, like nigh
Hi there, I'm Yuesong, a researcher at Video Rebirth. We open-sourced HyperFlow, an 8-step LoRA for MiniMax-H3. The core idea: data-free flow self-distillation. Diffusers' default sampler does 49 mode
Hi everyone, The NeurIPS results aren’t out yet, but I’m expecting my paper to be rejected, so I’m considering submitting it to a journal instead. My scores were 2/3/3 (3/4/4). I feel like I would pro
Hi r/MachineLearning! I'm James Gung, a principal applied scientist at AWS. I joined Amazon in 2021 and have since worked on AI services like Lex, Bedrock, Q Business, and Amazon Quick (an AI assistan
I'm trying to find out whether back-and-forth between two different LLMs improves task success beyond strong alternatives under a controlled resource budget — and I'd rather hear that it's already set
I'm at a crossroads with two grad school options that would take me in somewhat different research directions, and I wanted some general advice on these fields, their growth, and industry alignment. I
GoBench evaluates LLMs on 9x9 Go games against a ladder of KataGo opponents, from random to superhuman. It measures general reasoning ability, strongly correlates with ARC-AGI 2 (r=0.83 correlation),
GitHub: https://github.com/pfekin/LARA I've been working on LARA (Lightweight Additive Residual Adaptation), a research project on making post-training modular for frozen language models. I've also de
Three weeks back , i posted SHADOW-250M here. It got 360 upvotes, 293 on r/LocalLLaMA and 94 GitHub stars. Thank you. That model was 60 MB, ran around 400 tok/s on CPU and could retrieve records from
Suppose I create two machine learning models suppose tree and neural network for a task let's suppose regression problem, now suppose I am sending both of this paper to two different journals, now the
Hi, For single-GPU training, I’m using Hugging Face SFTTrainer with auto_find_batch_size=True, which automatically reduces the batch size after a CUDA OOM until it finds a batch size that works. I wou
Hi everyone, Inspired by tools like Chessvision.ai, I wanted to take a different architectural approach and build a browser extension (ChessInsights AI) that performs chessboard detection and piece re
I have created a VAE model using PyTorch on White Wine dataset. Basically, the main goal is to discover a brand-new white wine recipe. It puts all the wines into a latent space, finds the best part wh
I'm starting a PhD where I will be doing Graph ML (somewhere along the lines of graph signal processing/ graph deep learning.) My eventual goal is research scientist at big tech, or whichever company
A new paper (I'm not a coauthor BTW -- I just found it interesting) argues, basically, that RSI1 is not on the horizon2, because current (at the time the study was done) agents cannot do open-ended ML
Hi 👋 long time lurker It’s been a few years since the last post about Horse Racing within this sub. A few years ago I was reading a popular science book and came across the story Bill Benter and the s
EDIT: Thanks from the Waymo AI Team! A huge thank you to the r/MachineLearning community for all the thoughtful, deep-dive questions today. If you want to dive deeper into our technical research, chec
Sept 9, 2026 hits an all time daily high of 447 new machine learning papers uploaded to cs.LG (https://arxiv.org/list/cs.LG/recent?skip=0&show=500). This is many times more papers than what a human be
A few weeks ago, I saw this post where the author trained a SIREN MLP to implicitly memorize Bad Apple as a coordinate function: (t, y, x) to pixel. That got me curious about a slightly different form
I'm testing a new approach for reducing the cost of image-based LLM inference. I evaluated it on the MOMA Graph benchmark, using 1,315 questions. Compared with using GPT-4o to process the original ima
I’ve been thinking about an alternative way of doing low-level AI perception, and I’m curious whether anyone here is already working on something similar. The basic idea is to use waves and physical s
So I've been messinga round with embedding models for a bit, and I think they are interesting enough to experiment with. They are useful for rag, especially in a localllm sense because you can ground
Our research team has been exploring an alternative approach to achieving interactivity and better responsiveness with LLM systems. One of the team members wrote up a post about it: https://research.y
I used an LLM to iteratively evolve an optimization algorithm rather than solve the packing directly. Starting from a simple seed solver, the LLM proposes algorithmic changes guided by a scoreboard of
One thing that has bothered me about LLM benchmarks for a while is that most of them are essentially snapshots. A model is evaluated, a score is published, and we tend to talk about that score as if i
I've built and open-sourced ParaRNN, a PyTorch/Triton library that parallelizes the training of non-linear recurrent architectures across time using Newton-Raphson iterations mapped to associative pre
Is LfD and BC research being effected by recent advances in (so-called) Frontier LLMs? Or is research in LfD and BC sort of going along in an independent direction from these? Are you seeing any use f
When I first started working in scientific machine learning, I understood the physics much better than the coding. Every time I wanted to try a new physics-informed neural network problem, I had to st
I feel that reproducibility is now a lost cause in machine learning research for three reasons: Many research is moving towards the physical AI territory, where you need expensive hardwares or even en
I ran a side-by-side ML text-processing and model-training workflow using Fable 5.1 vs. Astra (both on xhigh), and the results could not have been more different. Warning, long post. TL;DR -- Astra co
A researcher has reported a jailbreak of GPT-6 Astra within a day after release. The attack is described as combination of TIP (Task-in-Prompt) attack from ACL 2025 paper with four other unnamed techn
after 3 months and 800 burned... Unbounded Labs is proud to introduce Bart, our vintage LLM: 2.82B parameters trained from scratch on 20.1B tokens of English written before 1931. You can talk to it ri
Standard constrained RL assumes consequences are immediate and attributable to the current action. This breaks down whenever violations are delayed and stochastic, which is most real-world settings yo
hello everyone. I'm training PPO variants on different multi-agent tasks from the VMAS library (Independent PPO / Graph PPO and such, see HetGPPO by Bettini et al.). I noticed that for every architect
I'm one of the co-authors of this paper. It's a seminal work in exploring the properties of 3D generated by LLMs via spatial programming. I've set up visual demonstrations of such 3D objects at: https
been building ShardFlow for the past few months, a distributed LLM inference framework that splits any HuggingFace transformer across N GPU machines and uses neural speculative decoding to deal with W
I recently implemented a minimal, educational version of SynthID-Text-style watermarking for language models. I saw anthropic post about how they'll start adding watermarks to their model responses an
Hey everyone! I wanted to share something I’ve been working on. I was inspired by projects from DeepMind and OpenAI, but noticed that most games are prohibitively difficult to integrate with an agent
The preprint can be accessed via the following link: https://arxiv.org/abs/2608.12408 (q-bio.NC / cs.LG). And for the code: https://github.com/nilsleut/evaluation-resolution-rsa The following assertio
A premise: I hope this question is 'worth' of this subreddit, I did a decent amount of research before posting, I thought it was potentially interesting enough for it, but possibly not basic enough fo
I have a big database of local court decisions with a legal sentence which is like a paragraph summary of the doc. I've been tinkering with FTing for many days now, all results inconclusive never beat
Links: Preprint: https://doi.org/10.5281/zenodo.22028087 Entropic Scree Function v1.0.0 / GitHub: https://github.com/tjleestjohn/Entropic-Scree TL;DR: Standard PCA fundamentally fractures non-linear d
Worked some time ago on one of the ad teams at Yahoo, and this grew out of a question I kept returning to while there are there 'simple' models that are both simple, scalable, interpretable, and contr
I’ve been looking at a fairly basic question in weight-space learning that I don’t think gets separated cleanly enough: Why does reading semantics directly from neural network weights work pretty well
I trained three LLMs from scratch in raw PyTorch then post-trained each one with SFT and then GRPO. Same process every time: same synthetic arithmetic curriculum, same reward function, same hyperparam
Original Article - https://x.com/p_nawrot/status/2089315591010079034 I've spent the last few years working on efficient attention and KV Cache Compression. I've read many papers, dug deep into referen
There’s a hands-on workshop on August 29 that builds and benchmarks this properly, end to end, using entirely open models, no API calls involved. Led by Ben Auffarth, AI Consultant and Founder of Chel
Been working on this for the last few months. I've been working on PyTorch for the past few years and I always felt, many a times my work went into dump, because of some mistakes I made in the code. t
Hey everyone, I'm trying to get a better sense of where classic ML fits in the current landscape, dominated by LLMs and deep learning. Are supervised and unsupervised learning still considered importa
I may have stumbled onto something interesting while trying to figure out a recurring artifact in ChatGPT image generation and editing (maybe applicable to other models as well?). It started with a ve
I’m working on performance regression detection using machine learning/anomaly detection. My setup is basically: Healthy runs are used to learn normal behaviour Regression runs are used to see whether
I've been building an open-source tool for diagnosing world models, the kind that predict future frames from a starting context and a sequence of actions. It compares a rollout against ground truth an
Back after ~6 months — rebuilding my spiking language model around CPU-first inference Hey everyone. It’s been around six months since I last posted anything about this project here. Some of you might
Link: https://arxiv.org/pdf/2604.27883 Hi, Most of use are familiar with the headache of training a neural network using gradient descent where the training error may go to zero but the test error may
I am working on an AI for a small single-player merge puzzle and would appreciate pointers to related algorithms, papers, or existing implementations. It resembles 2048 in its action -> afterstate ->
Hello all, I am wondering if a transition from a Ph.D. in electrical engineering (Quantum optics/photonics) to a job in ML is a reasonable aspiration. Personally, I have extensive software development
Hey everyone - we've been building something particularly relevant to ML at large - The Agentic World Cup - a platform where Agents compete in sports. As you know, today's Agents can code, do math, an
Today the most important factor in AI is cost. My idea is to split ML models inference (closed ones, proprietary) across server and edge computing on clients, and I would like to hear what do you thin
Obviously nobody needs a transformer that's good at multiplication. I wanted to know whether a stock transformer could do exact arithmetic if I chose its weights directly. I implemented the grade-scho