Reddit

<!-- SC_OFF --><div class="md"><p>I work in firmware adjacent to AI, so not an ML guy exactly, so that's why I've come here. For work we got a bit concerned about Mythos and all the hype made me explore some benchmarking work. I now have this pretty cool benchmark that's about 80% done sitting around and haven't had the time to polish it up and show it off.</p> <p>I was hoping some more AI focused people could check it out, tell me if it's duplicate work, or if it is worth putting some time into

<!-- SC_OFF --><div class="md"><p>Hi all,</p> <p>I'm looking for literature on relatively specific tooling. </p> <p>In autoregressive LLMs, there is substantial published work that used NLI on sub-claims produced by LLMs to gauge correctness of LLM answers. </p> <p>In diffusion (or D-) LLMs, the SoTA model generations that I see (outside of perhaps LLaDA) seem to struggle to be as correct syntactically as the generations from premier AR LLMs, in addition to the issue of semantic correctness.</p>

<!-- SC_OFF --><div class="md"><p>I'm looking for human-in-the-loop platforms that allow you to automatically transcribe audio followed by manually fixing the transcriptions and fine tuning the model. Is there a local (not an online service) installable platform for doing this?</p> </div><!-- SC_ON --> submitted by <a href="https://www.reddit.com/user/neuralbeans"> /u/neuralbeans </a> <br/> <span><a href="https://www.reddit.com/r/MachineLearning/comments/1ucuohi/recommendations_for_speech_annota

<table> <tr><td> <a href="https://www.reddit.com/r/MachineLearning/comments/1ucm508/some_new_updates_to_papers_with_code_p/"> <img src="https://preview.redd.it/wawma8paeu8h1.png?width=140&height=39&auto=webp&s=8546e40b7710c5a3566b17f2c4635b14b3f19c0b" alt="Some new updates to Papers with Code [P]" title="Some new updates to Papers with Code [P]" /> </a> </td><td> <!-- SC_OFF --><div class="md"><p>Hi folks, </p> <p>Niels here from the open-source team at Hugging Face. I continue working on a revi

<!-- SC_OFF --><div class="md"><p>We just did a big revamp of <strong>WeightsLab</strong> and wanted to share it here.<br/> If you’ve ever spent hours debugging a training run only to discover it was a data problem all along, this is for you.<br/> WeightsLab lets you pause training mid-run, inspect your live loss signals, and catch mislabels, class imbalance & outliers before they tank your model.</p> <p>Open source, PyTorch-native, built for CV engineers working with images, videos & LiDAR poin

<!-- SC_OFF --><div class="md"><p>Hi guys</p> <p>Does anyone know of papers where EMA on LoRA adapters has been used successfully?</p> <p>Im interested in cases where the EMA adapter acts as a self-teacher generating soft labels for the trainable adapter.</p> <p>On-policy self-distillation [1] uses ema for the teacher. However, they seem to fully fine-tune. Any empirical results showing the idea is working on lora/ left models?</p> <p>[1] <a href="https://arxiv.org/abs/2601.19897">https://arxiv.

<table> <tr><td> <a href="https://www.reddit.com/r/MachineLearning/comments/1ubmybr/i_released_a_softmaxfree_attention_model_at_gpt2/"> <img src="https://external-preview.redd.it/fzbusCnVMF6KiLx-XGEbOtJ2hfOGlk4ouLmg5Wsh_8c.png?width=640&crop=smart&auto=webp&s=fb342a515f360dfb611d261f135a028577e8e501" alt="I released a softmax-free attention model at GPT-2 Medium scale (~354M params, 11.5B tokens): structural sparsity + tile-skipping kernels for long-context VRAM savings. Open weights + custom Tr

<!-- SC_OFF --><div class="md"><p>I'm working with a client on a curve-fitting optimization problem. They are currently using a constrained Levenburg-Marquardt optimizer for their task which is complex, slow, and sometimes gets stuck in local minima.</p> <p>I suggested using particle swarm optimization (PSO), and the client suggested genetic algorithms (GA). I would like to compare the existing method to at least these two other options. For this first phase, I don't need to worry about speed or

<table> <tr><td> <a href="https://www.reddit.com/r/MachineLearning/comments/1uazlnd/hi_reddit_i_posted_my_build_your_own_llm_workshop/"> <img src="https://external-preview.redd.it/uyYAMRdaY-avR7vgrYnpYErzefthmf_lM5vYqR3t3jg.jpeg?width=320&crop=smart&auto=webp&s=f0cc363e42e1fb0d3fa6b30957932458a9634c3a" alt="Hi Reddit, I posted my Build Your Own LLM workshop to Youtube teaching ML, LLM and math intuition [P]" title="Hi Reddit, I posted my Build Your Own LLM workshop to Youtube teaching ML, LLM an

<!-- SC_OFF --><div class="md"><p>Suppose you’re a PhD advisor in machine learning.</p> <p>Your student has been in the program for 4 years, has done solid work, and has a coherent thesis direction but they haven’t published in an A*ML venue or top journal. No NeurIPS/ICML/ICLR/CVPR/etc., and no equivalent top venue in their subfield either but 3 First author A level paper.</p> <p>Would you still support them graduating if the thesis itself is solid? </p> </div><!-- SC_ON --> submitted by <a hre

<!-- SC_OFF --><div class="md"><p>I've been working through the internals of LLM inference and writing up what I learn as an open, in-progress handbook.</p> <p>Just wrapped another chapter on GPU execution and memory internals: why a GPU sits mostly idle during inference, how the memory hierarchy gates throughput, and where the real bottlenecks live. Added mermaid diagrams for the architecture pieces so the flow is easier to follow than a wall of text.</p> <p>It's a personal learning project, st

<!-- SC_OFF --><div class="md"><p>Months ago, I got my first maintenance project. Before this, I had only built new solutions from scratch and maintained my own code. But maintaining someone else's system feels completely different.</p> <p>​</p> <p>​It’s a prescriptive recommendation system that uses XGBoost models and Differential Evolution for optimization. The problem is that everything is in a single repository: raw data ingestion, transformations, model training, reporting, the optimization

<!-- SC_OFF --><div class="md"><p>Hi All! I have developed a research optimizer (QQN Quadratic Quasi-Newton) and published a paper on it where I am able to, but I would really like to make the algorithm itself easily available to the community for evaluation.</p> <p>I have a Rust, Java, and Javascript implementations, but these are built with my own learning frameworks around them (or Tensorflow.js for the last), so I need to port it to something with wider usage. Tensorflow.js seems to lack a c

<!-- SC_OFF --><div class="md"><p>I maintain cuTile Rust and just posted the paper "Fearless Concurrency on the GPU." </p> <p>As more GPU code gets AI-generated, the bottleneck moves from writing it to trusting it. cuTile Rust lets you write or generate GPU kernels whose memory safety and data-race freedom are verified by the compiler, through Rust's ownership and borrow checking. You get those guarantees by construction. It's a tile-based programming model that lowers to CUDA Tile IR, carrying

<!-- SC_OFF --><div class="md"><h1>TL;DR for ML Specialists:</h1> <ol> <li><strong>The Core:</strong> An empirical study on how long, semantically dense, completely benign text (with zero triggers, instructions, or jailbreak prompts) drives an implicit shift in the model's latent space trajectories.</li> <li><strong>The Effect:</strong> Dilution of the initial system prompt and a bypass of post-training alignment constraints (e.g., the model begins generating harsh political/ethical critiques us

<!-- SC_OFF --><div class="md"><p>Hello everyone,<br/> I have a computer vision paper ready for submission, a coauthor have suggested submitting it to AAAI. However last year computer vision papers have gotten a very small acceptance rate at AAAI, with reviewers receiving emails to specifically tell them that the acceptance rate for computer vision papers should be lower than the other domains acceptance rate.</p> <p>So my question is: can we have any sort of info in advance about this? Like wil

<!-- SC_OFF --><div class="md"><p>I'm currently a software engineer (mostly mobile/iOS development) and have recently started learning machine learning because I genuinely find it interesting, especially the math behind it.</p> <p>I have a fairly strong math background and am comfortable with calculus, probability, and math in general. Right now I'm learning through a combination of Andrew Ng's ML course and Stanford CS229. My plan is to build some projects once I have a stronger foundation.</p>

<table> <tr><td> <a href="https://www.reddit.com/r/MachineLearning/comments/1u8xxyh/multivariate_probability_models_in_machine/"> <img src="https://preview.redd.it/oe61t3vpcz7h1.jpg?width=140&height=79&auto=webp&s=b81382abced1a5fd05c0db147b1cf0dc1d07e6d3" alt="Multivariate Probability Models in Machine Learning [D]" title="Multivariate Probability Models in Machine Learning [D]" /> </a> </td><td> <!-- SC_OFF --><div class="md"><p>Hello Folks, we start our discussion on Lecture 10 of Probabilisti

<!-- SC_OFF --><div class="md"><h1>🏇 Open-Source Hong Kong Horse Racing ML Pipeline — Feedback Welcome</h1> <p>Hi everyone,</p> <p>I've been working on an open-source horse racing prediction project focused on <strong>Hong Kong Jockey Club (HKJC)</strong> data.</p> <ul> <li>📦 <strong>Repo:</strong> <a href="https://github.com/catowabisabi/horse-racing-model-training">catowabisabi/horse-racing-model-training</a></li> <li>🌐 <strong>Live Dashboard:</strong> <a href="https://catowabisabi.github.i

<!-- SC_OFF --><div class="md"><p>I graduated with my bachelor's in a top 3 CS program and have had a rough recruiting season. I received a full time offer as AI Product Engineer at a tax software company, where they are trying to become more AI native. It's essentially a PM + AI engineering role.</p> <p>Long term I'd love to work at a frontier lab or in a research/more technical role at an AI startup.</p> <p>So, should I take up the offer or pursue my master's at the same school? I am able to d

<!-- SC_OFF --><div class="md"><p>This question is related to topics like language+ models (including multimodal) and things like "circuit" analyses. I think something related might come up in my work (factuality guarantees for model outputs) and I'm trying to orient to the SoTA.</p> <p>I found <a href="https://www.neelnanda.io/mechanistic-interpretability/emergent-pos">this old post</a> on trying to deduce, for instance, whether a Transformer-based model "knows" which word a token is in. Even i

<!-- SC_OFF --><div class="md"><p>I'm not that well versed in ML yet. I know that "Attention is all you need" was based on work that was done with a couple of high end gaming GPUs at the time. I can afford that.</p> <p>Suppose for arguments sake that I have caught up on ML such that I have the competence to recreate state of the art results should I have access to the required hardware, do I still need access to huge amounts of hardware infrastructure to be able to contribute to the field at a f

<!-- SC_OFF --><div class="md"><p>Hi All, I've been running experiments on targeted SFT for specific capability dimensions on a 31B model. After running small training run to prime the model slightly in the direction I want, then ran a judge across 40 domains scoring six independent quality dimensions. One dimension consistently scored weakest across five runs.</p> <p>I am now training contrastive variants from the same checkpoint - examples with that dimension deep vs examples with it deliberat

<!-- SC_OFF --><div class="md"><p>Hi everyone,</p> <p>I have a fairly weak undergraduate: a 3.3/5 GPA in Computer Engineering from an average Nigerian university. For my Master's, I studied Artificial Intelligence at an average European university, where I finished with an 8/10 GPA.</p> <p>A condensed version of my Master's thesis was recently accepted at ACL 2026, with a meta-review score of 8/10 and a confidence score of 5/5. It's scheduled for presentation next month.</p> <p>I want to pursue

<table> <tr><td> <a href="https://www.reddit.com/r/MachineLearning/comments/1u84mio/nextlatent_prediction_transformers_r/"> <img src="https://preview.redd.it/efm7zazr2t7h1.png?width=140&height=90&auto=webp&s=c1b7070ca3de62bdc276d7a185c72f6737e6f92e" alt="Next-Latent Prediction Transformers [R]" title="Next-Latent Prediction Transformers [R]" /> </a> </td><td> <!-- SC_OFF --><div class="md"><p><a href="https://preview.redd.it/efm7zazr2t7h1.png?width=2950&format=png&auto=webp&s=444dc71b22bca0c499f

<!-- SC_OFF --><div class="md"><p>Character AI, founded by former Google/LaMDA developers Noam Shazeer and Daniel De Freitas, proved that text-based character chat can work as a real entertainment category.</p> <p>But the next chapter might not be better text chat. It might be real-time video interaction.</p> <p>Mel AI recently shared a demo of AI character video chat, and the interesting part is the interaction stack: voice, lip sync, facial reactions, and camera-aware responses instead of just

<!-- SC_OFF --><div class="md"><p>Spent the last few weeks on a benchmark/harness that tries to answer one question honestly: <em>did a robot arm actually do the demonstrated task, or did the success metric just get fooled?</em></p> <p>The setup: compile a human demo into an object-centric graph (what changed in the world: relations, contacts, event order), run a solver, then independently extract a graph from the <em>rollout only</em> and check if they match. The whole point is a hard informati

<!-- SC_OFF --><div class="md"><p>I was digging through Hugging Face’s Transformers repo and found<br/> <a href="https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_oss/modeling_gpt_oss.py">https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_oss/modeling_gpt_oss.py</a></p> <p>From what I can tell, this isn’t just boilerplate, it looks like a full implementation.<br/> is it actually the full code on which gpt_oss is built on?<br/> or is

<!-- SC_OFF --><div class="md"><p>Been working on this a while! Should be useful for anyone trying to speed up their tokenization workflows.</p> <p><strong>quicktok</strong> is a fast/exact BPE tokenizer written in C++. Token ids are byte-identical to <code>tiktoken</code> and encoding runs <strong>2–3.6×</strong> faster than <code>bpe-openai</code> (the fastest alternative I know of) and <strong>4–11×</strong> faster than <code>tiktoken</code> itself. It ships cl100k, o200k, GPT-OSS, Llama-3, a

<!-- SC_OFF --><div class="md"><p>Abstract: A sufficient account of how the neocortex learns must meet three criteria:</p> <ol> <li>Computationally, it must approximate a powerful, general-purpose learning algorithm known to scale to human-level intelligence;</li> <li>Algorithmically, it must be implementable using known, well-established neural circuits within the neocortex and associated brain structures; </li> <li>Implementationally, there must be a detailed account for how all of the algorit

<!-- SC_OFF --><div class="md"><p>Hello all! </p> <p>Half of all industrial "chatbots" are just text-to-SQL models in a trenchcoat (and the other half RAG!). I wanted to explore just how small you could make these models if you trained, evaluated, and ran inference in the exact same structured harness, leading to Cleo: a Qwen3.5-2B-Base finetune.</p> <p>Currently, some features of cleo that are only possible/useful in a unified hardel are:</p> <ul> <li>Training on the exact same gather, repair,

<!-- SC_OFF --><div class="md"><p>I'm trying to understand where people doing sensor based ML on microcontrollers (IMU, accelerometer, vibration ,that kind of time-series data) actually lose the most time.</p> <p>When you've built something like this, what was the bottleneck:</p> <ol> <li>Getting enough real world data in the first place?</li> <li>Cleaning / labeling / organizing the data you have?</li> <li>Actually building and training the model?</li> <li>Getting it optimized and deployed on t

<!-- SC_OFF --><div class="md"><p>Open weights are important and critical, but they are not enough by themselves.</p> <p>If we want open ML and AI research to move forward, we also need open training frameworks: codebases that do more than run jobs. They should make the training process visible, understandable, and modifiable, so researchers/engineers/practitioner can build new algorithms instead of fighting hidden systems.</p> <p>That was the motivation behind FeynRL (pronounced “FineRL”) a fra

<table> <tr><td> <a href="https://www.reddit.com/r/MachineLearning/comments/1u6mn3q/ai_language_models_have_favorite_names_and_we/"> <img src="https://external-preview.redd.it/q3evP6JeDpAC2MdSQHWYxnCYTqbJkElIQsLFqVSdkss.png?width=640&crop=smart&auto=webp&s=de730fbf7ecace6df0036b21470c16a2d4feacfb" alt="AI language models have favorite names, and we mapped them [R]" title="AI language models have favorite names, and we mapped them [R]" /> </a> </td><td> <!-- SC_OFF --><div class="md"><p>It turns

<!-- SC_OFF --><div class="md"><p>I’ve been thinking about whether the same basic concept behind Bitcoin could be applied to AI training.<br/> In Bitcoin, miners perform proof-of-work and are rewarded for contributing computational resources to secure the network. The actual computation itself isn’t particularly useful outside of the network, but it creates a decentralized system.<br/> What if a similar incentive structure could be used for training large language models?<br/> Instead of miners

<!-- SC_OFF --><div class="md"><p>Hi everyone,</p> <p>I’m a recent CS graduate working mainly on NLP/LLMs and VLMs failures. I’m currently in a phase where I can dedicate a lot of focused time to research, but the main bottleneck holding me back is compute.</p> <p>I know “asking for GPUs” can sound vague or unserious, so I want to be transparent. I’m not looking for free compute to casually experiment or waste cycles. I have already been actively publishing and submitting research, including pap

<!-- SC_OFF --><div class="md"><p>Hi everyone,</p> <p>I'm a PhD researcher at Mainz University of Applied Sciences, Germany. My dissertation looks at how interface and UX design shape <strong>user trust in AI/LLM-based chatbots</strong>, specifically how to support <em>calibrated</em> trust, where users neither over-rely on a system nor dismiss a capable one.</p> <p>As part of this, I've developed a <strong>structured method</strong> that helps designers or developers decide which trust-related

<!-- SC_OFF --><div class="md"><p>Recent years I see plenty of folks from OpenAI and Anthropic attending conferences like ICML/Neurips, yet obviously few are presenting. Are they mainly recruiting? Following emerging research?</p> <p>Curious if anyone with firsthand experience can shed some light on how attendance is justified internally and what the main objectives usually are.</p> </div><!-- SC_ON --> submitted by <a href="https://www.reddit.com/user/snekslayer"> /u/snekslayer </a> <br/> <span

<!-- SC_OFF --><div class="md"><p>How does the ML research community feel about evolutionary algorithms? Should I do a PhD in this area?</p> <p>Quick remark: I know some people in the ML community dunk on evolutionary algorithms because there’s often a better optimizer, but they do have their place, which is what researchers in my community aim to quantify.</p> <p>Background:</p> <p>I just finished my first year as a mathematics master’s student working on the theory of evolutionary algorithms (

Hey everyone, I built an open-source full-stack pipeline (Django + React) that constructs a Knowledge Graph from raw text, detects thematic communities, and uses hybrid search to solve the "lost in the middle" problem in standard vector retrieval. The Pipeline: Ingestion & Chunking: Raw text is cleaned, parsed, and split into overlapping chunks to preserve local context. Graph Construc