Reddit r/MachineLearning

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), and remains highly unsaturated. GPT-6 Astra max achieves 2500 Elo, much lower than the best KataGo, which achieves 4400 Elo. With coding tools and two hours of preparation before evaluation, Codex with Astra achieves 3560 Elo. I will keep the leaderboard updated as long as it is not saturated. leaderboard: https://rolandgao.com/blog/gobench/ code: https://github.com/RolandGao/gobench paper: https://github.com/RolandGao/gobench/blob/main/paper/gobench2.pdf x: https://x.com/Roland65821498/status/2100253388562723298?s=20 submitted by /u/Roland31415 [link] [comments]

I have received 2nd email from E&D track chairs regarding the reference checker report. I got flagged for 2 references Paper and Venue exist. The author's first name has 2 extra letters. Paper and Venue exist. Of the 3 listed authors, the first names of 2 are incorrect, while the last names are correct. For the 3rd author listed, It is correct. This is a mistake from my end. I have allowed LLM to touch my bib file for formatting. I am not sure whether they will come under minor or major issues. Received mail clearly states that: The author list does not match the title, includes additional authors or is missing authors. Minor mismatches are acceptable. We allow major issues with at most one reference. Is my submission going for Desk Reject ? submitted by /u/suryanreddy [link] [comments]

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 developed a small PyTorch library that implements it. The main idea is to train a low-rank residual adapter at selected layers rather than modifying the model's weights. The resulting behaviors are small enough to keep separately and can be loaded, removed, blended or routed at inference time. The Mixture of Behaviors (MoBs) demo is maybe the easiest way to see what this means in practice. Several independently trained behaviors can share the same frozen model, with a soft router selecting or combining them on a token by token basis. For example, a single model can have separate coding, maths, m

A new paper (I'm not a coauthor BTW -- I just found it interesting) argues, basically, that RSI is not on the horizon, because current (at the time the study was done) agents cannot do open-ended ML research. Specifically, they took some accepted, but unpublished papers from NeurIPS, and tried to get the agents to do the same work, which was then graded by the original authors. And the agents (Codex/GPT-5.6 Sol and OpenClaw/Opus 4.8) could not do it. And since they cannot do open-ended ML research, they cannot recursively self-improve -- this is their argument. Link: https://arxiv.org/abs/2607.27191 I think I've regretted the last 10 or so times I posted any kind of "research" in this subreddit -- either people downvote it, or it gets upvoted, but there is zero meaningful discussion. This

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 thing is the baseline models I need to only run once because I have reported same baseline in both papers, so the RMSE tables looks exactly same except the proposed model, does it lead to any problems like palgiarism?? Edit : I don't know why I am getting downvotes submitted by /u/Jealous_Key_4030 [link] [comments]

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 recognition 100% client-side using local inference—with zero image data ever leaving the user's machine, support for detecting multiple boards in a single frame, and entirely free features. The main goal was to bridge passive chess content (YouTube, Twitch, PDFs, articles) with active engine analysis without context switching: capture what's on screen and get a FEN string + engine eval in a couple of clicks. System Architecture & Technical Approach On-Demand Capture (Multi-Board Support): Instead of continuously sampling video frames, the extension captures a screenshot of the visible tab via

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 has a strong research division, where I can continue similar AI/ML work. I have concerns about the job market (both now and in 5 years), and I'm wondering whether I should do my degree under the CS or EE department. For context, my research is within the eecs overlap and this degree would not change my research at all, rather it is a personal branding exercise. I'm thinking about saturation in cs vs ATS filtering/wide applicability of cs as a tradeoff. Please let me know what you recommend. submitted by /u/legoWolf13 [link] [comments]

Dario says we need to pace the frontier. Good news: we’ve been pacing Tahuna for months. Today, Tahuna is open source—as promised back in April. We built it so small teams could train models, run inference, orchestrate GPUs, and experiment with autonomous research without first becoming a small cloud provider. The basic workflow: init → sync → train → artifacts → serve Under the hood: content-addressed code and data sync, compute provisioning, reproducible manifest-pinned runs, metrics, checkpoints, artifacts, and inference deployments. We also started building Hillclimb, an autonomous experimentation loop that proposes and runs iterative improvements. The first public-preview release supports RunPod and R2. It includes Docker self-hosting instructions, a coding-agent setup skill, and exam

I’ve been working on a small research project about whether sub-million-parameter models can learn to generate executable drawing programs for constrained hardware. The current system is an 825k-parameter autoregressive transformer which generates ~100 bytes of drawing bytecode rather than pixels. The bytecode is then transferred to a Raspberry Pi Pico, where a small fixed-point virtual machine executes it and streams the resulting geometry back over UART. The model runs on the host. The Pico only stores and executes the generated program, so this is not a claim that the transformer itself runs on the microcontroller. The execution side is currently the most solid part of the project: 12,670/12,670 generated traces matched the Python reference VM exactly, 1,862 bytes of flash for the inter

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 being or even a sizeable reading group could feasibly read and digest in a year. This is preceded by around 200/day of new ML papers before and after. Are we pass the point of no return? Should the system be be, like he says, "burned to the ground" before good science can resume? submitted by /u/NeighborhoodFatCat [link] [comments]

So want to switch to macbook after running epochs on my run down vivobook which still works pretty well thanks to its rtx 3050. Now my budget us nearly 80,000-1lac rupees(800-1000$) and buying it second hand is also an option. I could have asked this in any apple Reddit pages but most of my work include python programming, sql and LLM frameworks. Also I’m a 26 passout looking for jobs thats y building projects and always posting on x. I wanted suggestion which to buy M2 - can buy easily M3 - moderately flexible M4 - can do if my father sponsors me 🥲 M5 - on a far side but can try by using offers submitted by /u/UnionInside7251 [link] [comments]

The title, by tech reports I don't mean arXiv submissions, but reports of a large model, like say Kimi K3, DeepSeek, Gemini, Mistral Leanstral, etc. Is it much above, above, much below, below or equal to a first author A* paper? submitted by /u/simple-Flat0263 [link] [comments]

I trained a 210M-parameter text-to-image diffusion transformer from scratch (3.5 days, one RTX PRO 6000, 4.2M images at 256²) mainly to understand the recipe end to end. Three measurements came out of it that I have not seen stated plainly elsewhere, so I'm posting those rather than the samples. 1. Learned null attention slots become the sink. Following the register-token idea, the model has 16 register tokens in the image stream plus 2 learned key/value slots appended to every cross-attention. At mid-noise in a middle block, those 2 slots receive ~90% of the cross-attention mass; the EOS token, which is the usual sink in cross-attention models, drops to ~4%; content words keep a few percent each, sharply on their objects. Register vectors grow to 4–13× the norm of image tokens by the midd

I'm an undergrad student who got a taste of research. I love it. I currently have a draft, which me and my mentor have planned for TMLR, and plan to submit it by next month for the first round of review. It was some work on self explanation methods of LLM models. We are confident that it'll get accepted (I hope it does, I've put a lotta hours polishing it into making it a good accept :_) ). I'm very, very interested in TTT. I've got a strong feeling that this is gonna be the big thing in 2-3 years. Is there anyone here working on this, who can hopefully provision me compute beyond my own laptop? I believe I'll be a good RA to you. We can talk over DMs if someone out there is looking for help :) PS, I can spill some beans about the XAI paper if you wanna discuss, but only limited to how muc

Hello this is my fifth small language model I've made and apart of my third series and it has been a lot of work but it payed off: **348M parameters, 22.7B tokens**, then fine-tuned into a math model that solves arithmetic by *showing the work* — column addition with carries, borrow chains, partial-product multiplication — rather than guessing at an answer. Last time I posted a 326M model trained on 10B tokens. This has about 2.3× the data, and the math side is WAY better than my previous two math models. --- ## The benchmarks **99.4% average across the nine GPT-3 arithmetic sub-tasks**, which does much better past even where I trained it. | Task | GPT-3 175B *(few-shot, direct)* | **This model (348M)** | |---|:--:|:--:| | 2-digit add | ~100% | **100%** | | 3-digit add | 80.4% | **100%** |

Hi r/machinelearning . Nice to meet you! My name is Chris Piech and I'm a professor at Stanford University in the AI lab. I built a class called Probability for AI: pai.stanford.edu. It starts Oct 9th and applications are due end of Sept. Its (hopefully) cool for a few reasons: The plan is to have one volunteer teacher for every 10 students! Apps have been open for a week and over 1,000+ folks have applied to teach. So we might actually be able to make this pretty big. I have built a lot of fun tools to make the assignments neat and easy for folks with just light math background. For example in your application, after about 1 hour of learning you will build an AI text detection app alongside a free coding agent -- that cares about probability education. If you are a teacher, we will give y

As reported by the New York Times: https://www.nytimes.com/2026/09/08/science/openai-proof-millennium-problem.html?smid=nytcore-ios-share OpenAI’s announcement: https://openai.com/index/navier-stokes-solution/ submitted by /u/Shizuka_Kuze [link] [comments]

I was wondering why dynamic competitive games like Rocket League don't have a decision-quality engine like chess has with Stockfish. Something that doesn't just measure your boost usage or speed, but actually evaluates whether your positioning or challenge was the mathematically optimal play. I initially drafted a naive proposal based on slicing replays into 5-second physical rollouts in the cloud to test decision trees. However, after using AI models to strictly critique the physics and compute feasibility, Machine Learning seems to offer a much more viable path: Decision Quality: Evaluates plays using Offline RL (Trajectory Transformers & Implicit Q-Learning) over a Sequential POMDP. Instead of chaotic physics simulations, it calculates decision probability based on pro dataset distribut

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 your answers in truth. But what happens if you have a billion documents, and you decide to upgrade your model to a "better" one? on an h100, that would take about 108 days, just to upgrade the vectors so u can start serving again (tested qwen embed 8b on h100). Even if you aren't doing 1b vectors, and are doing just 50 million, upgrading can still take a considerable time. Me and my research lab decided to tackle this problem, and we came up with embedflow. The method is really simple; from the old index made with the source model, take K documents and rerank them with the new model. We see th

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 images directly, I observed approximately: ~95% lower token usage roughly the same accuracy as the GPT-4o direct-image baseline I'm intentionally not sharing implementation details yet because the method is still under development. I'm mainly trying to understand how strong the result itself is. If these numbers hold across larger and more diverse benchmarks, would you consider this a meaningful result in multimodal AI efficiency? What evidence would you want to see before taking the claim seriously? For example: more datasets stronger baselines statistical significance latency measurements API