Reddit r/MachineLearning

TL;DR: Please be hard in desk rejecting AI generated papers Recently I went to two AI conferences (A and A*) and I was surprised that "AI cheaters" didn't even read their own paper before presentation! For example, a guy couldn't even answer the question of how he represented knowledge graphs in his papaer, he thought for 5 minutes before answering "with a vector". Another one was opening chat gpt and typing questions and reading answers with no shame!!! One more story that shocked me... a guy published same paper in 2 AI conferences in the same time and both got accepted, when I saw github, this young dude was careless to even remove LLM chat from his repo, I can see all prompts... guess what... he faked all data 🚀 :D ...I have dozens of stories like this happened to me and my friends jus

Hey everyone— we’re the Deep Principle team behind Mira AI Scientist. We just released Hydrogen-1.0 , an open-weight model post-trained from GLM-5.3 for chemistry, materials science, and life sciences. Here’s how it performed in our evaluations: FrontierScience Research: 38.3% pass rate vs. 33.3% for GPT-6 Astra at max thinking—a 5 percentage point lead . Estimated output-side cost on FSR: $0.25 per problem vs. $0.88 for Astra at max thinking—about 72% lower under our evaluation setup. GPQA Diamond: 92.9% accuracy, compared with Kimi K3’s 93.5% . On separate internal task sets, Hydrogen also scored higher than GPT-6 Astra at max thinking on scientific computing (+5.13 percentage points) and single-step forward reaction prediction (+4.76 points). It trailed Astra on NMR structure elucidatio

Question to all the PhDs & research engineers in this group. Have any of you done small-scale experiments with the Chinchilla scaling laws (using PyTorch or JAX)? If so, we'd be keen to hear more (or have a chat). Thanks! Here's the official Chinchilla paper, from the DeepMind website ("Training Compute-Optimal Large Language Models"). https://deepmind.google/blog/an-empirical-analysis-of-compute-optimal-large-language-model-training/ https://proceedings.neurips.cc/paper/2022/file/c1e2faff6f588870935f114ebe04a3e5-Paper-Conference.pdf submitted by /u/turing-math-labs [link] [comments]

Hi everyone, I wanted to share a new open-weights PII detection model, benchmark and research paper. It's a fine-tune of RoBERTa using 1.2B samples of synthetically generated text that contain a variety of PII. The benchmark, PII-SCOPE (Synthetic Context and Ontology for PII Extraction), was built to address common issues in public LLM-generated datasets and is on Hugging Face too. The model and script identify instances of PII based on surrounding context, and then return the index and type of each instance. Alternatively, it can return the input with all PII redacted. Technical report (research paper) https://arxiv.org/html/2610.30518v2 Hugging Face model card https://huggingface.co/Protegrity/protegrity-pii submitted by /u/shanekj [link] [comments]

What are the latest papers that are there in the space on inference architecture of llms, I would like to discuss with them on the approaches. Btw I have been doing work with llms for the last 2 months and I have gotten to know very new things. I read about the lunch table discussion of bell labs so taking motivation from it would like to discuss it in a similar fashion!! submitted by /u/Local_Minima0507 [link] [comments]

I’m the author of a new preprint looking at a fairly simple question: if the argument itself stays the same, does changing the claimed source change how an LLM evaluates it? I tested 20 arguments across 10 topics under four source conditions: relevant expert irrelevant expert non-expert no source The arguments were evaluated by GPT-5.6 Sol, Claude Opus 5.5, Gemini 4 Argon, and DeepSeek Flash across four dimensions: logic, credibility, persuasiveness, and agreement . A few findings stood out: Credibility was by far the most source-sensitive dimension. Source attribution affected credibility much more consistently than logic or agreement. More interestingly, the effect did not usually look like a simple “expert bonus.” In several models, the stronger pattern was an irrelevant-expertise penal

The reviews will be out soon. Really worried about the narrow range of scores 1-4 this time and about people using the AI based LLM reviews instead of actual fair reading. Share your opinions. Lets keep this to update the reviews received. submitted by /u/Old-Duck-3693 [link] [comments]

Link: https://rikiyat.github.io/blog/weight-space-programming/ Snippet: “We find that frontier LLMs are surprisingly good at natively reading and writing neural network weights , well past what we’d expect from humans or conventional algorithms. This might be a big deal for both optimization and interpretability. Just by looking at the raw weights, GPT-6 Astra can tell what a network was trained to do . And without any environment feedback, it has no trouble writing, from scratch, small game-playing networks that outplay networks trained with PPO for 100 million steps.” Very surprising result, I didn’t expect frontier models to have this capability that has not been optimized for, and it’s something humans cannot do either. submitted by /u/RobbinDeBank [link] [comments]

I'm writing a machine learning paper. One of my baselines is a published method. I reran it with the authors' released code and the exact hyperparameters listed in their paper. On most benchmarks my numbers match theirs within noise, but on two benchmarks my reproduction is clearly lower, by several points, over three seeds. I've repeated the reproduction four different times, months apart, with the same result. I plan to report both: their published numbers and my reproduction with the exact configuration, clearly labeled. My worry is that reviewers tend to trust the published number more than a submission's reproduction, and may assume I ran the baseline incorrectly. Questions: What makes a reproduction convincing to reviewers? So far I have: the authors' code unchanged, their hyperparam

It was just before OpenAI started that I met Ilya, who you interviewed . One of the first things he said to me was — “ Look. The models, they just want to learn. You have to understand this. The models, they just want to learn. ” And it was a bit like a Zen Koan. I listened to this and I became enlightened. And over the years, I would be the one who would formalize a lot of these things and kind of put them together, but what that told me was that the phenomenon that I'd seen wasn't just some random thing. It was broad. It was more general. The models just want to learn. You get the obstacles out of their way. You give them good data, you give them enough space to operate in, you don't do something stupid like condition them badly numerically, and they want to learn. They'll do it. I'm act

https://preview.redd.it/260fkihtk3zh1.png?width=2400&format=png&auto=webp&s=a80b6861abdeee3c9a4e705b6ad9b337be005722 hey folks, I'm Ye, one of the contributors to Kapitsa, an open-source inference engine optimized specifically for Apple Silicon to run Qwen, Nemotron, Flint and Clover models locally. Our custom-written Metal kernels let us tune for each chip and each model. https://github.com/nace-ai/kapitsa/ The latest update, 0.2.26, is out. Since our last M5 Max numbers, Flash-Next 180B reads prompts 46% faster and the 27B writes 8% faster, and DiffuSpace's Clover 35B joins the catalog. Kapitsa also runs Qwen Flash-Next 180B with SSD offloading : on a Mac that can't hold it, the experts that don't fit in RAM stream from the SSD, so it runs on a 48 GB MacBook Pro at 61 tok/s. Here are the

I made this explainer for my small channel and would like feedback from people learning this, especially if anything is wrong. It follows one word the whole way: "How many r's are in strawberry?" is 8 tokens in o200k_base (the GPT-4o / GPT-5 tokenizer). " strawberry" is a single token, id 101830, so the three r's are hidden inside one id. Why pieces of words: letters make sequences about 4x longer, whole words need a huge vocabulary and can't handle new words. A tiny byte pair encoding run on a made-up text, merge by merge, until strawberry becomes st | raw | berry. The space belongs to the word: "strawberry", " strawberry" and "Strawberry" are three different token sequences. Why reasoning models count letters better: in OpenAI's 2025 paper "Why Language Models Hallucinate", DeepSeek-R1 s

For context, I am a new postgraduate student in machine learning (but not a very good one). I have produced some augmented datasets (TIFF files where each file contains one object to be detected / classified) which I used in training deep learning models. I used fairly aggressive cropping (random crops between 0 and 90%) and more moderate zooms (random values between 0 and 20%). My supervisor wants me to characterise these datasets to see how augmentation has changed it. Sorry if this is a rookie question, but which statistics should I use? My supervisor says the stats I use should indicate changes in size and shape but I honestly don't know where to start. If anyone can recommend a decent software/program to produce the stats and histograms as well, that would be great. Thanks in advance!

I'm an undergraduate researcher working independently on conversational memory systems. I developed LSREP to evaluate how persistent memory accumulates, changes and retrieves information over the course of an evolving conversation. Instead of relying only on isolated endpoint questions, LSREP combines chronological replay, lifecycle schedules, repeated probes, evolving reference answers and mechanism-fidelity checks. The accompanying architectural case study is ICE v2, a local-first conversational memory system. The evaluation covers 1,985 turns, 219 distinct probes and 1,211 probe-checkpoint observations across 52 checkpoints, using a private single-user history. An important part of the findings is that ICE v2 did not outperform pure vector-RAG on the matched LongMemEval evaluation. The

Hi there, wanted a suggestion from the community regarding the hardware to run local models. The first option was just to get a MacBook Pro with 32 GB RAM, and the second one was to invest into Mac Mini (256 GB SSD and 32 GB RAM), and get an iPAD as well PS; I have access to B200, and multiple A100s/L40s at my Univ which I can definitely use for training purposes, and I just wanted to get a device to run local inference submitted by /u/Fantastic-Nerve-4056 [link] [comments]

Hey everyone! I've built and trained a small LLM (~8M parameters) from scratch using PyTorch, mainly as an experimental lab where I can implement and test ideas from different research papers. I've been documenting the whole journey through casual blogs, including my mistakes, experiments, and things I've learned. So far: Building the Transformer Training it for 60,000 steps Next up is KV caching, followed by PagedAttention, Orca, and whatever else I find interesting. Just wanted to ask if sharing these experiments and blogs here would be welcome. Nothing promotional, just me experimenting, learning, and hopefully getting feedback from people who know more than I do. submitted by /u/the-random-guy-2002 [link] [comments]

After reading a bunch of RSI papers, I keep coming back to familiar problems from RL and evolutionary search: Sampling: How do we find promising changes efficiently? Evaluation: How do we know a new variant actually improves the agent? Diversity: How do we avoid converging too early? That motivated us to build Gear , an open-source, meta-learning-inspired framework. A task agent produces execution traces; a meta-agent uses those traces and evaluation feedback to improve prompts, tools, workflows, and context management. Gear separates search decisions from execution and evaluation. Our goal is a scikit-learn for RSI , making approaches such as GEPA, RRSI, and Harness² easier to implement and compare. We currently have a working GEPA variant with shared failure diagnosis and staged evaluati

I rarely write my own code these days. I’m building cool applications but I barely remember how the real magic works. I’m starting to doubt I could’ve come to with the solution(s) I have without an agent/tool. How are people fighting this? Are people fighting this? submitted by /u/PrimevalSpectre [link] [comments]

Two GSM8K scores: 96.89% → 96.82%, across all 1,319 test questions. These are from NVIDIA’s Ling-3.0-flash comparison on one DGX Spark. The optimized configuration changes the MoE backend, adds an FP8 output head and enables NEXTN speculative decoding together. Both runs use the FP4 checkpoint. Aggregate scores summarize the total. A paired view of the 1,319 answers would show where the two configurations agree and where they trade wins and losses. For a claim of no meaningful quality regression, what evidence would you consider sufficient: paired per-question outcomes, repeated seeds, a declared equivalence margin, another task set? Especially when a precision change and a decoding optimization ship together, which checks give the most information for the evaluation budget? submitted by /

I built Arc-Transcriber, which does ASR-NL in 1-step. Every 100 ms, one model returns the transcript plus: - Names linked to a list you give it (a roster, contacts, product names); new names are flagged "not on the list" - Numbers with their values (scores, clocks, money, dates) - Speakers and roles; - Sound and channel events (telephone audio, clipping, poor audio) - Whether the speech was addressed to an assistant. - Inbuilt tags for dialog act, speaker segmentation and more. Total 200+ trained decision heads. The usual ASR, then NER, then entity-linking and then LLM chain is collapsed into the model's own heads, so live "fact-checking" against your list happens inside the stream, not in a second pass. Currently it has two variants Arc mini (which uses moonshot pretrained encoder) and se