Search: “papers”

I'm a Ai engineer with about 2 years work experience, but let's just assume that I was a undergraduate student just starting out where would I begin so that I can publish a A* conference paper at some

I want to try using AI to find possibly overlooked interventions for irritable bowel syndrome (IBS) and create a cocktail of interventions that AI thinks would give the highest expected benefit, attac

This r/MachineLearning discussion thread focuses on the post-discussion reviewer scores for ICML 2026's dedicated Position Paper Track, where authors and community members share and compare their revi

UT-based small models, despite being trained from scratch on these tasks without internet-scale pre-training, consistently outperform most standard Transformer-based Large Language models (LLMs) by a

As for me, I own a system with an RTX 5090, Ryzen 9 9950X3D2, and 64 GB of DDR5. Every time I see research come out with a new way to train AI, I immediately think to try it on my system to see the re

Hi, same as the title, I am currently trying to begin with some research on clustering using LLMs. So my requirement is as follows: I will be given some 100 document files, the end goal is to have clu

Paper linky - Context Language Models The central idea of the paper is incredibly simple. Give a model the ability to edit its context on-the-go like a file has major benefits on task performance, con

This r/MachineLearning discussion thread addresses the widespread challenge of reproducing results claimed in modern ML research papers, a topic of significant concern in the field. Community members

One of the papers I reviewed has what seems to be entirely LLM-generated rebuttals, and the original paper is also clearly LLM-generated, with Claude-speak everywhere. While the authors acknowledge LL

Everyone now talks about the architecture that's not auto regressive and does lightning fast probability prediction with a json schema. I worked on this literally one year back in March 2025, publishe

Hi everyone, We've open-sourced Tri-Net v2, the official implementation accompanying our recently published Scientific Reports (Nature Portfolio) paper: 'Tri-Net: Unified Deep Learning for Skin Lesion

This r/MachineLearning discussion thread explores the standards and expectations reviewers and program committees use when evaluating ML papers for acceptance at top venues such as NeurIPS, ICML, and

Model quantization has become essential for efficient large language model deployment, yet existing approaches involve clear trade-offs: methods such as GPTQ and AWQ achieve practical compression but

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

I’ve been experimenting with a custom inference/runtime research project called CRANE V2, mostly because I wanted to answer a stupid question: How far can you push absurdly large MoE models on an ordi

A developer documented their experience building a fully-local paper Retrieval-Augmented Generation (RAG) system using two NVIDIA GTX 1080 Ti GPUs and one RTX 3090, sharing three significant challenge

DeepSeek released the full technical report for DeepSeek-V4 on April 24, 2026, titled 'DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence.' The paper details FP4 quantization-awa

This Reddit thread from r/MachineLearning reflects community skepticism about the peer review standards at top ML conferences, with a user expressing surprise that a particular paper received an oral

Training large language models (LLMs) is resource-intensive, and adapting them for diverse deployment scenarios with varying computational constraints remains challenging. While elastic architectures

I was very curious why all the models were topping out on GPQA-Diamond around 92 or 93% (AA) and spent the last few weeks pouring over GPQA (Diamond and Extended), and then expanded to auditing MMLU-P

Speculative decoding accelerates autoregressive generation in large language models. In each drafting stage, a lightweight draft model proposes tokens that the target model subsequently verifies. With

Conditional memory architectures such as DeepSeek Engram use input n-grams to look up learned embeddings, expanding the capacity of large language models (LLMs) with limited additional computation. Be

ToMoE: Converting Dense Large Language Models to Mixture-of-Experts through Dynamic Structural Pruning Large Language Models (LLMs) have demonstrated remarkable abilities in tackling a wide range of c

Large language models (LLMs) are transforming recommender systems from matching co-occurrence patterns in historical behavior toward reasoning about the intent that drives it. RecGPT-V1 pioneered this

Full-parameter post-training of trillion-parameter-scale MoE models introduces substantial system-level challenges for large-scale distributed training, including severe memory pressure, non-overlappe

Looped language models repeatedly apply a weight-shared block to increase effective depth without increasing parameter count, but the resulting T sequential recurrent-block calls per generated token s

We introduce Mobius-v0, an architecture that comprises a globally shared Memory (FFN) that stores knowledge vectors and multiple Reasoners (Self-Attn) that iteratively achieve compositional reasoning.

Largest empirical study of AI penetration in academic publishing ever conducted—51%-by-2025 is the most authoritative quantitative marker yet of how thoroughly LLMs have reshaped scientific writing, a

Mooncake is the serving platform for Kimi developed by Moonshot AI, featuring a KVCache-centric disaggregated architecture that separates prefill and decoding clusters while leveraging underutilized C

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

Hi everyone, About a month ago I publish my very first research paper on my neural network architecture called Silia. You can look at the model here: https://huggingface.co/Srijan-Srivastava/Silia-v2

Figure 1b from our paper Disclosure: I'm one of the authors (Microsoft). The paper, code, dataset are public and ThinkingBox is on Hugging Face OpenEnv as well. Raw evaluation trajectories are not rel

Hi just wondering has anyone submitted anything to JMLR before? Especially in the last 2 years? What is the experience like? Background: I am a Comp Sci PhD student, but my secondary supervisor (the o

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

Reviews and discussions for all accepted papers at ICML are made public on OpenReview after the reviewing period concludes. Authors of rejected papers may also opt-in to have their reviews and discuss

This Reddit thread on r/MachineLearning discusses CVPR 2026's policy requiring accepted papers to be registered under an in-person author registration, with virtual attendance still permitted if circu

First: That the 7GB (int8) 2.1 comes even close to the 20GB (int8) 2511 is amazing, but maybe because of the smaller size some capabilities got lost. For some tasks 2.1 it is much better than 2511, bu

This Reddit thread on r/MachineLearning discusses the workshop paper accept/reject notifications for ECCV 2026, the European Conference on Computer Vision (ECCV), a biennial premier research conferenc

`citracer` is a small Python CLI tool available on PyPI that traces citation chains for any keyword across research papers, helping researchers identify where a concept originates in a citation gra...

Yesterday I released Bonsai-Llama-Jev - a Jev-like Typed Decision inference system for locally operated, sovereign AI. In my new typed-decision-bench, covering more than 22,000 decisions across 175 us

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

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

I've tested Nanbeige-4.2-3B. On paper, the benchmarks promise it blows away Qwen3.5-9B and Gemma4-12B. My goal was to have something very light and fast to replace Qwen3.6-35B (or finetunes thereof) f

This r/MachineLearning discussion thread addresses the common and frustrating experience of receiving highly polarizing reviewer scores for ICML paper submissions — for example, one reviewer rating a

Disclaimer: I work for NuMind, the company behind this plugin. TLDR: Text/image (simili-)embeddings from the LLM you already have loaded :-) I've been working on making it easier to obtain embeddings

I use ChatGPT a lot. Probably a lot more than is reasonable. I read David Robinson’s Atlantic piece today about leaving OpenAI and his criticism of its culture of trial-and-error / “iterative deployme