Hi everyone, I’ve been working on a personal project which I’d like some help with. Its an LLM based CLI tool to explore MLflow logs. One thing I really want for testing purposes is data. I’ve tried looking for MLflow db files online, but I guess people don’t really push them to github. I’m currently working with some dummy data that I generated, but I would really like people to use it or share any databases with me […]
arXiv:2602.10139v1 Announce Type: new Abstract: Mobile Graphical User Interface (GUI) agents have demonstrated strong capabilities in automating complex smartphone tasks by leveraging multimodal large language models (MLLMs) and system-level control interfaces. However, this paradigm introduces significant privacy risks, as agents typically capture and process entire screen contents, thereby exposing sensitive personal data such as phone numbers, addresses, messages, and financial information. Existing defenses either reduce UI exposure, obfuscate only task-irrelevant content, or rely on user authorization, but none […]
In many economically relevant contexts where machine learning is deployed, multiple platforms obtain data from the same pool of users, each of whom selects the platform that best serves them. Prior work in this setting focuses exclusively on the "local" losses of learners on the distribution of data that they observe. We find that there exist instances where learners who use existing algorithms almost surely converge to models with arbitrarily poor global performance, even when models with low […]
I spent years calling models via API : Bedrock, Sagemaker, Anthropic. This week was about understanding what’s actually happening inside the box, and why that matters for building systems around it. Coming into this project, I had built production AI systems on AWS Bedrock using flows, agents. I knew how to wire models into applications. What I didn’t know was what the models were actually doing with the text I gave them, why RAG works at all, or […]
Designing algorithms for Multi-Agent Reinforcement Learning (MARL) in imperfect-information games — scenarios where players act sequentially and cannot see each other’s private information, like poker — has historically relied on manual iteration. Researchers identify weighting schemes, discounting rules, and equilibrium solvers through intuition and trial-and-error. Google DeepMind researchers proposes AlphaEvolve, an LLM-powered evolutionary coding agent that replaces that manual process with automated search. The research team applies this framework to two established paradigms: Counterfactual Regret Minimization (CFR) and […]
MLP is a heavily used backbone in modern deep learning (DL) architectures for supervised learning on tabular data, and AdamW is the go-to optimizer used to train tabular DL models. Unlike architecture design, however, the choice of optimizer for tabular DL has not been examined systematically, despite new optimizers showing promise in other domains. To fill this gap, we benchmark 15 optimizers on 17 tabular datasets for training MLP-based models in the standard supervised learning setting under a […]
Last announcement-style post for a little while, but I figured this was worthy of a standalone update about the SOTA Toolkit. The first three release repositories are now fully governed under GPLv3, along with the Hugging Face and Ollama variants of the recently released artifact: qwen3-pinion / qwen3-pinion-gguf. All repositories for Operation / Toolkit-SOTA have retired the Somnus License, and all current code/tooling repositories are now fully governed by GPLv3. Drop #1: Reinforcement-Learning-Full-Pipeline Drop #2: SOTA-Runtime-Core (Neural Router […]
How are you, hacker? 🪐Want to know what’s trending right now?: The Techbeat by HackerNoon has got you covered with fresh content from our trending stories of the day! Set email preference here. ## Pretext Does What CSS Can’t — Measuring Text Before the DOM Even Exists By @typesetting [ 8 Min read ] Cheng Lou’s Pretext library measures multiline text height without touching the DOM — unlocking layout capabilities CSS has never been able to offer. Read […]
arXiv:2602.05174v1 Announce Type: new Abstract: Riemannian flow matching (RFM) extends flow-based generative modeling to data supported on manifolds by learning a time-dependent tangent vector field whose flow-ODE transports a simple base distribution to the data law. We develop a nonasymptotic Total Variation (TV) convergence analysis for RFM samplers that use a learned vector field together with Euler discretization on manifolds. Our key technical ingredient is a differential inequality governing the evolution of TV between two manifold ODE flows, […]
arXiv:2601.20060v1 Announce Type: new Abstract: Let $P$ be a generic set of $n$ points in the plane, and let $P=Rcup B$ be a coloring of $P$ in two colors. We are interested in the number of crossings between the minimum spanning trees (MSTs) of $R$ and $B$, denoted by $crossAB(R,B)$. We define the emph{bicolored MST crossing number} of $P$, denoted by $cross(P)$, as $cross(P) = max_{P= Rcup B}(crossAB(R,B))$. We prove a linear upper bound for $cross(P)$ when $P$ […]