Reading through the release notes for the 397B-A17B model. The active parameter count is incredibly low for its overall size. Do you guys think this specific MoE routing is a major breakthrough for open source, or is it just a natural, incremental step up from what we already had? submitted by /u/astrophile_ashish [link] [comments]
Key Highlights: AI companies are pushing agent-based browsers, with the aim of transforming web browsing like never before. Companies like OpenAI, Perplexity, and others have already dropped their AI browsers. Well, it’s hard to tell whether Google will drop something similar. Instead, the company is looking to integrate its AI assistant, Gemini, to help users browse the internet more smartly. Google is testing a new Gemini-powered “Auto Browse” feature to automate Chrome browsing According to a report by […]
Made with Paperglide ✨ — digest research papers faster TL;DR: Researchers have discovered that AI models can learn complex math and reasoning by changing as few as 13 individual parameters, which is roughly the amount of data in a single short text message. While traditional training requires the AI to memorize exact examples, this method uses a “reward-based” system that teaches the model to focus only on getting the right answer rather than copying a specific style. This […]
In API-first systems, poor data quality (invalid emails, duplicate records, etc.) creates unpredictable bugs, forces defensive coding, and makes releases feel risky. This “hidden tax” consumes time and mental energy that should go to building features. The fix? Treat data quality as core infrastructure. By using real-time validation APIs at the point of ingestion, you create predictable systems, simplify business logic, and build developer confidence. This turns a vicious cycle of complexity into a virtuous cycle of velocity […]
arXiv:2601.05227v1 Announce Type: new Abstract: I propose a novel framework that integrates stochastic differential equations (SDEs) with deep generative models to improve uncertainty quantification in machine learning applications involving structured and temporal data. This approach, termed Stochastic Latent Differential Inference (SLDI), embeds an It^o SDE in the latent space of a variational autoencoder, allowing for flexible, continuous-time modeling of uncertainty while preserving a principled mathematical foundation. The drift and diffusion terms of the SDE are parameterized by neural […]
I am reviewing CVPR paper this season and have found out that authors have included an “external link” to the paper which is a clear violation of the CVPR submission guidelines. I also confirmed that authors have checked the “No external link checkbox” clearly stating: I confirm that the paper submission and supplementary material contain no external links intended to expand content… Guidelines says: Authors are not allowed to include external links (e.g., to webpages, images, or videos) […]
arXiv:2602.24207v1 Announce Type: cross Abstract: The use of algorithmic predictions in decision-making leads to a feedback loop where the models we deploy actively influence the data distributions we see, and later use to retrain on. This dynamic was formalized by Perdomo et al. 2020 in their work on performative prediction. Our main result is an unconditional reduction showing that any no-regret algorithm deployed in performative settings converges to a (mixed) performatively stable equilibrium: a solution in which models […]
Mid-January saw insurance company, Travelers, announce a new deal that empowers 10,000 engineers and data scientists with AI assistants. However, less than two weeks on, Travelers’ leadership explained that the company’s true competitive advantage lies in expertise, not AIs alone, believing this is what will drive longer-term profit growth. According to Travelers’ chief executive officer Alan Schnitzer, over 20,000 professionals at the company currently “use AI tools regularly.” He also commented on company claims that Travelers’ call centres […]
Distributed Federated Learning (DFL) enables decentralized model training across large-scale systems without a central parameter server. However, DFL faces three critical challenges: privacy leakage from honest-but-curious neighbors, slow convergence due to the lack of central coordination, and vulnerability to Byzantine adversaries aiming to degrade model accuracy. To address these issues, we propose a novel DFL framework that integrates Byzantine robustness, privacy preservation, and convergence acceleration. Within this framework, each device trains a local model using a Bayesian approach […]
The standard post-training recipe for large reasoning models, supervised fine-tuning followed by reinforcement learning (SFT-then-RL), may limit the benefits of the RL stage: while SFT imitates expert demonstrations, it often causes overconfidence and reduces generation diversity, leaving RL with a narrowed solution space to explore. Adding entropy regularization during SFT is not a cure-all; it tends to flatten token distributions toward uniformity, increasing entropy without improving meaningful exploration capability. In this paper, we propose CurioSFT, an entropy-preserving SFT […]