AI Tools & Frameworks Directory

A practical walkthrough of building and deploying a multistage, multimodal recommender system on Amazon EKS, covering data pipelines, model training, Bloom filters, feature caching, and real-time ranking. The post Deploying a Multistage Multimodal Recommender System on Amazon Elastic Kubernetes Serv...

Google DeepMind is integrating Street View with Project Genie to create immersive, interactive world simulations for robotics, gaming, and travel, allowing users to explore environments, weather changes, and rare scenarios.

Google launched Gemini 3.5 Flash, its most powerful coding and agentic AI model yet, at the company's annual developer conference. It is capable of autonomously executing complex tasks and building software from scratch.

Google is transforming Search from a list of links into an AI-powered experience filled with conversational answers, autonomous agents, and interactive interfaces — a shift that could further reduce traffic to publishers across the web.

The updates signal Google’s push to turn its Gemini Gemini app into an all-purpose AI hub rather than a standalone chatbot.

Google unveiled new web-based AI tools that can generate native Android apps in minutes, as the company expands its push into AI-powered software development.

a series of colorful icons representing Google I/O against a black background

Why production LLM systems need live web search to overcome knowledge cutoffs and stale training data The post Grounding LLMs with Fresh Web Data to Reduce Hallucinations appeared first on Towards Data Science.

Another day, another example of an AI Agent “running rogue” and doing something the human operator didn’t want it to do. The tl;dr is that Jeremy (Jer) Crane, founder of PocketOS, was using Claude to perform some routine DB maintenance. Claude then proceeded to delete the production database and all...

Discover how AI copilots are creating a new class of micro-executives—individuals who operate with the leverage, speed, and output of entire departments.

You have probably spent time learning how to prompt AI well.

What if the model you've been evaluating has been evaluating you right back? New research finds that LLMs systematically alter their output depending on whether, and by whom, they believe they are being observed. It might have serious implications - are you ready?

Companies everywhere are trying to leverage AI to boost internal productivity metrics. Some, like Ramp and Intercom, are succeeding. Many are failing. To make matters more complicated, the narrative around what tooling enables these gains is constantly shifting. For software engineers, auto-complete...

OpenAI advances AI content provenance with Content Credentials, SynthID, and a verification tool to help people identify and trust AI-generated media.

arXiv:2605.16259v1 Announce Type: new Abstract: While real-time image generation using diffusion models has advanced rapidly on NVIDIA GPUs, systematic optimization research on non-CUDA platforms such as Apple Silicon remains extremely limited. In this study, we conducted comprehensive optimization...

arXiv:2605.16262v1 Announce Type: new Abstract: Variational inequalities play a key role in machine learning research, such as generative adversarial networks, reinforcement learning, adversarial training, and generative models. This paper is devoted to the constrained variational inequality proble...

arXiv:2605.16302v1 Announce Type: new Abstract: Reinforcement learning for multi-step reasoning with large language models (LLMs) often relies on sparse terminal rewards, leading to poor credit assignment conditions where the final feedback is evenly propagated across all intermediate decisions. Th...

arXiv:2605.16311v1 Announce Type: new Abstract: Distributed training of large neural networks is bottlenecked by full-precision gradient communication and by coordinatewise optimizers that ignore the matrix structure of weight tensors. We propose Sign-Muon, a 1-bit, matrix-aware optimizer that comb...

arXiv:2605.16312v1 Announce Type: new Abstract: We study adversarial action masking in self-play reinforcement learning: an attacker selectively removes legal actions from a victim's action set. Unlike observation or action perturbations, removal eliminates decision options before the agent acts. A...

arXiv:2605.16265v1 Announce Type: new Abstract: The safety of autonomous AI agents is increasingly recognized as a critical open problem. As agents transition from passive text generators to active actors capable of executing shell commands, modifying files, calling APIs, and browsing the web, the ...

arXiv:2605.16309v1 Announce Type: new Abstract: LLM-based agents can recover from individual execution errors, yet they repeatedly fail on the same fault when the underlying process knowledge--operator schemas, preconditions, and constraints--remains unrepaired. Existing self-evolving approaches ad...

arXiv:2605.16552v1 Announce Type: new Abstract: Automating science laboratories enables faster, safer, more accurate, and more reproducible execution of protocols, accelerating the discovery and testing of new materials, drugs, and more. However, setting up and running autonomous labs requires coor...

arXiv:2605.16565v2 Announce Type: new Abstract: Skim is a speculative execution framework for web agents that exploits the predictable structure of purpose-built websites. Today's web-agent expense is not intrinsic to the tasks but a property of how agents are composed: frontier-model inference, br...