Curiosity-Critic: Cumulative Prediction Error Improvement as a Tractable Intrinsic Reward for World Model Training
arXiv:2604.18701v1 Announce Type: new
Abstract: Local prediction-error-based curiosity rewards focus on the current transition without considering the world model's cumulative prediction error across all visited transitions. We introduce Curiosity-Critic, which grounds its intrinsic reward in the i...
Beyond One Output: Visualizing and Comparing Distributions of Language Model Generations
arXiv:2604.18724v1 Announce Type: new
Abstract: Users typically interact with and evaluate language models via single outputs, but each output is just one sample from a broad distribution of possible completions. This interaction hides distributional structure such as modes, uncommon edge cases, an...
ARES: Adaptive Red-Teaming and End-to-End Repair of Policy-Reward System
arXiv:2604.18789v1 Announce Type: new
Abstract: Reinforcement Learning from Human Feedback (RLHF) is central to aligning Large Language Models (LLMs), yet it introduces a critical vulnerability: an imperfect Reward Model (RM) can become a single point of failure when it fails to penalize unsafe beh...
AI scientists produce results without reasoning scientifically
arXiv:2604.18805v1 Announce Type: new
Abstract: Large language model (LLM)-based systems are increasingly deployed to conduct scientific research autonomously, yet whether their reasoning adheres to the epistemic norms that make scientific inquiry self-correcting is poorly understood. Here, we eval...
Quantum inspired qubit qutrit neural networks for real time financial forecasting
arXiv:2604.18838v1 Announce Type: new
Abstract: This research investigates the performance and efficacy of machine learning models in stock prediction, comparing Artificial Neural Networks (ANNs), Quantum Qubit-based Neural Networks (QQBNs), and Quantum Qutrit-based Neural Networks (QQTNs). By outl...
Hugging Face Releases ml-intern: An Open-Source AI Agent that Automates the LLM Post-Training Workflow
Hugging Face has released ml-intern, an open-source AI agent designed to automate end-to-end post-training workflows for large language models (LLMs). Built on the company’s smolagents framework, the tool can autonomously perform literature review, dataset discovery, training script execution, and i...
A Coding Implementation to Build a Conditional Bayesian Hyperparameter Optimization Pipeline with Hyperopt, TPE, and Early Stopping
In this tutorial, we implement an advanced Bayesian hyperparameter optimization workflow using Hyperopt and the Tree-structured Parzen Estimator (TPE) algorithm. We construct a conditional search space that dynamically switches between different model families, demonstrating how Hyperopt handles hie...
AI Weekly Issue #486: Apple is replacing Tim Cook because of AI
Last week you voted
We asked how you'd like deep dives delivered. 165 votes — nearly half (47.9%) want separate emails per topic, 38.8% want one weekly digest, 13.3% have no preference. The split means we'll do both: pick your setup in Preferences. That part's live.
See full results →
OpenAI Privacy Filter is an open-weight model for detecting and redacting personally identifiable information (PII) in text with state-of-the-art accuracy
OpenAI makes ChatGPT for Clinicians free for verified U.S. physicians, nurse practitioners, and pharmacists, supporting clinical care, documentation, and research.
When ChatGPT launched as an experimental prototype in late 2022, OpenAI’s chatbot became an everyday everything app for hundreds of millions of people. LLMs like ChatGPT were the new future: The entire tech industry was consumed by the inferno, with companies racing to spin up rival products. The as...
When ChatGPT was released to the public in late 2022, it opened people’s eyes to how easily generative AI could churn out vast amounts of human-seeming text from simple prompts. This quickly caught the attention of criminals, who soon began using large language models to produce malicious emails—bot...
AI systems have already gained impressive mastery over the digital world, but the physical world is still humanity’s domain. As it turns out, building an AI system that can compose a novel or code an app is far easier than developing one that can fold laundry or navigate a city street. To get there,...
DIY AI & ML: Solving The Multi-Armed Bandit Problem with Thompson Sampling
How you can build your own Thompson Sampling Algorithm object in Python and apply it to a hypothetical yet real-life example
The post DIY AI & ML: Solving The Multi-Armed Bandit Problem with Thompson Sampling appeared first on Towards Data Science.
As AI agents increasingly work alongside humans across organizations, companies could be inadvertently opening a new attack surface. Insecure agents can be manipulated to access sensitive systems and proprietary data, increasing enterprise risk. In some modern enterprises, non-human identities (NHI)...
Google Introduces Simula: A Reasoning-First Framework for Generating Controllable, Scalable Synthetic Datasets Across Specialized AI Domains
Training powerful AI models depends on one resource that is quietly running out: specialized data. While the internet provided a seemingly infinite supply of text and images to train today’s generalist models, the next wave of AI breakthroughs — in cybersecurity, legal reasoning, healthcare, and oth...
Bond, a new social media platform, wants to use AI to help you kick your doomscrolling habit
Bond wants you to get off the couch and get back into the real world, its creator says. The new platform's AI system is designed to motivate users to do things away from the app.
As AI agents grow more autonomous, trust can't rely on logs alone. In this this article, I explore how cryptographic techniques — from content-addressed code to tamper-evident audit trails — are laying the groundwork for a new era of verifiable, auditable AI.