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Where the Agent Development Lifecycle Fits

Coordinating agent capability development with the application it powers The post Where the Agent Development Lifecycle Fits appeared first on Towards Data Science.

How to Build a Control Plane for AI Agents

Giving an LLM permission to act, in 9 steps The post How to Build a Control Plane for AI Agents appeared first on Towards Data Science.

😺 48% thought Tavus’s AI was human

Meta’s Muse Hit 5 Million Downloads. The Bigger Story Is How It Got There

Meta’s Muse reached an estimated 5 million downloads in just 22 days, helped by one of the biggest distribution machines in tech. The bigger test is whether Meta can turn that reach into lasting control over how people…

Investigators Found a New Problem With OpenAI’s Rogue Agents: Missing Evidence

OpenAI’s latest rogue-agent story is less about a proven government hack than a growing accountability problem. As agents stitch together outside services, the evidence needed to investigate their behavior can become…

OpenAI Agent Security: The New Rules for Containing AI Agents

An OpenAI Agent Security engineer says frontier AI containment now depends on layered isolation, permissions, monitoring, alignment, and incident-response culture. The Hugging Face incident shows why.

AI Skill of the Day Digest — September 2026 (Part 2)

Explore 13 practical AI skills from September 16–30, with workflows for ChatGPT, Claude, Gemini, Jev, Codex, and AI agents.

Can Light Make Deepfake Detection Cheaper? UCLA Researchers Think So

UCLA researchers are testing a hybrid optical-neural system that screens 15 videos in parallel. The prototype reached 97.79% accuracy on Celeb-DF and could reduce the computing demands of initial deepfake screening.

Learning to Plan from Random Exploration

arXiv:2609.38383v1 Announce Type: new Abstract: Random exploration reveals how an environment can be traversed before a goal is specified. Can this experience support long-range planning without policy-improvement…

Switching Linear Attention

arXiv:2609.39034v1 Announce Type: new Abstract: Designing expressive sequence layers with efficient inference remains a central challenge in modern machine learning. Standard softmax attention achieves excellent…

Does Learning Protein Folding Generalize to Broader Reasoning?

arXiv:2609.38879v1 Announce Type: new Abstract: Large language models rely heavily on human text, which often conveys surface answers rather than the spatial and structural logic behind them. Protein folding is a…

BayesNDE: Bayesian Generative Modeling for Neural Density Estimation

arXiv:2609.39843v1 Announce Type: cross Abstract: Density estimation is a fundamental problem in statistics and machine learning. In this work, we introduce BayesNDE, a neural density estimator based on Bayesian…

TaxDistill: Improving Metagenomic Taxonomic Annotation via Distilled Genomic Foundation Models

arXiv:2605.28868v2 Announce Type: replace Abstract: Metagenomic taxonomic annotation is essential for interpreting complex microbial communities, yet reliable annotation remains challenging under reference database…

Towards Universal Wasserstein Barycenters through Flow Matching

arXiv:2609.38547v1 Announce Type: new Abstract: Defining a weighted mean over probability measures under probability metrics is a central tool in probabilistic machine learning. Under the Wasserstein metric, these are…

RSIGame: Autonomous Agentic Game Development with Recursive Self-improvement

arXiv:2609.39045v1 Announce Type: cross Abstract: Recent advances in large language models have made automatic game generation increasingly feasible, yet reliably improving generated games beyond a playable version…

Sharp Statistical Rates for Asynchronous TD Learning with Markovian Data

arXiv:2609.38880v1 Announce Type: cross Abstract: We study the last iterate of standard tabular temporal-difference (TD) learning from a single trajectory of a finite Markov reward process. For discount factor $\gamma$,…

NeuroAtlas: Benchmarking Foundation Models for Clinical EEG and Brain-Computer Interfaces

arXiv:2605.14698v2 Announce Type: replace Abstract: Foundation models (FMs) promise to extract unified representations that generalize across downstream tasks. They have emerged across fields, including…

Sharp Stationary Gaussian Approximation for Constant-Stepsize SGD

arXiv:2609.39144v2 Announce Type: new Abstract: We prove a sharp Gaussian approximation for the invariant law of constant-stepsize SGD with bounded additive noise generated by an exogenous uniformly ergodic Markov…

Patient-Centered Treatment Planning for Chronic Multimorbidity: A Hierarchical Reinforcement Learning Framework for Preference Modeling

arXiv:2609.39911v1 Announce Type: new Abstract: Patient preference, defined as a patient's demonstrated willingness and capacity to adhere to clinical recommendations, is a primary determinant of therapeutic effect yet…