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Toward provably private learning from federated data

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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.

A Multi-Agent LLM Framework for Personalized Health Checkup Interpretation and Guidance

arXiv:2610.01451v1 Announce Type: new Abstract: Personalized interpretation of health checkup results requires reasoning across longitudinal records, medical knowledge, lifestyle guidance, and healthcare navigation. We…

External Observers May See More Clearly: Cross-Model Span-Level Hallucination Detection in Large Language Models via Hidden State Probing

arXiv:2610.02066v1 Announce Type: new Abstract: As Large Language Models (LLMs) increasingly serve as foundational reasoning engines, their tendency to hallucinate remains a critical vulnerability. While recent internal…

Outer Diversity of Condorcet Domains

arXiv:2610.00720v1 Announce Type: cross Abstract: A Condorcet domain is a set of rankings over a given candidate set, such that every election that consists only of (an odd number of) votes from the domain has a…

SCOPE-AD: Sequential cost-aware ordinal-belief planning with energy-based models for diagnostic agents

arXiv:2610.01278v1 Announce Type: new Abstract: Alzheimer's disease (AD) diagnosis requires sequential evidence acquisition under heterogeneous test costs and patient burden. Fixed-modality predictors do not jointly…

Capabilities Ain't All You Need: Measuring Propensities in AI

arXiv:2602.18182v5 Announce Type: replace-cross Abstract: AI evaluation has primarily focused on measuring capabilities, with formal approaches inspired from Item Response Theory (IRT) being increasingly applied. Yet…

HHR: Hierarchical Hash Retrieval for Efficient LLM Generation

arXiv:2610.01230v1 Announce Type: new Abstract: Efficient long-context inference is essential for large language models (LLMs), yet it poses a severe computational bottleneck. Hash-based retrieval offers an efficient…

Robust Is Salient: An Informed Adversary Moves the Optimal Signal onto the Salience Pole

arXiv:2610.00233v1 Announce Type: new Abstract: When an informed adversary shares the audience of a constrained signalling channel, the signal that best protects the truth is the signal that best describes it. On 108…

TopK-Guided: Adaptive, Budget-Aware Activation Sparsity for Efficient LLM Inference

arXiv:2610.01763v1 Announce Type: new Abstract: Activation sparsity speeds up large language model (LLM) inference by setting unimportant activations to zero so that the corresponding computations can be skipped.…

Not All Error Yields to Scale: Where Scaling Stops in Vision-Language Inference

arXiv:2610.01640v1 Announce Type: cross Abstract: Vision-language models (VLMs) face a fixed-budget trade-off between processing more visual information for fine-grained perception and using a larger language backbone…

Increasing Width Allows Greedy Layer-wise Training to Rival End-to-End Backpropagation in Self-Supervised Learning

arXiv:2610.00753v1 Announce Type: cross Abstract: End-to-end backpropagation has been the dominant mode of training in deep learning, allowing for the coordination of parameter updates across layers of a neural network.…

RelationVGGT: Visual Geometry Transformers for 3D Spatial Relation Segmentation

arXiv:2610.00970v1 Announce Type: cross Abstract: Recent advances in 3D reconstruction have progressed from per-scene optimization to feed-forward inference, and semantic scene understanding has followed suit -- yet…