AI Research

Toward provably private learning from federated data

Mobile Systems

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.

STATERA: Hidden Mass Estimation via Zero-Shot Sim-to-Real Kinematics using Frozen Temporal Tubelets

arXiv:2610.00003v1 Announce Type: cross Abstract: Vision models pretrained for frame-level appearance often struggle to infer hidden physical properties from motion. We study center-of-mass (CoM) localization for…

SpikeMoE: Brain-Inspired Competitive Routing for Flexible Spiking Mixture-of-Experts

arXiv:2610.01418v1 Announce Type: new Abstract: Spiking Neural Networks (SNNs) enable event-driven computation through biologically inspired dynamics at the neuronal scale, while Mixture-of-Experts (MoE) perform…

CortexBridge: Cortical Alignment of EEG Montages for Foundation Models

arXiv:2610.01124v1 Announce Type: new Abstract: Electroencephalography (EEG) foundation models are often pretrained with a fixed channel vocabulary or a limited set of montages, making transfer difficult when electrode…

Grounding Large Language Models in DSGE Simulators for Policy Generation and Forecasting

arXiv:2610.01128v1 Announce Type: new Abstract: Large language models can produce economic policy responses that sound reasonable, but this does not show that their actions are consistent with economic dynamics. We test…

Completion Aware Guidance for World Action Models

arXiv:2610.01559v1 Announce Type: cross Abstract: World Action Models (WAMs) predict visual futures and robot actions, yet they remain susceptible to task-incomplete imagination, where plausible, action-consistent…

Mingbird: A Local-First Agent Harness Enabling Small Open Models to Complete Real Tasks

arXiv:2610.02001v1 Announce Type: new Abstract: Small open-weight models (2-9B) run on ordinary laptops, but under cloud-scale agent harnesses they rarely complete real tasks: tool prefill overflows the context,…

GUI-HARVEST: Self-Improving GUI Agents through Evidence-Driven Harness Evolution

arXiv:2610.00948v1 Announce Type: cross Abstract: The executable harness surrounding a GUI model determines how observations are assembled, actions are executed, and verification, recovery, and termination are…

Beyond State-of-the-Art: Standardising Environmental Impact Metrics for AI Research

arXiv:2610.01116v1 Announce Type: new Abstract: As the capabilities and ubiquity of Large Language Models (LLMs) grow, so does their environmental footprint. Despite calls for responsible AI, the machine learning…

T2SPO: Trajectory-to-Step Policy Optimization for Agentic Reinforcement Learning

arXiv:2610.00388v1 Announce Type: cross Abstract: Reinforcement learning enables large language model (LLM) agents to learn multi-step behaviors through interaction with their environments. However, rewards in many…

VISPA: Pluralistic Alignment via Automatic Value Selection and Activation

arXiv:2601.12758v2 Announce Type: replace-cross Abstract: As large language models are increasingly used in high-stakes domains, it is essential that their outputs reflect not average} human preference, rather range of…

JevSpawn: Adaptive Agentic Inference through Compositional Action Spaces

arXiv:2610.00437v1 Announce Type: new Abstract: LLM agents generate intermediate reasoning and actions token by token, making extended interactions slow and computationally expensive. Jev-style models offer fast…