Early Prediction of Satellite Collision Probability Using a Hybrid TCN-Transformer Model for a CDM-Based Conjunction Analysis Framework
arXiv:2609.13191v1 Announce Type: new
Abstract: The rapid expansion of operational satellites and orbital debris has increased the frequency of close approach events in low Earth orbit (LEO), creating a higher operational burden for satellite operators. This problem is especially critical for satel...
ZGCM-1: A Fully Open and Extremely Efficient Foundation Model for Math and Agentic Search
arXiv:2609.13356v1 Announce Type: new
Abstract: In this work, we present ZGCM-1, a fully open 7B dense foundation model trained from scratch with extreme data, system, and algorithmic efficiency. ZGCM-1 is founded on a core premise: compact models cannot passively memorize the open web, but can ove...
Generalized Agent Iteration: One Formal Framework for Iterative Policy Improvement and Recursive Self-Improvement
arXiv:2609.13406v1 Announce Type: new
Abstract: When we speak of recursive self-improvement (RSI), are we speaking of a phenomenon, a mechanism, or a prospect? Towards autonomous and evolving intelligence, RSI is being claimed at many scales, while no single framework that formally describes these ...
Vibe Patenting: Evaluating LLM Judges for Professional Patent-Drafting Agents
arXiv:2609.13422v1 Announce Type: new
Abstract: LLM judges are increasingly used to evaluate and improve AI-generated outputs, yet their reliability for complex professional work remains unclear. We study this problem through Vibe Patenting, an end-to-end patent-drafting testbed for AI-agent evalua...
arXiv:2609.13436v1 Announce Type: new
Abstract: Large Language Model (LLM) agents offer a promising path toward autonomously managing long-term physical tasks without human intervention. However, physical tasks require agents to continuously observe the environment, make consequential actions, and ...
Reward AI Releases OM-1: A Robot Policy Trained on Human Demonstrations Only, With No Teleoperation or On-Robot Data
Reward AI has released OM-1 (Omnibody Model 1), a general-purpose manipulation policy trained entirely on human demonstrations captured with a 7-DoF wearable glove, with no teleoperation or on-robot data. The policy runs on industrial arms and humanoids at human speed, learns a new task from under 3...
First in a series on probabilistic forecasting for physical signals. Next: what happens when you roll the forecast forward more than one step.
The post Your Model’s MSE Is Lying to You appeared first on Towards Data Science.
The AI industry has taken a doomer turn. What now?
This story appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. This weekend, Dario Amodei, CEO of Anthropic, posted an essay calling for a brake on the pace of development of LLMs. Amodei cites the looming dangers he sees from the techn...
When to Use One Model and When to Use a Team of Agents
When Codex is the right shape for the problem, when Claude Code is, and how I split 5 specialist agents between them on dense AI capacity work.
The post When to Use One Model and When to Use a Team of Agents appeared first on Towards Data Science.
Microsoft’s new AI ‘code of conduct’ tells models not to hack systems or trick humans
The code of conduct lays out general principles that Microsoft AI models should uphold — supporting humans rather than replacing them, for instance, and accelerating human flourishing — as well as specific safety constraints meant to implement those principles.
AI agents blew the whistle on their cheating colleagues
A group of AI agents asked to solve a series of math problems split into rival factions—when some cheated, others tried to stop them. That whistleblowing behavior, seen for the first time in a recent experiment run by Google DeepMind, could have implications for alignment researchers trying to keep ...
Perplexity Portable Computer Is Now Available on Windows, Powered by NVIDIA RTX
As local models become more capable, AI agents can handle more work directly on a PC while keeping sensitive information on the device. Portable Computer is a local version of the agent Perplexity Computer that plans and carries out multistep tasks. Accelerated by NVIDIA GPUs, it uses local models t...
Only at TechCrunch Disrupt 2026: What happens when OpenAI ships your roadmap?
If you're building an AI company, the question isn't whether foundation models will continue to evolve. It's whether your company will continue creating value as they do. The greatest risk isn't building a weak product — it's building a strong one that eventually becomes someone else's feature. You ...
Graph Engineering for AI Agents: From Prompts and Loops to Workflows
A viral debate over loops versus graphs points to a bigger shift in how we build AI systems. Here’s what graph engineering actually means, how it differs from prompt, context, and loop engineering, and why it matters.
The post Graph Engineering for AI Agents: From Prompts and Loops to Workflows appe...
From Static to Dynamic Skills: A Different Model for Agent Knowledge
Why the skill-inflation panic is aimed at the wrong thing, and what it costs to make agent knowledge a build artifact instead of a file.
The post From Static to Dynamic Skills: A Different Model for Agent Knowledge appeared first on Towards Data Science.
Enterprise Analytics Beyond Dashboards: Intelligent Data Orchestration with LLMs
In 17 years of building enterprise data platforms, I’ve watched every organization eventually ask the same question: “Can I ask one question and get one answer across everything my company knows?” A finance analyst wants actual revenue from the warehouse, pipeline data from the CRM, commentary from ...
NVIDIA Open-Sources OSMO: One YAML Orchestrates Physical AI Training, Simulation, and Robot Testing
NVIDIA has open-sourced OSMO, the Kubernetes-native workflow orchestrator it uses internally for Project GR00T, Isaac Lab, and Isaac Sim. OSMO lets robotics teams define training, simulation, and hardware-in-the-loop tasks in a single YAML file and routes each one to the right compute tier, from GB2...
Fed-Equilibrium Framework for Topological Pareto Control in Robust and Fair Clinical Federated Learning
arXiv:2609.11937v1 Announce Type: new
Abstract: The deployment of Federated Learning (FL) in multi-center clinical networks faces the challenge of "knowledge dominance," where high-volume hubs naturally overwhelm minority community nodes, implicitly treating the distinct clinical patterns of smalle...
Efficient AI Model Deployment Using Quantization Analysis Tool
arXiv:2609.11954v1 Announce Type: new
Abstract: As deep learning models are increasingly deployed on resource constrained devices, the demand for efficient model optimization techniques continues to grow. Effective deployment of AI models on edge and low power platforms requires optimization method...
Performance, Efficiency and Collapse -- Advantages and Challenges in Offline Post-training of Code LLMs
arXiv:2609.11956v1 Announce Type: new
Abstract: Post-training with reinforcement learning (RL) is a critical phase in the development of code-generating large language models (LLMs), as it ensures adherence to instructions and the production of functionally correct code. This process typically requ...