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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 overcome parametric capacity limits by coupling deliberate internal thinking with active external tool use. To support this paradigm across a 256K context, we develop an end-to-end, high-efficiency open training recipe: Architecture Progressive Curriculum & MDP Mid-Training: context scaling across 16K, 64K, and 256K, and the reformulation of interaction traces into Markov Decision Processes. Furthermore, we establish an AI-native R&D workflow where agent swarms autonomously manage cluster operations, data curation, and rapid diagnostic evaluation. Extensive evaluations show that ZGCM-1-7B is competitive across 7B model family on general benchmarks. On several challenging mathematical reasoning a
ShieldVLA: Feasibility-Aware Safety Alignment for Vision-Language-Action Models
arXiv:2609.13231v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models demonstrate strong generalization in robotic manipulation and navigation, but existing fine-tuning methods provide limited safety guarantees. Current approaches primarily rely on Lagrangian optimization that enforces safety through soft penalties on expected cumulative cost, often resulting in residual constraint violations or overly conservative behavior. Moreover, learning safety in visual domains is challenging due to the absence of dense per-step safety annotations. We propose ShieldVLA, a safety-aligned fine-tuning framework for VLA models based on Hamilton-Jacobi (HJ) reachability. ShieldVLA learns a model-free approximation of the HJ reachability value function directly from visual observations to estimate the safe operating region. The learned safety critic gates policy optimization by separating reward maximization within feasible regions from recovery near unsafe states, avoiding persistent re
Seeing What the Vehicle Sees: Video-Augmented Virtual Reality for Physical Autonomous Vehicles
arXiv:2609.13224v1 Announce Type: new Abstract: Autonomous vehicles are expected to improve road safety and efficiency, but passengers often remain uncertain about what the vehicle perceives and why it acts as it does. Virtual reality (VR) offers a safe and repeatable medium for presenting this information, yet most passenger-facing VR studies rely on fully simulated vehicles or pre-scripted scenarios, so the motion and perception shown to the user do not originate from a physically operating autonomous system. This paper presents a video-augmented VR framework that couples a physical ROS 2 autonomous robot vehicle to a Unity 6 application deployed on a Meta Quest 3S headset. The vehicle state and live onboard camera stream are transmitted over two independent communication channels, allowing the virtual vehicle to mirror the physical robot's motion while the passenger simultaneously views the vehicle's first-person camera feed and its navigation decisions through an in-vehicle dashboa
How AI agents could change last mile delivery for businesses?
For years, the delivery van wasn t the bottleneck for last-mile delivery, though the decision behind it was. In the past, deliveries Continue reading on Medium »
Why Are AI Creations Always Half-Finished?
From being able to generate to being able to deliver the missing piece is not a smarter modelContinue reading on Medium »
The 400-Year-Old Logarithm Still Powering AI in 2026
A 1614 math shortcut called the logarithm quietly stabilizes cross-entropy loss, log probabilities, and perplexity in every modern AI Continue reading on Generative AI »
GitHub HydraFusion: The AI Coding System That Uses Multiple Models to Build Better Software
GitHub is moving beyond choose the best AI model. HydraFusion asks a much more interesting question: What if the best AI model is Continue reading on CodeToDeploy »
Valve is still figuring out ‘how and when’ to do Steam Deck 2
Now that Valve has finally launched its entire 2026 hardware lineup - the Steam Controller, the Steam Machine, and today's Steam Frame - are we any closer to a next-gen Steam Deck handheld? Valve isn't saying so yet. The company is figuring out "how and when we can deliver something like that," Valve designer Pierre-Loup Griffais now tells IGN. Valve has been clear for years that 1) a Steam Deck 2 will indeed happen, and 2) it's not coming until Valve finds the right chip. The original Steam Deck worked because it delivered the right combination of price, performance, and battery life, and Valve has repeatedly said it won't sacrifice effici … Read the full story at The Verge.
Why AI Agents Need Serverless Postgres: Inside the Lakebase Architectural Shift
The next generation of autonomous AI requires persistent memory, fast vector lookups, and instant sandbox environments. Here is how Continue reading on Medium »
20 years ago I was already writing about ideas we would now file under “Agentic AI”, agent-to-agent…
But before I take you through that story, let me tell you why this matters right now.Continue reading on Medium »
Show HN: Macros with a Behringer FCB1010 MIDI Pedalboard in macOS
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Is Big Tech’s AI slowdown a safety pact or a cartel?
When OpenAI CEO Sam Altman, Anthropic CEO Dario Amodei, Google DeepMind cofounder Demis Hassabis, and SpaceX head Elon Musk loosely agreed over the weekend to slow down AI development, skeptics spotted an ulterior motive immediately. The AI titans had declared that their aim was to "pace the frontier," signing on at least partially to a proposal for embedding third-party auditors, regulating domestic labs, and reaching a global slowdown agreement. Their critics, however, argued they simply wanted to stop would-be competitors, kneecap the open-source movement, and avoid real legal safeguards - some dubbed it an outright "cartel." The truth … Read the full story at The Verge.