Designing microrobots with embodied physical intelligence
Science Robotics, Volume 11, Issue 113, April 2026.
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From autonomy to alliance: Robotic foundation models must learn with us, not just for us
Science Robotics, Volume 11, Issue 113, April 2026.
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Object-centric task representation and transfer using diffused orientation fields
Science Robotics, Volume 11, Issue 113, April 2026.
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INT3 compression+fused metal kernels [R]
Hey guys, I am a researcher and solo founder. I compress models with INT3 at +0.14 nats and built a 2-bit KV cache for long-horizon tasks. I shipped both (INT3 model + INT2 KV) with custom fused Metal kernels for Mac (M-series). Currently Qwen 7B is available in preview. #install brew install reinforceai/spiral/spiral #chat spiral-chat I am optimizing kernels further and working on Triton kernels for GPU support. There is still more room to pack more efficiently, I […]
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Machine Learning for Two-Stage Graph Sparsification for the Travelling Salesman Problem
High-performance TSP solvers like LKH search within a sparsified candidate graph rather than over all possible edges. Graph sparsification is non-trivial: keep too many edges and the solver wastes time; cut too many and it loses edges that belong to the optimal tour. The two leading heuristic methods, $α$-Nearest and POPMUSIC, produce high-quality candidate graphs, but no single heuristic is both sparse and reliable across all instance sizes and distributions. Machine learning methods can potentially learn better sparsification […]
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Physics Informed Neural Networks for the Unsteady Taylor Green Vortex with DeepXDE
The Taylor Green vortex is a classic benchmark for unsteady incompressible flows, but most existing physics-informed neural network (PINN) studies on this problem report only early time results or omit the pressure field entirely. We present a complete, step by step PINN simulation of the two-dimensional decaying vortex at Reynolds number 100 using the DeepXDE library. A fully connected network with four hidden layers of 128 neurons each is trained on 20,000 collocation points, 2,000 boundary points, and […]
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AI QA Testing: The Missing Link in Your AI Development Workflow
With tools like Cursor, GitHub Copilot, Claude Code, tasks that used to sit on developers’ to-do lists get solved much faster than before. On the development side, everything has sped up. But QA processes haven’t picked up the pace. Someone still has to write Playwright tests from scratch, maintain selectors that break with every UI change, and debug failures manually. Any manual QA work done while code is being generated slows releases nearly to a halt.. In this […]
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Why Apache SeaTunnel Zeta Can Be Both “Fast and Stable”
If SeaTunnel Zeta is simply understood as “a faster execution engine,” its true value will be underestimated. For data integration systems, the real challenge has never been “whether the pipeline can run,” but whether the following can be achieved at the same time: sufficiently high throughput, recoverability after failure, no data duplication or loss, and controlled resource consumption. What makes Zeta worth serious attention lies exactly here: it does not win through a single performance optimization, but instead […]
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Vibrotactile Preference Learning: Uncertainty-Aware Preference Learning for Personalized Vibration Feedback
Individual differences in vibrotactile perception underscore the growing importance of personalization as haptic feedback becomes more prevalent in interactive systems. We propose Vibrotactile Preference Learning (VPL), a system that captures user-specific preference spaces over vibrotactile parameters via Gaussian-process-based uncertainty-aware preference learning. VPL uses an expected information gain-based acquisition strategy to guide query selection over 40 rounds of pairwise comparisons of overall user preference, augmented with user-reported uncertainty, enabling efficient exploration of the parameter space. We evaluate VPL in […]
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