arXiv:2602.10611v1 Announce Type: cross Abstract: Physics-informed neural networks (PINNs) have gained significant attention as a surrogate modeling strategy for partial differential equations (PDEs), particularly in regimes where labeled data are scarce and physical constraints can be leveraged to regularize the learning process. In practice, however, PINNs are frequently trained using experimental or numerical data that are not fully consistent with the governing equations due to measurement noise, discretization errors, or modeling assumptions. The implications of such data-to-PDE inconsistencies […]

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arXiv:2602.13485v1 Announce Type: cross Abstract: Networks of modern industrial systems are increasingly monitored by distributed sensors, where each system comprises multiple subsystems generating high dimensional time series data. These subsystems are often interdependent, making it important to understand how temporal patterns at one subsystem relate to others. This is challenging in decentralized settings where raw measurements cannot be shared and client observations are heterogeneous. In practical deployments each subsystem (client) operates a fixed proprietary model that cannot be […]

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Revolutionizing warehouse automation with scientific simulation With a novel parallel-computing architecture, a CAD-to-USD pipeline, and the use of OpenUSD as ground truth, a new simulator can explore hundreds of sensor configurations in the time it takes to test just a few physical setups. Robotics Deniz Akyildiz Hunter Liu August 26, 03:50 PM September 09, 10:51 AM Modern warehouses rely on complex networks of sensors to enable safe and efficient operations. These sensors must detect everything from packages and […]

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Hey everyone, We just open-sourced our reasoning model, Chaperone-Thinking-LQ-1.0, on Hugging Face. It’s built on DeepSeek-R1-Distill-Qwen-32B but goes well beyond a simple quantization — here’s what we actually did: The pipeline: 4-bit GPTQ quantization — compressed the model from ~60GB down to ~20GB Quantization-aware training (QAT) via GPTQ with calibration to minimize accuracy loss QLoRA fine-tuning on medical and scientific corpora Removed the adaptive identity layer for transparency — the model correctly attributes its architecture to DeepSeek’s original […]

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Semi-supervised learning (SSL) addresses the critical challenge of training accurate models when labeled data is scarce but unlabeled data is abundant. Graph-based SSL (GSSL) has emerged as a popular framework that captures data structure through graph representations. Classic graph SSL methods, such as Label Propagation and Label Spreading, aim to compute low-dimensional representations where points with the same labels are close in representation space. Although often effective, these methods can be suboptimal on data with complex label distributions. […]

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arXiv:2602.07100v1 Announce Type: new Abstract: Automated floorplan generation aims to improve design quality, architectural efficiency, and sustainability by jointly modeling global spatial organization and precise geometric detail. However, existing approaches operate in raster space and rely on post hoc vectorization, which introduces structural inconsistencies and hinders end-to-end learning. Motivated by compositional spatial reasoning, we propose TLC-Plan, a hierarchical generative model that directly synthesizes vector floorplans from input boundaries, aligning with human architectural workflows based on modular and reusable […]

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arXiv:2603.24755v1 Announce Type: new Abstract: Software development is iterative, yet agentic coding benchmarks overwhelmingly evaluate single-shot solutions against complete specifications. Code can pass the test suite but become progressively harder to extend. Recent iterative benchmarks attempt to close this gap, but constrain the agent’s design decisions too tightly to faithfully measure how code quality shapes future extensions. We introduce SlopCodeBench, a language-agnostic benchmark comprising 20 problems and 93 checkpoints, in which agents repeatedly extend their own prior solutions […]

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arXiv:2602.15135v1 Announce Type: new Abstract: Passkeys — discoverable WebAuthn credentials synchronised across devices are widely promoted as the future of passwordless authentication. Built on the FIDO2 standard, they eliminate shared secrets and resist phishing while offering usability through platform credential managers. Since their introduction in 2022, major vendors have integrated passkeys into operating systems and browsers, and prominent websites have announced support. Yet the true extent of adoption across the broader web remains unknown. Measuring this is challenging […]

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Accurate methane sorption prediction across heterogeneous coal ranks requires models that combine thermodynamic consistency, efficient knowledge transfer across data-scarce geological systems, and calibrated uncertainty estimates, capabilities that are rarely addressed together in existing frameworks. We present a physics-informed transfer learning framework that adapts a hydrogen sorption PINN to methane sorption prediction via Elastic Weight Consolidation, coal-specific feature engineering, and a three-phase curriculum that progressively balances transfer preservation with thermodynamic fine-tuning. Trained on 993 equilibrium measurements from 114 independent […]

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