I originally got asked to write about app/azure_integrations/azure_maps.py—specifically the cache key scheme (tile vs geohash), TTL heuristics by zoom level, and the fallthrough behavior to the upstream geocoder with backoff. That’s exactly the post I wanted to write, because that file is real and non-trivial in this codebase: it defines an Azure Maps client (async, httpx-backed), exposes geocoding methods (including geocode_address referenced from our LangGraph manager), and it’s wired into the enrichment graph as the “last mile” location […]

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arXiv:2601.14505v1 Announce Type: new Abstract: In the network security domain, due to practical issues — including imbalanced data and heterogeneous legitimate network traffic — adversarial attacks in machine learning-based NIDSs have been viewed as attack packets misclassified as benign. Due to this prevailing belief, the possibility of (maliciously) perturbed benign packets being misclassified as attack has been largely ignored. In this paper, we demonstrate that this is not only theoretically possible, but also a particular threat to NIDS. […]

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arXiv:2601.17074v1 Announce Type: new Abstract: The accurate estimation of Arctic snow depth ($h_s$) remains a critical time-varying inverse problem due to the extreme scarcity and noise inherent in associated sea ice parameters. Existing process-based and data-driven models are either highly sensitive to sparse data or lack the physical interpretability required for climate-critical applications. To address this gap, we introduce PhysE-Inv, a novel framework that integrates a sophisticated sequential architecture, an LSTM Encoder-Decoder with Multi-head Attention and physics-guided contrastive […]

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arXiv:2603.28937v1 Announce Type: new Abstract: Interpolatory subdivision schemes generate smooth curves from piecewise-linear control polygons by repeatedly inserting new vertices. Classical schemes rely on a single global tension parameter and typically require separate formulations in Euclidean, spherical, and hyperbolic geometries. We introduce a shared learned tension predictor that replaces the global parameter with per-edge insertion angles predicted by a single 140K-parameter network. The network takes local intrinsic features and a trainable geometry embedding as input, and the predicted […]

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Large language models (LLMs) have transformed how we interact with AI, but one size doesn’t fit at all. Out-of-the-box LLMs are trained with broad, general knowledge and improved for a wide range of use cases, but they often fall short when it comes to domain-specific tasks, proprietary workflows, or unique business requirements. Enterprise customers increasingly need specialized LLMs that deeply understand their proprietary data, business processes, and domain-specific terminology. Without customization, you’re forced to choose between accepting generic […]

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Catastrophic forgetting, the tendency of neural networks to forget previously learned knowledge when learning new tasks, has been a major challenge in continual learning (CL). To tackle this challenge, CL methods have been proposed and shown to reduce forgetting. Furthermore, CL models deployed in mission-critical settings can benefit from uncertainty awareness by calibrating their predictions to reliably assess their confidences. However, existing uncertainty-aware continual learning methods suffer from high computational overhead and incompatibility with mainstream replay methods. To […]

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arXiv:2601.17048v1 Announce Type: new Abstract: Accurate characterization of silicon microstructures is essential for advancing microscale fabrication, quality control, and device performance. Traditional analysis using Scanning Electron Microscopy (SEM) often requires labor-intensive, manual evaluation of feature geometry, limiting throughput and reproducibility. In this study, we propose SiMiC: Context-Aware Silicon Microstructure Characterization Using Attention-Based Convolutional Neural Networks for Field-Emission Tip Analysis. By leveraging deep learning, our approach efficiently extracts morphological features-such as size, shape, and apex curvature-from SEM images, significantly […]

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I built an open-source Python pipeline that scans multiple codebases, extracts structured information using LLMs, and generates Markdown documentation with Mermaid diagrams — all with incremental processing so only changed files get re-analyzed. No wasted LLM calls, no stale docs. ⭐ ==The entire project is open source — grab it, fork it, ship it: full source code on GitHub. Star CocoIndex if it is helpful!== The Problem Every Engineering Team Knows Too Well Documentation rots. It’s one of the […]

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arXiv:2602.05051v1 Announce Type: new Abstract: Offline reinforcement learning (RL) aims to learn the optimal policy from a fixed dataset generated by behavior policies without additional environment interactions. One common challenge that arises in this setting is the out-of-distribution (OOD) error, which occurs when the policy leaves the training distribution. Prior methods penalize a statistical distance term to keep the policy close to the behavior policy, but this constrains policy improvement and may not completely prevent OOD actions. Another […]

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