Sparsely-Supervised Data Assimilation via Physics-Informed Schr”odinger Bridge

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arXiv:2603.22319v1 Announce Type: new Abstract: Data assimilation (DA) for systems governed by partial differential equations (PDE) aims to reconstruct full spatiotemporal fields from sparse high-fidelity (HF) observations while respecting physical constraints. While full-grid low-fidelity (LF) simulations provide informative priors in multi-fidelity settings, recovering an HF field consistent with both sparse observations and the governing PDE typically requires per-instance test-time optimization, which becomes a major bottleneck in time-critical applications. To alleviate this, amortized reconstruction using generative models has recently […]

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5 Underrated Libraries & Frameworks for AI Engineers to Learn in 2026

5 Underrated Libraries & Frameworks for AI Engineers to Learn in 2026

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In the fast-moving world of AI, we often get distracted by the flashiest models where everyone is talking about Gemini, GPT, Claude, or Grok models. But for AI Engineers building actual production systems, the model is just one small piece of a much larger complicated puzzle. To build a robust AI application, you need to solve distinct engineering challenges: inference latency, observability, user interfaces, agentic orchestration, and memory management. Here are 5 underrated libraries/frameworks (plus a bonus) that […]

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Contrastive Learning for Diversity-Aware Product Recommendations in Retail

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Recommender systems often struggle with long-tail distributions and limited item catalog exposure, where a small subset of popular items dominates recommendations. This challenge is especially critical in large-scale online retail settings with extensive and diverse product assortments. This paper introduces an approach to enhance catalog coverage without compromising recommendation quality in the existing digital recommendation pipeline at IKEA Retail. Drawing inspiration from recent advances in negative sampling to address popularity bias, we integrate contrastive learning with carefully selected […]

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An Implementation Guide to Building a DuckDB-Python Analytics Pipeline with SQL, DataFrames, Parquet, UDFs, and Performance Profiling

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In this tutorial, we build a comprehensive, hands-on understanding of DuckDB-Python by working through its features directly in code on Colab. We start with the fundamentals of connection management and data generation, then move into real analytical workflows, including querying Pandas, Polars, and Arrow objects without manual loading, transforming results across multiple formats, and writing expressive SQL for window functions, pivots, macros, recursive CTEs, and joins. As we progress, we also explore performance-oriented capabilities such as bulk insertion, […]

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The TechBeat: IPv6 and CTV: The Measurement Challenge From the Fastest-Growing Ad Channel (1/7/2026)

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How are you, hacker? 🪐Want to know what’s trending right now?: The Techbeat by HackerNoon has got you covered with fresh content from our trending stories of the day! Set email preference here. ## A Developer’s Guide to Building Next-Gen Smart Wallets With ERC-4337 — Part 2: Bundlers By @hacker39947670 [ 15 Min read ] Bundlers are the bridge between account abstraction and the execution layer. Read More. Why Verifiability Matters for Security: 12 Key Cloud Application Use […]

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Your Group-Relative Advantage Is Biased

Your Group-Relative Advantage Is Biased

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This paper identifies and theoretically proves a statistical bias in group-based advantage estimation within Reinforcement Learning from Verifier Rewards (RLVR) algorithms used for post-training large language models on reasoning tasks. It proposes History-Aware Adaptive Difficulty Weighting (HA-DW) to mitigate this bias, consistently improving LLM performance and training efficiency across benchmarks. Paper link: https://arxiv.org/pdf/2601.08521 https://preview.redd.it/2j5xdz35h7pg1.png?width=1720&format=png&auto=webp&s=ec7e34a6f49da2b2c1394a37fa865c8193eee28a submitted by /u/This_Ad9834 [link] [comments]

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[D] Simple Questions Thread

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Please post your questions here instead of creating a new thread. Encourage others who create new posts for questions to post here instead! Thread will stay alive until next one so keep posting after the date in the title. Thanks to everyone for answering questions in the previous thread! submitted by /u/AutoModerator [link] [comments]

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Clawdbot Was the Prototype. OpenClaw Is the Interface War

Clawdbot Was the Prototype. OpenClaw Is the Interface War

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Two weeks ago, Clawdbot felt like a weird internet toy An agent that could actually touch your machine instead of roleplaying “productivity” in a chat box. This week, OpenClaw looks like something else entirely: the first consumer-grade “agent runtime” that people are deploying like it’s a normal app. And that’s exactly the problem. Because once an agent can run code, read files, and remember you, you don’t just get automation. You get a new attack surface with a personality. And Peter Steinberger […]

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Cortex Code in Snowflake: How to Use It Without Burning Credits

Cortex Code in Snowflake: How to Use It Without Burning Credits

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Snowflake Cortex Code (CoCo) is like an AI assistant inside Snowsight (and CLI also). You can ask it to write SQL, create dbt models, explore data, help in ML work, and even do some admin tasks. But one thing people don’t realise early — this tool is powerful, but also costly if used wrongly. Bad prompts → more tokens → more credits → surprise bill. Prompt Engineering (this directly impacts cost) CoCo works on token consumption. what you type → counted 2. what it replies […]

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Foundation Models

Foundation Models

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The term “foundation models” is very new. It was introduced and popularized by the Stanford Institute for Human-Centered Artificial Intelligence when first large-scale multitask models started to appear in 2019-ish. Early examples of foundation models are GPT-3, BERT, and DALL-E 2. They were called “base models” or “foundation models” because they were trained on a massive amount of multimodal data. That allowed them to perform an assortment of tasks — all you need to do is provide the […]

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