Hi everyone, I’m trying to understand how to add theoretical justification to an AI/ML paper. My background is mostly in empirical modeling, so I’m comfortable with experiments, results, and analysis. But I often see papers that include formal elements like theorems, lemmas, and proofs, and I’m not sure how to approach that side. For example, I’m exploring an idea about measuring uncertainty in the attention mechanism by looking at the outputs of different attention heads. Intuitively it makes […]

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We introduce Latent Space Distribution Matching (LSDM), a novel framework for semi-supervised generative modeling of conditional distributions. LSDM operates in two stages: (i) learning a low-dimensional latent space from both paired and unpaired data, and (ii) performing joint distribution matching in this space via the 1-Wasserstein distance, using only paired data. This two-step approach minimizes an upper bound on the 1-Wasserstein distance between joint distributions, reducing reliance on scarce paired samples while enabling fast one-step generation. Theoretically, we […]

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Modern applications often struggle to scale, not because of weak infrastructure, but because decision-making intelligence is added only after systems are already under load. Auto-scaling, DevOps pipelines, and cloud elasticity help, but they are largely reactive by design. As user demand grows, engineering teams are forced to chase latency spikes, error rates, and infrastructure bottlenecks after they appear, turning scalability into a constant firefight. This is where AI-native solutions fundamentally change how scalability is engineered. By embedding intelligence directly into […]

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District Heating Systems are essential infrastructure for delivering heat to consumers across a geographic region sustainably, yet efficient management relies on optimizing diverse energy sources, such as wood, gas, electricity, and solar, in response to fluctuating demand. Aligning supply with demand is critical not only for ensuring reliable heat distribution but also for minimizing carbon emissions and extending infrastructure lifespan through lower operating temperatures. However, accurate multi-step forecasting to support these goals remains challenging due to complex, non-linear […]

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arXiv:2603.16879v1 Announce Type: new Abstract: Solving the alternating current power flow equations in real time is essential for secure grid operation, yet classical Newton-Raphson solvers can be slow under stressed conditions. Existing graph neural networks for power flow are typically trained on a single system and often degrade on different systems. We present PowerModelsGAT-AI, a physics-informed graph attention network that predicts bus voltages and generator injections. The model uses bus-type-aware masking to handle different bus types and balances […]

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There’s a question that quietly predicts whether an AI agent will feel sharp or strangely distant: How long does the agent remain misaligned when a new domain term enters the conversation? It sounds almost trivial — a glossary issue, a maintenance task. But in practice, this single latency window reveals more about an agent’s reliability than most teams realize. Because language doesn’t evolve politely. It arrives mid‑conversation. A finance analyst introduces a new internal metric. A product team […]

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The rapid deployment of machine learning across platforms from milliwatt-class TinyML devices to large language models has made energy efficiency a primary constraint for sustainable AI. Across these scales, performance and energy are increasingly limited by data movement and memory-system behavior rather than by arithmetic throughput alone. This work reviews energy efficient software hardware codesign methods spanning edge inference and training to datacenter-scale LLM serving, covering accelerator architectures (e.g., ASIC/FPGA dataflows, processing-/compute-in-memory designs) and system-level techniques (e.g., partitioning, […]

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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. ## The Long Now of the Web: Inside the Internet Archive’s Fight Against Forgetting By @zbruceli [ 18 Min read ] A deep dive into the Internet Archive’s custom tech stack. Read More. Why Kubernetes Outages Are Usually Human Failures, Not Platform Bugs By @davidiyanu […]

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arXiv:2601.08856v1 Announce Type: new Abstract: Unit tests are critical in the hardware design lifecycle to ensure that component design modules are functionally correct and conform to the specification before they are integrated at the system level. Thus developing unit tests targeting various design features requires deep understanding of the design functionality and creativity. When one or more unit tests expose a design failure, the debugging engineer needs to diagnose, localize, and debug the failure to ensure design correctness, […]

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