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Why Mayan EDMS Cannot Read Half Your Scanned Documents?
Mayan EDMS cannot read half your scanned documents because the scans are too faint, too small, or too crooked for OCR to process the text Continue reading on Medium »
Nvidia had to cosign OpenAI’s loan
My job, on a normal Tuesday, is reading bond covenants until my eyes cross. I underwrite project debt for data centers, not the sexy AI Continue reading on Medium »
Microsoft issues emergency Windows 11 update to fix its record-breaking patch
Microsoft has started rolling out an emergency out-of-band update to fix problems with its record-breaking September patch Tuesday update. The software giant patched nearly 1,000 flaws in its biggest update ever this month, but it also introduced some bugs that have affected businesses, developers, and gamers. The September patch Tuesday affected folder shares on Hyper-V-based Linux virtual machines, Remote Desktop Services sessions, and some USB audio devices. Microsoft's out-of-band update, released yesterday, addresses these issues across Windows 11 versions 26h1, 25H2, and 24H2. Microsoft has also released updates for Windows Server 202 … Read the full story at The Verge.
From Links to Answers: Does Generative AI Improve Information Access or Reduce User Control?
How generative AI is transforming the relationship between users, platforms and online knowledgeContinue reading on Medium »
Why Google Paid $400 Million for a 14-Person Lab With No Product — And What It Reveals About AI M&A…
DeepMind wasn t a fluke. It was the template for how every major tech company now grows.Continue reading on Medium »
Volvo’s plug-in hybrid XC60 and XC90 can really go the distance
Most plug-in hybrid electric vehicles (PHEV) today get an average of around 30-50 miles of battery-only range: decent, but not mind-blowing. With hybrid sales dominating the market today, Volvo is out to prove that its PHEVs can push the limits of what's possible. Today, the Swedish automaker revealed the updated PHEV versions of its popular XC60 and XC90 SUVs are its first "long range plug-in hybrids," delivering class-leading ranges of 78 miles and 73 miles, respectively. That's more than double the electric range of previous PHEV variant. The numbers are still preliminary and based on EPA testing standards. But if confirmed, they would … Read the full story at The Verge.
I Stopped Asking AI to Write Code. I Started Asking It to Think With Me.
The biggest change AI made to my work isn t writing code faster. It s helping me think differently.Continue reading on Medium »
What Is Recursive Self-Improvement in AI? A Practical Explanation
Artificial intelligence has traditionally improved through a process controlled by humans: researchers collect data, design algorithms Continue reading on Medium »
A derivative-fidelity failure mode in physics-informed neural networks: strengthened benchmark evidence from function-value training
arXiv:2609.13171v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) use automatic differentiation to impose differential-equation residuals, but good agreement in function values does not necessarily imply accurate derivatives. This paper formulates derivative fidelity as a failure mode of PINNs and tests it with one-dimensional benchmarks. Multilayer perceptrons are trained only on function values for sin(x) and exp(x), while second derivatives obtained by automatic differentiation are evaluated separately. The hypothesis is strengthened by additional tests over training-point density, activation functions, endpoint-dense evaluation, and both L2 and maximum-error diagnostics. The results show that visually accurate function approximation can coexist with substantially larger second-derivative errors, especially near high-curvature boundary regions. The experiment provides a diagnostic protocol for distinguishing value accuracy from physics-residual reliability.
BudgetBench: A Budget-Tiered Protocol and Pilot Harness for Memory Strategy Evaluation in Local Large Language Model Agents
arXiv:2609.13149v1 Announce Type: new Abstract: For local large language model agents, active context is a scarce resource: memory capacity, prefill latency, cache growth, and service objectives all constrain how many input tokens each call can afford. We present BudgetBench, an active-budget protocol and reference harness that treats the per-call input-token budget as the independent variable when comparing memory strategies. Holding the model, task, sampler, and decoding fixed, it sweeps budgets over 2K, 4K, 8K, 16K, and 32K tokens and records quality, budget utilization, latency, and, as a first-class outcome, budget-violation rates. The core contribution is this reusable measurement surface: a swappable MemoryStrategy contract, explicit budget enforcement, deterministic or versioned graders, prompt-audit metadata, and reproducibility artifacts, released at https://github.com/aviskaar/budgetbench. We substantiate the protocol with pilot studies rather than final rankings. Across a l
Converge Then Diversify: Decoupling Convergence and Diversity in Multi-Objective Bayesian Optimisation
arXiv:2609.13396v1 Announce Type: new Abstract: Multi-objective Bayesian optimisation (MOBO) is a sample-efficient approach for optimising expensive black-box functions with multiple objectives. In MOBO, the goal is to adequately approximate the Pareto front; that is, to obtain a high-quality solution set with 1) good convergence (closeness to the Pareto front) and 2) good diversity (spread across the Pareto front). Existing MOBO methods typically aim to accomplish these two tasks simultaneously, i.e., driving the search towards the Pareto front while maintaining a diverse set of nondominated solutions, such that the solutions, ideally, can gradually approach the entire front. When sufficient search budgets are available, this approach is effective. However, considering both convergence and diversity throughout the search is not easy and requires careful design. Under very tight budgets, there may not be enough solutions generated to be able to simultaneously approach the entire Pareto