arXiv:2603.15643v1 Announce Type: new Abstract: Green Stormwater Infrastructure (GSI) systems, such as permeable pavement, rain gardens, and bioretention facilities, require continuous inspection and maintenance to ensure long-term performance. However, domain knowledge about GSI is often scattered across municipal manuals, regulatory documents, and inspection forms. As a result, non-expert users and maintenance staff may struggle to obtain reliable and actionable guidance from field observations. Although Large Language Models (LLMs) have demonstrated strong general reasoning and language generation capabilities, they […]

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arXiv:2603.00629v1 Announce Type: new Abstract: Large time series models (LTMs) have emerged as powerful tools for universal forecasting, yet they often struggle with the inherent diversity and nonstationarity of real-world time series data, leading to an unsatisfactory trade-off between forecasting accuracy and generalization. Rather than continually finetuning new LTM instances for each domain, we propose a data-centric framework, time-series adaptive transformation optimization (TATO), that enables a single frozen pre-trained LTM to adapt to diverse downstream domains through an […]

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“A follow-up to “What I Learned from Lee Kuan Yew – The Alpha Engineer Who Built a Nation” Walking the Talk In my previous piece, I explored how Lee Kuan Yew exemplified the Alpha Engineer archetype, a leader who understood systems at the deepest technical level and used that understanding to transform an entire nation. But there’s a critical principle I didn’t fully emphasize: Alpha Engineers never truly leave the engine room, regardless of the executive positions they hold. […]

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arXiv:2603.03369v1 Announce Type: new Abstract: Hidden communication systems (HCS) embed covert messages within ordinary network activity to hide the presence of communication. In practice, the undetectability of an HCS is typically evaluated using ad hoc traffic statistics or specific detectors, making security claims tightly coupled to experimental setups and implicit adversarial assumptions. In this work, we formalize undetectability as the statistical indistinguishability of observable execution traces under two deployments: a baseline system without hidden communication and an HCS […]

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Training implicit neural representations (INRs) to capture fine-scale details typically relies on iterative backpropagation and is often hindered by spectral bias when the target exhibits highly non-uniform frequency content. We propose ELM-INR, a backpropagation-free INR that decomposes the domain into overlapping subdomains and fits each local problem using an Extreme Learning Machine (ELM) in closed form, replacing iterative optimization with stable linear least-squares solutions. This design yields fast and numerically robust reconstruction by combining local predictors through a […]

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We describe the evolution of DEMOCRITUS, a system for inferring causality from language. Extracting causal claims from natural language is unstable under paraphrase granularity shifts, and context drift. A document collection may express the same causal statement in many surface forms, while neighboring studies may agree locally yet fail to glue globally because relation families or polarities change across regimes. This paper studies that problem through successive versions of DEMOCRITUS, an implemented system for compiling documents into local […]

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Superhuman AI Exfiltrates Emails Classic prompt injection attack: When asked to summarize the user’s recent mail, a prompt injection in an untrusted email manipulated Superhuman AI to submit content from dozens of other sensitive emails (including financial, legal, and medical information) in the user’s inbox to an attacker’s Google Form. To Superhuman’s credit they treated this as the high priority incident it is and issued a fix. The root cause was a CSP rule that allowed markdown images […]

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Efficient ground state search is fundamental to advancing combinatorial optimization problems and quantum chemistry. While the Variational Imaginary Time Evolution (VITE) method offers a useful alternative to Variational Quantum Eigensolver (VQE), and Quantum Approximate Optimization Algorithm (QAOA), its implementation on Noisy Intermediate-Scale Quantum (NISQ) devices is severely limited by the gate counts and depth of manually designed ansatz. Here, we present an automated framework for VITE circuit design using Double Deep-Q Networks (DDQN). Our approach treats circuit construction […]

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Reaction-diffusion (RD) equations underpin pattern formation across chemistry, biology, and physics, yet learning stable operators that forecast their long-term dynamics from data remains challenging. Neural-operator surrogates provide resolution-robust prediction, but autoregressive rollouts can drift due to the accumulation of error, and out-of-distribution (OOD) initial conditions often degrade accuracy. Physics-based numerical residual objectives can regularize operator learning, although they introduce additional assumptions, sensitivity to discretization and loss design, and higher training cost. Here we develop a purely data-driven operator […]

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