arXiv:2602.15270v1 Announce Type: new Abstract: Generating realistic synthetic populations is essential for agent-based models (ABM) in transportation and urban planning. Current methods face two major limitations. First, many rely on a single dataset or follow a sequential data fusion and generation process, which means they fail to capture the complex interplay between features. Second, these approaches struggle with sampling zeros (valid but unobserved attribute combinations) and structural zeros (infeasible combinations due to logical constraints), which reduce the diversity […]

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Working on a system where coding tasks flow through a multi-stage pipeline, with each stage receiving a curated context subset (full, summary, or omitted per field) rather than full project state. Key observations: focused context produces better code than noisy context. Bounded retry loops with differentiated prompts (first-pass vs remediation) resolve most test failures. Git worktree isolation enables true parallel execution. Model-agnostic (Claude, GPT, Ollama). swimcode.ai. Interested in whether others have measured scoped vs full context in multi-agent […]

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The release of Symfony 7.4 LTS marks a pivotal moment in the PHP ecosystem. While the framework itself introduces robust features like stricter type safety and native PHP serialization for containers, the real revolution is happening in how we serve these applications.Gone are the days when NGINX + PHP-FPM was the undisputed default. Application servers written in Go have matured, offering “Worker Mode” capabilities that boot your kernel once and keep it in memory, slashing latency and skyrocketing throughput. In […]

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Hi everyone! I’m a foreign PhD student currently studying in China, and I’ve recently connected with a mid-sized technology/manufacturing company based in China. They’re traditionally focused on audio, communications, and public-address electronic systems that are widely used in education, transportation, and enterprise infrastructure Over the past few weeks, we’ve had a couple of positive interactions: Their team invited me to visit their manufacturing facility and showed me around. More recently, they shared that they’ve been working on or […]

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arXiv:2603.00617v1 Announce Type: new Abstract: The persistent militarization of large reasoning models stems not from technical necessity but from governance arrangements that strip researchers of meaningful authority to refuse harmful transfers and deployments. Existing accountability mechanisms such as model cards and responsible AI statements operate as reputational signals detached from decision making architecture. We identify institutional veto power as a missing governance primitive: a formal authority to halt subsequent use or distribution of research when credible risks of […]

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arXiv:2304.04724v3 Announce Type: replace-cross Abstract: We analyze the mixing time of Metropolized Hamiltonian Monte Carlo (HMC) with the leapfrog integrator to sample from a distribution on $mathbb{R}^d$ whose log-density is smooth, has Lipschitz Hessian in Frobenius norm and satisfies isoperimetry. We bound the gradient complexity to reach $epsilon$ error in total variation distance from a warm start by $tilde O(d^{1/4}text{polylog}(1/epsilon))$ and demonstrate the benefit of choosing the number of leapfrog steps to be larger than 1. To surpass […]

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arXiv:2603.02729v1 Announce Type: cross Abstract: We study the problem of recovering a low-tubal-rank tensor $mathcal{X}_starin mathbb{R}^{n times n times k}$ from noisy linear measurements under the t-product framework. A widely adopted strategy involves factorizing the optimization variable as $mathcal{U} * mathcal{U}^top$, where $mathcal{U} in mathbb{R}^{n times R times k}$, followed by applying factorized gradient descent (FGD) to solve the resulting optimization problem. Since the tubal-rank $r$ of the underlying tensor $mathcal{X}_star$ is typically unknown, this method often assumes […]

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The rapid proliferation of airborne platforms, including commercial aircraft, drones, and UAVs, has intensified the need for real-time, automated threat assessment systems. Current approaches depend heavily on manual monitoring, resulting in limited scalability and operational inefficiencies. This work introduces a dual-task model based on EfficientNetB4 capable of performing airborne object classification and threat-level prediction simultaneously. To address the scarcity of clean, balanced training data, we constructed the AODTA Dataset by aggregating and refining multiple public sources. We benchmarked […]

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