technocracy

arXiv:2601.16363v1 Announce Type: new Abstract: The software engineering research community is productive, yet it faces a constellation of challenges: swamped review processes, metric-driven incentives, distorted publication practices, and increasing pressures from AI, scale, and outright scams. These issues are often treated in isolation, yet they arise from deep structural dynamics within the research ecosystem itself and distract us from the larger role of research in society. Meaningful progress requires a holistic system-level view. We sketch such a framework […]

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Hello everyone, I’m building a RL project around Monster Hunter Tri running in Dolphin, and I’m hitting a set of problems that feel very close to partial observability / state estimation rather than “just” policy learning. The setup is hybrid: – memory reads for player state and environment context, – heuristic detection when memory is incomplete, – an octree/cube-based spatial approximation, – and eventually more vision-based signals. The biggest issue is monster state. I can get some usable […]

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arXiv:2602.17922v2 Announce Type: replace-cross Abstract: The glmnet package in R is widely used for lasso estimation because of its computational efficiency. Despite its popularity, glmnet occasionally yields solutions that deviate substantially from the true ones because of the inappropriate default configuration of the algorithm. The accuracy of the obtained solutions can be improved by appropriately tuning the configuration. However, such improvements typically increase computational time, resulting in a tradeoff between accuracy and computational efficiency. Therefore, a systematic approach […]

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arXiv:2602.23587v1 Announce Type: new Abstract: Knowledge distillation transfers large teacher models to compact student models, enabling deployment on resource-limited platforms while suffering minimal performance degradation. However, this paradigm could lead to various security risks, especially model theft. Existing defenses against model theft, such as watermarking and secure enclaves, focus primarily on identity authentication and incur significant resource costs. Aiming to provide post-theft accountability and traceability, we propose a novel fingerprinting framework that superimposes device-specific Physical Unclonable Function (PUF) […]

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arXiv:2506.12818v2 Announce Type: replace-cross Abstract: Bayesian optimization (BO) has traditionally solved black-box problems where function evaluation is expensive and, therefore, observations are few. Recently, however, there has been growing interest in applying BO to problems where function evaluation is cheaper and observations are more plentiful. In this regime, scaling to many observations $N$ is impeded by Gaussian-process (GP) surrogates: GP hyperparameter fitting scales as $mathcal{O}(N^3)$ (reduced to roughly $mathcal{O}(N^2)$ in modern implementations), and it is repeated at every […]

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arXiv:2603.26301v1 Announce Type: cross Abstract: Many causal discovery algorithms, including the celebrated FCI algorithm, output a Partial Ancestral Graph (PAG). PAGs serve as an abstract graphical representation of the underlying causal structure, modeled by directed acyclic graphs with latent and selection variables. This paper develops a characterization of the set of extended-type conditional independence relations that are invariant across all causal models represented by a PAG. This theory allows us to formulate a general measure-theoretic version of Pearl’s […]

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arXiv:2603.09995v1 Announce Type: new Abstract: Behavioral interview evaluation using large language models presents unique challenges that require structured assessment, realistic interviewer behavior simulation, and pedagogical value for candidate training. We investigate chain of thought prompting for interview answer evaluation and improvement through two controlled experiments with 50 behavioral interview question and answer pairs. Our contributions are threefold. First, we provide a quantitative comparison between human in the loop and automated chain of thought improvement. Using a within subject […]

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arXiv:2601.02505v1 Announce Type: new Abstract: Complex multi-robot missions often require heterogeneous teams to jointly optimize task allocation, scheduling, and path planning to improve team performance under strict constraints. We formalize these complexities into a new class of problems, dubbed Spatio-Temporal Efficacy-optimized Allocation for Multi-robot systems (STEAM). STEAM builds upon trait-based frameworks that model robots using their capabilities (e.g., payload and speed), but goes beyond the typical binary success-failure model by explicitly modeling the efficacy of allocations as trait-efficacy […]

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The Elephant in Every WordPress Agency’s Room There are roughly 835 million websites on the internet. According to W3Techs, WordPress powers about 43% of them. That is not a niche platform. That is the infrastructure layer of the modern web. But here is the uncomfortable part: a staggering number of those WordPress sites have not been meaningfully updated in years. Not the core software updates (most hosts handle those automatically now), but the actual design, content structure, and […]

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In this work, we provide a sharp theory of scaling laws for two-layer neural networks trained on a class of hierarchical multi-index targets, in a genuinely representation-limited regime. We derive exact information-theoretic scaling laws for subspace recovery and prediction error, revealing how the hierarchical features of the target are sequentially learned through a cascade of phase transitions. We further show that these optimal rates are achieved by a simple, target-agnostic spectral estimator, which can be interpreted as the […]

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