Third party services SDKs and their circumstances Developing and publishing games often involves relying on third-party services, middleware, or platforms. Numerous vendors offer commercial products like advertising networks, backend services, sound engines, user authentication, and more. These tools simplify the process of monetization, expanding player reach, storing data on servers, and performing other functions beyond what a game engine typically provides. To facilitate integration, these vendors often supply SDKs tailored for specific game engines, allowing developers to easily […]

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arXiv:2509.02171v2 Announce Type: replace Abstract: Actuarial ratemaking depends on high-quality data, yet access to such data is often limited by the cost of obtaining new data, privacy concerns, etc. In this paper, we explore synthetic-data generation as a potential solution to these issues. In addition to generative methods previously studied in the actuarial literature, we explore and benchmark another class of approaches based on Multivariate Imputation by Chained Equations (MICE). In a comparative study using an open-source dataset, […]

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arXiv:2601.15490v1 Announce Type: new Abstract: Bias in chest X-ray classifiers frequently stems from sex- and age-related shortcuts, leading to systematic underdiagnosis of minority subgroups. Previous pixel-space attribute neutralizers, which rely on convolutional encoders, lessen but do not fully remove this attribute leakage at clinically usable edit strengths. This study evaluates whether substituting the U-Net convolutional encoder with a Vision Transformer backbone in the Attribute-Neutral Framework can reduce demographic attribute leakage while preserving diagnostic accuracy. A data-efficient Image Transformer […]

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arXiv:2602.13273v1 Announce Type: new Abstract: Large language model (LLM) merging has become a key technique in modern LLM development pipelines, enabling the integration of multiple task- or domain-specific expert models without retraining. However, as the number of experts grows, existing merging implementations treat model parameters as unstructured files and execute merges in a stateless, one-shot manner, leading to excessive disk I/O, redundant parameter scans, and poor scalability. In this paper, we present textbf{MergePipe}, a parameter management system for […]

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arXiv:2412.01212v2 Announce Type: replace Abstract: Several power-law critical properties involving different statistics in natural languages — reminiscent of scaling properties of physical systems at or near phase transitions — have been documented for decades. The recent rise of large language models has added further evidence and excitement by providing intriguing similarities with notions in physics such as scaling laws and emergent abilities. However, specific instances of classes of generative language models that exhibit phase transitions, as understood by […]

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I keep hearing the same question at every engineering offsite, Slack thread, and investor pitch: “What’s the best model right now — GPT, Claude, or Gemini?” I spent the last several months building and debugging agent-based systems, and I think this is the wrong question entirely. The evidence is now overwhelming: what determines whether an AI agent succeeds in production is not the model underneath it, but the infrastructure wrapped around it. I am going to lay out my hypothesis, test […]

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arXiv:2601.13191v1 Announce Type: new Abstract: In this paper, the solution to the empirical risk minimization problem with $f$-divergence regularization (ERM-$f$DR) is presented and conditions under which the solution also serves as the solution to the minimization of the expected empirical risk subject to an $f$-divergence constraint are established. The proposed approach extends applicability to a broader class of $f$-divergences than previously reported and yields theoretical results that recover previously known results. Additionally, the difference between the expected empirical […]

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In real-time delivery of AV1 over MPEG2-TS using HTTP/3 over QUIC, two fundamental approaches can be identified for improving video quality. The first is bitrate adaptation, represented by Adaptive Bitrate (ABR), which estimates available bandwidth and selects the optimal resolution and frame rate within that range. The second approach focuses on low-latency control, reducing RTT and ACK delay to increase effective throughput. This study emphasizes the latter perspective and, through terminal-side QUIC observation, suggests that user-space buffer saturation […]

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