IP geolocation APIs now function as core infrastructure within modern applications. Developers depend on them to support fraud prevention, user personalization, traffic routing, analytics enrichment, regulatory compliance, and more. IP data influences decisions that occur in milliseconds, yet carry long-term impact across security posture, revenue, and user trust. As architectures continue to mature, expectations for IP geolocation have grown accordingly. Teams increasingly rely on geolocation signals as inputs into automated systems, a shift that raises the bar for accuracy and […]

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This paper investigates search in model-based reinforcement learning (RL). Conventional wisdom holds that long-term predictions and compounding errors are the primary obstacles for model-based RL. We challenge this view, showing that search is not a plug-and-play replacement for a learned policy. Surprisingly, we find that search can harm performance even when the model is highly accurate. Instead, we show that mitigating distribution shift matters more than improving model or value function accuracy. Building on this insight, we identify […]

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arXiv:2602.10216v1 Announce Type: new Abstract: A single text prompt passed to a diffusion model often yields a wide range of visual outputs determined solely by stochastic process, leaving users with no direct control over which specific semantic variations appear in the image. While existing unsupervised methods attempt to analyze these variations via output features, they omit the underlying generative process. In this work, we propose a framework to disentangle these semantic directions directly within the input embedding space. […]

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arXiv:2509.22341v2 Announce Type: replace Abstract: Model collapse occurs when generative models degrade after repeatedly training on their own synthetic outputs. We study this effect in overparameterized linear regression in a setting where each iteration mixes fresh real labels with synthetic labels drawn from the model fitted in the previous iteration. We derive precise generalization error formulae for minimum-$ell_2$-norm interpolation and ridge regression under this iterative scheme. Our analysis reveals intriguing properties of the optimal mixing weight that minimizes […]

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arXiv:2601.15412v1 Announce Type: new Abstract: Mental health concerns are rising globally, prompting increased reliance on technology to address the demand-supply gap in mental health services. In particular, mental health chatbots are emerging as a promising solution, but these remain largely untested, raising concerns about safety and potential harms. In this paper, we dive into the literature to identify critical gaps in the design and implementation of mental health chatbots. We contribute an operational checklist to help guide the […]

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arXiv:2602.10324v1 Announce Type: new Abstract: As Large Language Models (LLMs) are increasingly deployed in social and strategic scenarios, it becomes critical to understand where and why their behavior diverges from that of humans. While behavioral game theory (BGT) provides a framework for analyzing behavior, existing models do not fully capture the idiosyncratic behavior of humans or black-box, non-human agents like LLMs. We employ AlphaEvolve, a cutting-edge program discovery tool, to directly discover interpretable models of human and LLM […]

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OpenAI is launching the OpenAI Academy for News Organizations, a new learning hub built with the American Journalism Project and The Lenfest Institute to help newsrooms use AI effectively. The Academy offers training, practical use cases, and responsible-use guidance to support journalists, editors, and publishers as they adopt AI in their reporting and operations.

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Performance degradation caused by temporal-difference signal fluctuations and the inflexibility of fixed proximal constraints remains a key challenge in policy optimization algorithms. To address these issues, this article develops a Coherence-Adaptive Proximal Optimization (CAPO) method. We first derive a temporal-coherence-aware advantage estimation mechanism by measuring the directional consistency of temporal-difference residuals within a local time window. Based on this mechanism, short-horizon and long-horizon advantage estimates are adaptively integrated to provide a more stable and temporally reliable policy improvement […]

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