In the previous case, I described how we optimized the art pipeline and produced a full set of production artifacts, accelerating art development by approximately 70% (you can read more about it here). However, the majority of these results would not have been achievable without addressing another critical factor—one that is often overlooked, yet fully capable of derailing even the best plans. That factor is the quality of the working day and cross-department interaction. Let’s walk through it […]

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Suppose a social media platform’s Ads analytics team wants to know: Does seeing a certain ad (or promoted post) cause users to convert or engage more? This causal question is tricky because users who see the ad might inherently differ from those who don’t. In practice, simply comparing conversion rates of exposed vs. unexposed users can be very misleading. Ad exposure is not randomly assigned – algorithms may show ads more to highly active users, or users self-select into […]

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arXiv:2602.23437v1 Announce Type: new Abstract: Online information is increasingly linked to real-world instability, especially as automated accounts and LLM-based agents help spread and amplify news. In this work, we study how information spreads on networks of Large Language Models (LLMs) using mathematical models. We investigate how different types of offline events, along with the “personalities” assigned to the LLMs, affect the network dynamics of online information spread of the events among the LLMs. We introduce two models: 1) […]

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While standard reinforcement learning optimizes a single reward signal, many applications require optimizing a nonlinear utility $f(J_1^π,dots,J_M^π)$ over multiple objectives, where each $J_m^π$ denotes the expected discounted return of a distinct reward function. A common approach is concave scalarization, which captures important trade-offs such as fairness and risk sensitivity. However, nonlinear scalarization introduces a fundamental challenge for policy gradient methods: the gradient depends on $partial f(J^π)$, while in practice only empirical return estimates $hat J$ are available. Because […]

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arXiv:2603.15689v1 Announce Type: new Abstract: Mainstream flow matching methods typically focus on learning the local velocity field, which inherently requires multiple integration steps during generation. In contrast, Mean Velocity Flow models establish a relationship between the local velocity field and the global mean velocity, enabling the latter to be learned through a mathematically grounded formulation and allowing generation to be transferred to arbitrary future time points. In this work, we propose a new paradigm that directly learns the […]

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One-sentence summary I describe the risk of personality self-replicators, the threat of OpenClaw-like agents managing spreading in hard-to-control ways.   Summary LLM agents like OpenClaw are defined by a small set of text files and are run by an open source framework which leverages LLMs for cognition. It is quite difficult for current frontier models to exfiltrate their weights and run elsewhere, whereas these agents only need to copy those few text files to self-replicate (at the cost of greater […]

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Reliable quantification of uncertainty estimates in continuous-time (CT) representation learning remains nascent, particularly within CT attention architectures. We introduce the Neuronal Stochastic Attention Circuit (NSAC), a novel biologically-inspired CT attention architecture that reformulates attention logit computation as the solution of an Ornstein-Uhlenbeck stochastic differential equation modulated by input-dependent, nonlinear interlinked gates derived from repurposed C.elegans Neuronal Circuit Policies (NCPs) wiring mechanism. It induces Gaussian distribution over logits that propagates principled stochasticity through logistic-normal distribution over attention weights to […]

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