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Respect is the most valuable currency at work. Being respected not only makes you feel good, it opens new doors of opportunities and possibilities. People at work vouch for you. You’re being recommended for higher level responsibilities. Your mistakes aren’t looked down upon or seen as a sign of incompetence—people have trust in your knowledge and skills. Others seek your advice as they value your opinion and you get invited to important meetings where you can both learn […]

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Airbnb recently published a detailed write-up on Mussel v2, the next generation of their internal key-value store for serving derived data. The system sits between offline data processing and online applications, turning large batch outputs and streaming updates into low-latency lookups used by systems like fraud detection, recommendations, and pricing. The new architecture is a substantial redesign. It introduces a stateless dispatch layer, range-based sharding, stronger consistency controls, and a new storage backend designed to handle both large […]

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A firsthand look at how domain breadth, context switching, and silence combine to drive burnout in technical leadership roles.

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Prediction markets turn collective knowledge into live probability estimates. Here’s how they work, how probabilities are represented in markets, and why prices aggregate information better than polls or pundits. The Room That Couldn’t Agree Imagine asking 50 intelligent, informed people the same question: “What’s the probability that the Federal Reserve cuts interest rates at its next meeting?” You’d get 50 different answers. Some would say 30%. Others would say 70%. A few would say they have no idea. All […]

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Living systems can be used as physical substrates for information processing, but current implementations are specialized rather than general-purpose. Two distinct directions exist. Molecular approaches use DNA and related chemistry to encode, store, and query data. These systems achieve extreme theoretical density and long-term durability, which positions them for archival storage. Their limitations are economic and operational, particularly synthesis and sequencing costs, as well as latency. As a result, they resemble a potential successor to magnetic tape rather […]

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arXiv:2604.20551v1 Announce Type: new Abstract: Mixture-of-experts models provide a flexible framework for learning complex probabilistic input-output relationships by combining multiple expert models through an input-dependent gating mechanism. These models have become increasingly prominent in modern machine learning, yet their theoretical properties in the Bayesian framework remain largely unexplored. In this paper, we study Bayesian mixture-of-experts models, focusing on the ubiquitous softmax-based gating mechanism. Specifically, we investigate the asymptotic behavior of the posterior distribution for three fundamental statistical tasks: […]

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arXiv:2604.20516v1 Announce Type: new Abstract: Determining identifiability of causal effects from observational data under latent confounding is a central challenge in causal inference. For linear structural causal models, identifiability of causal effects is decidable through symbolic computation. However, standard approaches based on Gr”obner bases become computationally infeasible beyond small settings due to their doubly exponential complexity. In this work, we study how to practically use symbolic computation for deciding rational identifiability. In particular, we present an efficient algorithm […]

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arXiv:2604.20492v1 Announce Type: new Abstract: In this paper, it is shown, for the first time, that centralized performance is achievable in decentralized learning without sharing the local datasets. Specifically, when clients adopt an empirical risk minimization with relative-entropy regularization (ERM-RER) learning framework and a forward-backward communication between clients is established, it suffices to share the locally obtained Gibbs measures to achieve the same performance as that of a centralized ERM-RER with access to all the datasets. The core […]

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arXiv:2604.20301v1 Announce Type: new Abstract: We consider the problem of sampling from a probability distribution $pi$. It is well known that this can be written as an optimisation problem over the space of probability distributions in which we aim to minimise the Kullback–Leibler divergence from $pi$. We consider the effect of replacing $pi$ with a sequence of moving targets $(pi_t)_{tge0}$ defined via geometric tempering on the Wasserstein and Fisher–Rao gradient flows. We show that convergence occurs exponentially in […]

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arXiv:2604.20296v1 Announce Type: new Abstract: Survival analysis is a widely used statistical framework for modeling time-to-event data under censoring. Classical methods, such as the Cox proportional hazards (Cox PH) model, offer a semiparametric approach to estimating the effects of covariates on the hazard function. Despite its importance, survival analysis has been largely unexplored in online settings, particularly within the bandit framework, where decisions must be made sequentially to optimize treatments as new data arrive over time. In this […]

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