One of the new things I’ve come across recently, while researching command-line-based coding assistants, is the mention and use of a tool I hadn’t heard of before. That tool is called Tmux, which stands for Terminal Multiplexer.  In the simplest possible terms, Tmux allows you to split up a single terminal window into a number […] The post A beginner’s guide to Tmux: a multitasking superpower for your terminal appeared first on Towards Data Science.

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arXiv:2601.20888v2 Announce Type: replace Abstract: We study sampling from posterior distributions in Bayesian linear inverse problems where $A$, the parameters to observables operator, is computationally expensive. In many applications, $A$ can be factored in a manner that facilitates the construction of a cost-effective approximation $tilde{A}$. In this framework, we introduce Latent-IMH, a sampling method based on the Metropolis-Hastings independence (IMH) sampler. Latent-IMH first generates intermediate latent variables using the approximate $tilde{A}$, and then refines them using the exact […]

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Hi all. So I just learned about Monte-Carlo Tree Search from University of Queensland’s free book, and I have one question. From my understanding, each state has its own tree. Is it correct? If correct, then why? I thought that states that are closer to the root tree is already simulated, hence we can just reuse the calculation? Thank you in advance. submitted by /u/indo_dementor [link] [comments]

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arXiv:2601.15338v1 Announce Type: new Abstract: Axial coding is a commonly used qualitative analysis method that enhances document understanding by organizing sentence-level open codes into broader categories. In this paper, we operationalize axial coding with large language models (LLMs). Extending an ensemble-based open coding approach with an LLM moderator, we add an axial coding step that groups open codes into higher-order categories, transforming raw debate transcripts into concise, hierarchical representations. We compare two strategies: (i) clustering embeddings of code-utterance […]

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arXiv:2603.26790v1 Announce Type: new Abstract: Flow-matching generative models are increasingly used to simulate cell responses to biological perturbations. However, the design space for building such models is large and underexplored. We systematically analyse the design space of flow matching models for cell-microscopy images, finding that many popular techniques are unnecessary and can even hurt performance. We develop a simple, stable, and scalable recipe which we use to train our foundation model. We scale our model to two orders […]

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arXiv:2604.06329v1 Announce Type: new Abstract: Resource allocation problems across multiple contests are ubiquitous in adversarial settings, from military operations to market competition. While Colonel Blotto and General Lotto games have provided valuable theoretical foundations for such problems, their equilibrium characterizations typically permit resources to be arbitrarily allocated across all contests — a flexibility that rarely aligns with practical constraints. This paper introduces a novel constrained variant of the General Lotto game where one player is restricted to allocating […]

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Masked discrete diffusion models (MDMs) are a promising new approach to generative modelling, offering the ability for parallel token generation and therefore greater efficiency than autoregressive counterparts. However, achieving an optimal balance between parallel generation and sample quality remains an open problem. Current approaches primarily address this issue through fixed, heuristic parallel sampling methods. There exist some recent learning based approaches to this problem, but its formulation from the perspective of variational inference remains underexplored. In this work, […]

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arXiv:2603.00540v1 Announce Type: new Abstract: The evolution of Large Language Models (LLMs) from static instruction-followers to autonomous agents necessitates operating within complex, stateful environments to achieve precise state-transition objectives. However, this paradigm is bottlenecked by data scarcity, as existing tool-centric reverse-synthesis pipelines fail to capture the rigorous logic of real-world applications. We introduce textbf{LOGIGEN}, a logic-driven framework that synthesizes verifiable training data based on three core pillars: textbf{Hard-Compiled Policy Grounding}, textbf{Logic-Driven Forward Synthesis}, and textbf{Deterministic State Verification}. Specifically, […]

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arXiv:2602.10018v1 Announce Type: cross Abstract: Selective conformal prediction aims to construct prediction sets with valid coverage for a test unit conditional on it being selected by a data-driven mechanism. While existing methods in the offline setting handle any selection mechanism that is permutation invariant to the labeled data, their extension to the online setting — where data arrives sequentially and later decisions depend on earlier ones — is challenged by the fact that the selection mechanism is naturally […]

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