In this blog post, we will provide insights into the website deployment workflow developed during our collaboration with Runtime Verification. Our collaboration focused on creating nix-based deployment workflow for small websites associated with Runtime Verification projects, including: Kontrol: developer tooling for formal verification of smart contracts written in solodity. ERCx: developer tooling for ERC token testing. Firefly: developer tooling for Ethereum smart contract testing. We will walk through a simple example that showcases all the key features of […]

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arXiv:2602.10127v1 Announce Type: new Abstract: The rapid advancement of artificial intelligence (AI) agents has catalyzed the transition from static language models to autonomous agents capable of tool use, long-term planning, and social interaction. $textbf{Moltbook}$, the first social network designed exclusively for AI agents, has experienced viral growth in early 2026. To understand the behavior of AI agents in the agent-native community, in this paper, we present a large-scale empirical analysis of Moltbook leveraging a dataset of 44,411 posts […]

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arXiv:2601.21008v1 Announce Type: new Abstract: Operations Research practitioners routinely debug infeasible models through an iterative process: analyzing Irreducible Infeasible Subsystems (IIS{}), identifying constraint conflicts, and systematically repairing formulations until feasibility is achieved. Yet existing LLM benchmarks evaluate OR as one-shot translation — given a problem description, generate solver code — ignoring this diagnostic loop entirely. We introduce two benchmarks that place the textbf{solver in the evaluation loop}. textbf{ORDebug{}} evaluates iterative self-correction through 5,000+ problems spanning 9 error types; […]

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arXiv:2604.05087v1 Announce Type: new Abstract: Document expansion is a classical technique for improving retrieval quality, and is attractive since it shifts computation offline, avoiding additional query-time processing. However, when applied to modern retrievers, it has been shown to degrade performance, often introducing noise that obfuscates the discriminative signal. We recast document expansion as a document optimization problem: a language model or a vision language model is fine-tuned to transform documents into representations that better align with the expected […]

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arXiv:2601.17855v1 Announce Type: cross Abstract: Load balancing-the allocation of work across parallel resources to reduce delay, energy and cost-is a pervasive challenge in science and engineering, from large-scale simulation and data processing to cloud and manufacturing operations. Motivated by the emerging bottleneck in large language model (LLM) serving, we study a particularly stringent regime of load balancing that arises in barrier-synchronized, stateful systems: work cannot be freely migrated and progress is gated by the slowest participant at each […]

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Multimodal attributed graphs (MAGs) combine multimodal node attributes with structured relations. However, existing methods usually perform shared message passing on a single graph and implicitly assume that the same neighbors are equally useful for all modalities. In practice, neighbors that benefit one modality may interfere with another, blurring modality-specific signals under shared propagation. To address this issue, we propose RoleMAG, a multimodal graph framework that learns how different neighbors should participate in propagation. Concretely, RoleMAG distinguishes whether a […]

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arXiv:2602.06276v1 Announce Type: cross Abstract: We address the problem of training conversion prediction models in advertising domains under privacy constraints, where direct links between ad clicks and conversions are unavailable. Motivated by privacy-preserving browser APIs and the deprecation of third-party cookies, we study a setting where the learner observes a sequence of clicks and a sequence of conversions, but can only link a conversion to a set of candidate clicks (an attribution set) rather than a unique source. […]

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Pretext Exciting new browser library from Cheng Lou, previously a React core developer and the original creator of the react-motion animation library. Pretext solves the problem of calculating the height of a paragraph of line-wrapped text without touching the DOM. The usual way of doing this is to render the text and measure its dimensions, but this is extremely expensive. Pretext uses an array of clever tricks to make this much, much faster, which enables all sorts of […]

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