arXiv:2603.01786v1 Announce Type: cross Abstract: In this paper, we present a novel learning framework for finding shortest paths in graphs utilizing Generative Flow Networks (GFlowNets). First, we examine theoretical properties of GFlowNets in non-acyclic environments in relation to shortest paths. We prove that, if the total flow is minimized, forward and backward policies traverse the environment graph exclusively along shortest paths between the initial and terminal states. Building on this result, we show that the pathfinding problem in […]

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Neural surrogates for stiff differential-algebraic equations (DAEs) face two key challenges: soft-constraint methods leave algebraic residuals that stiffness amplifies into large errors, while hard-constraint methods require trajectory data from computationally expensive stiff integrators. We introduce an extended Newton implicit layer that enforces algebraic consistency and quasi-steady-state reduction within a single differentiable solve. Given slow-state predictions from a physics-informed DeepONet, the proposed layer recovers fast and algebraic states, eliminates the stiffness-amplification pathway within each time window, and reduces the […]

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arXiv:2602.04007v1 Announce Type: new Abstract: Block space on the blockchain is scarce and must be allocated efficiently through block building. However, Ethereum’s current block-building ecosystem, MEV-Boost, has become highly centralized due to integration, which distorts competition, reduces blockspace efficiency, and obscures MEV flow transparency. To guarantee equitability and economic efficiency in block building, we propose $mathrm{Boost+}$, a system that decouples the process into collecting and ordering transactions, and ensures equal access to all collected transactions. The core of […]

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Dubai, UAE, March 9th, 2026/Chainwire/–Walbi, a blockchain-focused trading platform, has introduced no-code AI trading agents for retail crypto users. Traders can now build, test, and launch their own AI-driven agents without writing a single line of code. In short, autonomous trading is no longer limited to developers or quant teams. AI Agent Strategy Development Walbi provides a no-code environment where users can turn trading ideas into working AI agents. Instead of configuring scripts, traders simply describe their strategy […]

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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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Coffee!! Most of us start with a cup of coffee to start our day. This is no different for a GenAI-native technical writer as well. As a technical writer, you attend the first meeting of the day whereby a product manager narrates a product feature scope along with some UI mockups. As an inquisitive technical writer, you are asking many questions during the meeting. Product managers and UX designers are sharing more information about purpose of the product […]

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arXiv:2603.15811v1 Announce Type: new Abstract: We present MATCH (Multi-view Avatars from Topologically Corresponding Heads), a multi-view Gaussian registration method for high-quality head avatar creation and editing. State-of-the-art multi-view head avatar methods require time-consuming head tracking followed by expensive avatar optimization, often resulting in a total creation time of more than one day. MATCH, in contrast, directly predicts Gaussian splat textures in correspondence from calibrated multi-view images in just 0.5 seconds per frame, without requiring data preprocessing. The learned […]

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arXiv:2602.16735v1 Announce Type: new Abstract: This paper proposes a few-shot classification framework based on Large Language Models (LLMs) to predict whether the next day will have spikes in real-time electricity prices. The approach aggregates system state information, including electricity demand, renewable generation, weather forecasts, and recent electricity prices, into a set of statistical features that are formatted as natural-language prompts and fed to an LLM along with general instructions. The model then determines the likelihood that the next […]

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Pathological complete response (pCR) after neoadjuvant therapy is an important indicator of treatment response and prognosis in breast cancer. Multi-modal breast MRI provides complementary information for pCR prediction, but existing methods often assume complete modality availability and do not fully exploit the complementary value of radiomics and deep features. To address these limitations, we propose a radiomics-guided multi-modal learning framework for pCR prediction from breast MRI under incomplete modality settings. The model employs a multi-branch 2.5D encoder to […]

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