arXiv:2602.10321v1 Announce Type: new Abstract: Retrieving known items from vague descriptions, Tip-of-the-Tongue (ToT) retrieval, remains a significant challenge. We propose using a single call to a generic 8B-parameter LLM for query reformulation, bridging the gap between ill-formed ToT queries and specific information needs. This method is particularly effective where standard Pseudo-Relevance Feedback fails due to poor initial recall. Crucially, our LLM is not fine-tuned for ToT or specific domains, demonstrating that gains stem from our prompting strategy rather […]

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Most startup marketing mistakes don’t look like mistakes when they’re made. They look reasonable. Your campaign feels promising. The numbers are “okay.” There’s pressure to move fast. You approve spending not because you’re confident, but because doing nothing feels worse. Weeks later, the budget is gone, results are unclear, and no one is sure whether the decision was wrong or just unlucky. This is the quiet tax of startup marketing: regret. Why Marketing Spend Feels So Personal in […]

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arXiv:2601.04293v1 Announce Type: new Abstract: As Autonomous Driving Systems (ADS) progress towards commercial deployment, there is an increasing focus on ensuring their safety and reliability. While considerable research has been conducted on testing methods for detecting faults in ADS, very little attention has been paid to debugging in ADS. Debugging is an essential process that follows test failures to localise and repair the faults in the systems to maintain their safety and reliability. This Systematic Mapping Study (SMS) […]

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Predicting the melting temperature (Tm) of multi-component and high-entropy alloys (HEAs) is critical for high-temperature applications but computationally expensive using traditional CALPHAD or DFT methods. In this work, we develop a gradient-boosted decision tree (XGBoost) model to predict Tm for complex alloys based on elemental properties. To ensure physical consistency, we address the issue of data leakage by excluding temperature-dependent thermodynamic descriptors (such as Gibbs free energy of mixing) and instead rely on physically motivated elemental features. The […]

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This study proposes the General Bayes framework for policy learning. We consider decision problems in which a decision-maker chooses an action from an action set to maximize its expected welfare. Typical examples include treatment choice and portfolio selection. In such problems, the statistical target is a decision rule, and the prediction of each outcome $Y(a)$ is not necessarily of primary interest. We formulate this policy learning problem by loss-based Bayesian updating. Our main technical device is a squared-loss […]

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arXiv:2601.02512v1 Announce Type: new Abstract: The rapid adoption of large language models (LLMs) has raised concerns about their substantial energy consumption, especially when deployed at industry scale. While several techniques have been proposed to address this, limited empirical evidence exists regarding the effectiveness of applying them to LLM-based industry applications. To fill this gap, we analyzed a chatbot application in an industrial context at Schuberg Philis, a Dutch IT services company. We then selected four techniques, namely Small […]

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How are you, hacker? 🪐 What’s happening in tech today, January 23, 2026? The HackerNoon Newsletter brings the HackerNoon homepage straight to your inbox. On this day, Apple launched the Macintosh Computer in 1984, President Nixon Announced End to the grueling Vietnam War in 1973, HackerNoon published its first story! in 2016, and we present you with these top quality stories. From How to Add the AWS WAF CAPTCHA to an Angular Application to How to Enter the […]

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arXiv:2602.20567v1 Announce Type: cross Abstract: Push-Sum-based decentralized learning enables optimization over directed communication networks, where information exchange may be asymmetric. While convergence properties of such methods are well understood, their finite-iteration stability and generalization behavior remain unclear due to structural bias induced by column-stochastic mixing and asymmetric error propagation. In this work, we develop a unified uniform-stability framework for the Stochastic Gradient Push (SGP) algorithm that captures the effect of directed topology. A key technical ingredient is an […]

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