Welcome to HackerNoon’s Projects of the Week, where we spotlight standout projects from the Proof of Usefulness Hackathon, HackerNoon’s competition designed to measure what actually matters: real utility over hype. n n Each week, we’ll highlight projects that demonstrate clear usefulness, technical execution, and real-world impact – backed by data, not buzzwords. This week, we’re excited to share three projects that have proven their utility by solving concrete problems for real users: ExpenseHut POS, FalconAI, and Risk Mirror. :::tip […]

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arXiv:2604.15318v1 Announce Type: new Abstract: While traditional game design prioritizes friction-free accessibility, the Soulslike subgenre has achieved commercial dominance through punishing difficulty and frequent failure. This paper challenges the conventional hedonistic paradigm of gaming to investigate the psychological mechanisms behind the Paradox of Failure. By integrating Csikszentmihalyi’s Flow Theory with Juul’s ludological framework, we propose the concept of Resilient Flow. We define this as a cognitive state wherein absorption is maintained not despite frustration but through the meaningful […]

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arXiv:2601.19933v1 Announce Type: new Abstract: Non-Resolution Reasoning (NRR) provides a formal framework for maintaining semantic ambiguity rather than forcing premature interpretation collapse. While the foundational architecture establishes state spaces and operators for ambiguity-preserving computation, the critical question of how natural language maps to these mathematical structures remains open. This paper introduces the text-to-state mapping function {phi} that transforms linguistic input into superposition states within the NRR framework. We formalize the Contradiction-Preservation Principle, which requires that genuinely ambiguous expressions […]

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arXiv:2604.15494v1 Announce Type: new Abstract: Deep networks that rely on prototypes-interpretable representations that can be related to the model input-have gained significant attention for balancing high accuracy with inherent interpretability, which makes them suitable for critical domains such as healthcare. However, these models are limited by their reliance on training data, which hampers their robustness to distribution shifts. While test-time adaptation (TTA) improves the robustness of deep networks by updating parameters and statistics, the prototypes of interpretable models […]

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arXiv:2603.23561v1 Announce Type: new Abstract: The sample compression conjecture is: Each concept class of VC dimension d has a compression scheme of size d.In this paper, for any concept class of finite functions, we present a labeled sample compression scheme of size equals to its VC dimension d. That is, the long standing open sample compression conjecture is resolved.

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arXiv:2602.06050v1 Announce Type: new Abstract: Despite the remarkable capabilities of Large Vision Language Models (LVLMs), they still lack detailed knowledge about specific entities. Retrieval-augmented Generation (RAG) is a widely adopted solution that enhances LVLMs by providing additional contexts from an external Knowledge Base. However, we observe that previous decoding methods for RAG are sub-optimal as they fail to sufficiently leverage multiple relevant contexts and suppress the negative effects of irrelevant contexts. To this end, we propose Relevance-aware Multi-context […]

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MLOps A hands-on, beginner-friendly guide to deploying, monitoring, and optimizing a Machine Learning model in production — from API creation to data drift detection, using the real Home Credit Default Risk dataset from Kaggle. Last Saturday morning, I was scrolling through freelance gig postings on Fiverr when I stumbled upon something that caught my attention. A small fintech startup was looking for someone to “take our trained credit scoring model and make it production-ready — API, Docker, CI/CD, […]

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arXiv:2604.16922v1 Announce Type: new Abstract: Climate research is pivotal for mitigating global environmental crises, yet the accelerating volume of multi-scale datasets and the complexity of analytical tools have created significant bottlenecks, constraining scientific discovery to fragmented and labor-intensive workflows. While the emergence Large Language Models (LLMs) offers a transformative paradigm to scale scientific expertise, existing explorations remain largely confined to simple Question-Answering (Q&A) tasks. These approaches often oversimplify real-world challenges, neglecting the intricate physical constraints and the data-driven […]

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arXiv:2602.11287v1 Announce Type: new Abstract: This paper introduces HiFloat4 (HiF4), a block floating-point data format tailored for deep learning. Each HiF4 unit packs 64 4-bit elements with 32 bits of shared scaling metadata, averaging 4.5 bits per value. The metadata specifies a three-level scaling hierarchy, capturing inter- and intra-group dynamic range while improving the utilization of the representational space. In addition, the large 64-element group size enables matrix multiplications to be executed in a highly fixed-point manner, significantly […]

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