The Business of Five-Star Service
<p>An HBR Executive Live conversation with Four Seasons CEO Alejandro Reynal.</p>
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Learning Behaviorally Grounded Item Embeddings via Personalized Temporal Contexts
Effective user modeling requires distinguishing between short-term and long-term preference evolution. While item embeddings have become a key component of recommender systems, standard approaches like Item2Vec treat user histories as unordered sets (bag-of-items), implicitly assuming that interactions separated by minutes are as semantically related as those separated by months. This simplification flattens the rich temporal structure of user behavior, obscuring the distinction between coherent consumption sessions and gradual interest drifts. In this work, we introduce TAI2Vec (Time-Aware Item-to-Vector), […]
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Domain Adaptation Without the Compute Burden for Efficient Whole Slide Image Analysis
arXiv:2603.15774v1 Announce Type: new Abstract: Computational methods on analyzing Whole Slide Images (WSIs) enable early diagnosis and treatments by supporting pathologists in detection and classification of tumors. However, the extremely high resolution of WSIs makes end-to-end training impractical compared to typical image analysis tasks. To address this, most approaches use pre-trained feature extractors to obtain fixed representations of whole slides, which are then combined with Multiple Instance Learning (MIL) for downstream tasks. These feature extractors are typically pre-trained […]
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Harness as an Asset: Enforcing Determinism via the Convergent AI Agent Framework (CAAF)
arXiv:2604.17025v1 Announce Type: new Abstract: Large Language Models (LLMs) produce a controllability gap in safety-critical engineering: even low rates of undetected constraint violations render a system undeployable. Current orchestration paradigms suffer from sycophantic compliance, context attention decay [Liu et al., 2024], and stochastic oscillation during self-correction [Huang et al., 2024]. We introduce the Convergent AI Agent Framework (CAAF), which transitions agentic workflows from open-loop generation to closed-loop Fail-Safe Determinism via three pillars: (1) Recursive Atomic Decomposition with physical […]
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Radial-VCReg: More Informative Representation Learning Through Radial Gaussianization
Self-supervised learning aims to learn maximally informative representations, but explicit information maximization is hindered by the curse of dimensionality. Existing methods like VCReg address this by regularizing first and second-order feature statistics, which cannot fully achieve maximum entropy. We propose Radial-VCReg, which augments VCReg with a radial Gaussianization loss that aligns feature norms with the Chi distribution-a defining property of high-dimensional Gaussians. We prove that Radial-VCReg transforms a broader class of distributions towards normality compared to VCReg and […]
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DeFi Crypto Mutuum Finance (MUTM) Finalizes V1 Smart Contract Audit With Halborn Security
Mutuum Finance (MUTM), a new crypto project focused on decentralized lending and borrowing, has confirmed the completion of its V1 smart contract audit conducted by Halborn Security. The update marks an important step in the project’s roadmap as it prepares for the initial deployment of its protocol. With core contracts reviewed and development milestones progressing, Mutuum Finance continues to move from design into execution within the DeFi crypto sector. Overview of The Mutuum Finance (MUTM) Mutuum Finance is […]
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From Representation to Clusters: A Contrastive Learning Approach for Attributed Hypergraph Clustering
Contrastive learning has demonstrated strong performance in attributed hypergraph clustering. Typically, existing methods based on contrastive learning first learn node embeddings and then apply clustering algorithms, such as k-means, to these embeddings to obtain the clustering results.However, these methods lack direct clustering supervision, risking the inclusion of clustering-irrelevant information in the learned graph.To this end, we propose a Contrastive learning approach for Attributed Hypergraph Clustering (CAHC), an end-to-end method that simultaneously learns node embeddings and obtains clustering results. […]
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Backpropagation in Neural Networks
Backpropagation is a fundamental component of deep learning for neural networks. Its development has significantly contributed to the widespread adoption of deep learning algorithms since the early 2000s. In this post, we explore the essential concepts associated with this method, as well as its applications and history. What is forward and backward propagation? Forward propagation in neural networks refers to the process of passing input data through the network’s layers to compute and produce an output. Each layer […]
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Comprehensive Evaluation of Large Language Models on Software Engineering Tasks: A Multi-Task Benchmark
arXiv:2602.07079v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities in software engineering, yet comprehensive benchmarks covering diverse SE activities remain limited. We present a multi-task evaluation of 11 state-of-the-art LLMs across five representative software engineering tasks: bug fixing, feature development, code refactoring, technical copywriting, and research synthesis. Our automated verification framework measures both output quality and completion efficiency. Key findings reveal that (1) models achieving identical perfect scores exhibit 22x variation in completion time, […]
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When Missing Becomes Structure: Intent-Preserving Policy Completion from Financial KOL Discourse
arXiv:2604.14333v1 Announce Type: new Abstract: Key Opinion Leader (KOL) discourse on social media is widely consumed as investment guidance, yet turning it into executable trading strategies without injecting assumptions about unspecified execution decisions remains an open problem. We observe that the gaps in KOL statements are not random deficiencies but a structured separation: KOLs express directional intent (what to buy or sell and why) while leaving execution decisions (when, how much, how long) systematically unspecified. Building on this […]
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