arXiv:2602.23530v1 Announce Type: new Abstract: Large-scale e-commerce search must surface a broad set of items from a vast catalog, ranging from bestselling products to new, trending, or seasonal items. Modern systems therefore rely on multiple specialized retrieval channels to surface products, each designed to satisfy a specific objective. A key challenge is how to effectively merge documents from these heterogeneous channels into a single ranked list under strict latency constraints while optimizing for business KPIs such as user […]

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arXiv:2601.11134v1 Announce Type: cross Abstract: Credit risk models are a critical decision-support tool for financial institutions, yet tightening data-protection rules (e.g., GDPR, CCPA) increasingly prohibit cross-border sharing of borrower data, even as these models benefit from cross-institution learning. Traditional default prediction suffers from two limitations: binary classification ignores default timing, treating early defaulters (high loss) equivalently to late defaulters (low loss), and centralized training violates emerging regulatory constraints. We propose a Federated Survival Learning framework with Bayesian Differential […]

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Most automated pricing systems are built to optimize. n Very few are built to survive failure. When a system manages 1,000,000+ SKUs and processes 500,000 daily price updates across multiple marketplaces, pricing stops being a growth feature. It becomes financial infrastructure. At that scale: A single incorrect price can erase 30% margin instantly A misinterpreted API response can affect thousands of SKUs within minutes A faulty rollout can create $15,000–$20,000 daily exposure per seller The problem is not […]

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Artificial intelligence is advancing faster than ever before and often reaches impressive heights. The growth of AI-based startups has accelerated 14 times since 2020. AI has really penetrated any and every corner of our lives: from content creation to fintech to healthcare. However, the problem with AI is that it often doesn’t exactly do what we request of it. It often represents a black box that interprets our queries in its own way. And sometimes it can be […]

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:::info Authors: Feiyang Tang Bjarte M. Østvold ::: Table Of Links Abstract 1 Introduction 2 Background 3 Privacy-Relevant Methods 4 Identifying API Privacy-relevant Methods 5 Labels for Personal Data Processing 6 Process of Identifying Personal Data 7 Data-based Ranking of Privacy-relevant Methods 8 Application to Privacy Code Review 9 Related Work Conclusion, Future Work, Acknowledgement And References Abstract Privacy code review is a critical process that enables developers and legal experts to ensure compliance with data protection regulations. […]

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arXiv:2602.09173v1 Announce Type: new Abstract: Recent progress in reinforcement learning with verifiable rewards (RLVR) shows that small, specialized language models (SLMs) can exhibit structured reasoning without relying on large monolithic LLMs. We introduce soft hidden-state collaboration, where multiple heterogeneous frozen SLM experts are integrated through their internal representations via a trainable attention interface. Experiments on Reasoning Gym and GSM8K show that this latent integration is competitive with strong single-model RLVR baselines. Ablations further reveal a dual mechanism of […]

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arXiv:2512.17688v2 Announce Type: replace-cross Abstract: We present a novel theoretical analysis of Federated SARSA (FedSARSA) with linear function approximation and local training. We establish convergence guarantees for FedSARSA in the presence of heterogeneity, both in local transitions and rewards, providing the first sample and communication complexity bounds in this setting. At the core of our analysis is a new, exact multi-step error expansion for single-agent SARSA, which is of independent interest. Our analysis precisely quantifies the impact of […]

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Microsoft researchers have introduced CORPGEN, an architecture-agnostic framework designed to manage the complexities of realistic organizational work through autonomous digital employees. While existing benchmarks evaluate AI agents on isolated, single tasks, real-world corporate environments require managing dozens of concurrent, interleaved tasks with complex dependencies. The research team identifies this distinct problem class as Multi-Horizon Task Environments (MHTEs). The Performance Gap in MHTEs Empirical testing reveals that baseline computer using agents (CUAs) experience significant performance degradation when moved from […]

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arXiv:2603.05721v1 Announce Type: new Abstract: We consider the task of estimating the trace of a matrix function, ${rm tr}(f({bf A}))$, of a large symmetric positive semi-definite matrix ${bf A}$. This problem arises in multiple applications, including kernel methods and inverse problems. A key challenge across existing trace estimation methods is the need for matrix-vector products (matvecs) with $f({bf A})$, which can be very expensive. In this article, we introduce a novel trace estimator, FlexTrace, an exchangeable, single-pass method […]

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