arXiv:2603.19440v1 Announce Type: new Abstract: Precision medicine aims to tailor therapeutic decisions to individual patient characteristics. This objective is commonly formalized through dynamic treatment regimes, which use statistical and machine learning methods to derive sequential decision rules adapted to evolving clinical information. In most existing formulations, these approaches produce a single optimal treatment at each stage, leading to a unique decision sequence. However, in many clinical settings, several treatment options may yield similar expected outcomes, and focusing on […]

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Victoria, Seychelles, February 5, 2026 MEXC, the fastest-growing global cryptocurrency exchange, redefining a user-first approach to digital assets through true zero-fee trading, today reported a 65% surge in January spot trading volume of newly listed tokens, as infrastructure projects—AI agents, zero-knowledge networks, and decentralized physical systems—captured the majority of sustained capital flows. n January’s 176 token listings drove spot trading volume in newly listed tokens up 65% and attracted 19% more active traders. The top performers averaged up […]

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Key Highlights What’s Really New in One R3? Opera has just released One R3 on Thursday, with new AI features & redesign to its flagship browser. The update focuses on “agentic” AI capabilities and a modular interface designed to streamline complex browsing sessions. The release marks the third annual “rebirth” of the Opera One platform. With this update, Opera focuses to shift towards an AI centric browser built on top of the Opera Neon browser. The company claims […]

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arXiv:2604.05000v1 Announce Type: new Abstract: This paper presents a closed-loop system for software lifecycle management framed as a control architecture rather than a code-generation tool. The system manages a backlog of approximately 1,602 rows across seven task families, ingests 13 structured source documents, and executes a deterministic seven-stage pipeline implemented as seven scheduled automation lanes. The automation stack comprises approximately 12,661 lines of Python across 23 scripts plus 6,907 lines of versioned prompt specifications, with checkpoint-based time budgets, […]

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Open-set active learning (OSAL) aims to identify informative samples for annotation when unlabeled data may contain previously unseen classes-a common challenge in safety-critical and open-world scenarios. Existing approaches typically rely on separately trained open-set detectors, introducing substantial training overhead and overlooking the supervisory value of labeled unknowns for improving known-class learning. In this paper, we propose E$^2$OAL (Effective and Efficient Open-set Active Learning), a unified and detector-free framework that fully exploits labeled unknowns for both stronger supervision and […]

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Auditing AI Systems: A Practical Guide to Testing Models for Bias, Compliance, Security, and Explainability Photo by Stephen Dawson on Unsplash Why accuracy alone is not enough and how organizations can audit AI systems before regulators, attackers, or users expose the failures. Artificial intelligence systems are now embedded in decisions that affect people’s lives credit approvals, fraud detection, hiring, underwriting, and customer support automation. But while AI adoption has accelerated rapidly, governance frameworks have struggled to keep up. Most […]

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arXiv:2603.22465v1 Announce Type: cross Abstract: Federated Learning (FL) is constrained by the communication and energy limitations of decentralized edge devices. While gradient sparsification via Top-K magnitude pruning effectively reduces the communication payload, it remains inherently energy-agnostic. It assumes all parameter updates incur identical downstream transmission and memory-update costs, ignoring hardware realities. We formalize the pruning process as an energy-constrained projection problem that accounts for the hardware-level disparities between memory-intensive and compute-efficient operations during the post-backpropagation phase. We propose […]

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Wave setup plays a significant role in transferring wave-induced energy to currents and causing an increase in water elevation. This excess momentum flux, known as radiation stress, motivates the coupling of circulation models with wave models to improve the accuracy of storm surge prediction, however, traditional numerical wave models are complex and computationally expensive. As a result, in practical coupled simulations, wave models are often executed at much coarser temporal resolution than circulation models. In this work, we […]

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arXiv:2602.07064v1 Announce Type: new Abstract: Physical understanding remains brittle in omni-modal models because key physical attributes are visually ambiguous and sparsely represented in web-scale data. We present OmniFysics, a compact omni-modal model that unifies understanding across images, audio, video, and text, with integrated speech and image generation. To inject explicit physical knowledge, we build a physical data engine with two components. FysicsAny produces physics-grounded instruction–image supervision by mapping salient objects to verified physical attributes through hierarchical retrieval over […]

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arXiv:2602.20379v1 Announce Type: new Abstract: Enterprise Retrieval-Augmented Generation (RAG) assistants operate in multi-turn, case-based workflows such as technical support and IT operations, where evaluation must reflect operational constraints, structured identifiers (e.g., error codes, versions), and resolution workflows. Existing RAG evaluation frameworks are primarily designed for benchmark-style or single-turn settings and often fail to capture enterprise-specific failure modes such as case misidentification, workflow misalignment, and partial resolution across turns. We present a case-aware LLM-as-a-Judge evaluation framework for enterprise multi-turn […]

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