February 10, 2026 – OSL Group (863.HK), Asia’s leading stablecoin trading and payment platform, today announced the official launch of USDGO, a regulated, enterprise-grade U.S. dollar stablecoin. As a cornerstone of OSL Group’s global payment infrastructure, USDGO is positioned for institutional settlement and corporate payments, serving the cross-border business ecosystem of Asian enterprises. Leveraging its enterprise-level features and services, USDGO provides users with a compliant tool for liquidity management and settlement. It is dedicated to the long-term empowerment […]

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arXiv:2601.05335v1 Announce Type: cross Abstract: Canonical Polyadic (CP) tensor decomposition is a workhorse algorithm for discovering underlying low-dimensional structure in tensor data. This is accomplished in conventional CP decomposition by fitting a low-rank tensor to data with respect to the least-squares loss. Generalized CP (GCP) decompositions generalize this approach by allowing general loss functions that can be more appropriate, e.g., to model binary and count data or to improve robustness to outliers. However, GCP decompositions do not explicitly […]

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Chinese electric vehicles (EVs) have been making a big splash in markets worldwide, with one major target for Chinese-made EVs being the United Arab Emirates (UAE). The UAE is a prime location for Chinese EVs to enter the Gulf market, enabling increased regional credibility while providing a robust market for EV development. The UAE also stands out as an investment opportunity for Chinese state-owned enterprises (SOEs) and other groups from an electric grid perspective, with China taking the […]

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arXiv:2602.13280v1 Announce Type: new Abstract: Simulating student learning behaviors in open-ended problem-solving environments holds potential for education research, from training adaptive tutoring systems to stress-testing pedagogical interventions. However, collecting authentic data is challenging due to privacy concerns and the high cost of longitudinal studies. While Large Language Models (LLMs) offer a promising path to student simulation, they suffer from competency bias, optimizing for efficient correctness rather than the erratic, iterative struggle characteristic of novice learners. We present BEAGLE, […]

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arXiv:2601.02505v1 Announce Type: new Abstract: Complex multi-robot missions often require heterogeneous teams to jointly optimize task allocation, scheduling, and path planning to improve team performance under strict constraints. We formalize these complexities into a new class of problems, dubbed Spatio-Temporal Efficacy-optimized Allocation for Multi-robot systems (STEAM). STEAM builds upon trait-based frameworks that model robots using their capabilities (e.g., payload and speed), but goes beyond the typical binary success-failure model by explicitly modeling the efficacy of allocations as trait-efficacy […]

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The Agent Governance Trust Protocol (AGTP) is an open-source tool for certifying AI agent safety. It weights controls like kill switches and guardrails based on effectiveness. We’re running a Delphi study to validate these weights with expert input, think empirical backing for AI governance. One example currently: Hardware kill switch at 0.98 vs. prompt guardrail at 0.27. Is that 3.6x difference spot on? Your scores will tell! Add brief reasons. Review anon peer feedback in later rounds and […]

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arXiv:2602.15882v1 Announce Type: new Abstract: General vision-language models increasingly support unified spatiotemporal reasoning over long video streams, yet deploying such capabilities on robots remains constrained by the prohibitive latency of processing long-horizon histories and generating high-dimensional future predictions. To bridge this gap, we present FUTURE-VLA, a unified architecture that reformulates long-horizon control and future forecasting as a monolithic sequence-generation task. Adopting a dual-sided efficiency paradigm, FUTURE-VLA leverages a temporally adaptive compression strategy to maximize spatiotemporal information density, enabling […]

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