March 2026

arXiv:2603.06599v1 Announce Type: new Abstract: Artificial intelligence pipelines — spanning data collection, model training, deployment, and post-deployment monitoring — concentrate ethical risks that intensify with multimodal and agentic systems. Existing governance instruments, including the EU AI Act, the IEEE 7000 series, and the NIST AI Risk Management Framework, provide high-level guidance but often lack enforceable, end-to-end operational controls. This paper presents an ethics-by-design control architecture that embeds consequentialist, deontological, and virtue-ethical reasoning into stage-specific enforcement mechanisms across the […]

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arXiv:2603.06598v1 Announce Type: new Abstract: In this study, the vulnerability of Sub-Saharan African migrants to AI-enabled deception, specifically the risk of exposure to scams targeting them, was evaluated. I hypothesized that the ability to distinguish human-generated content from AI-generated content had far-reaching implications beyond content assessment to determining vulnerability to AI-enabled deception. Data collected from a survey of 31 professionals and migrants from SSA across Europe and North America, covering themes on Demographics and Transnational Context, Core AI […]

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arXiv:2603.06597v1 Announce Type: new Abstract: This paper proposes a two-time scale neurodynamic duplex approach to solve distributionally robust geometric joint chance-constrained optimization problems. The probability distributions of the row vectors are not known in advance and belong to a certain distributional uncertainty set. In our paper, we study three uncertainty sets for the unknown distributions. The neurodynamic duplex is designed based on three projection equations. The main contribution of our work is to propose a neural network-based method […]

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arXiv:2603.06595v1 Announce Type: new Abstract: With large language models (LLMs) now performing strongly across diverse tasks, there is growing demand for them to personalize outputs for individual users. Personalization is typically framed as an additional layer on top of a base NLP task, requiring model responses to meet user-specific needs while still accomplishing the underlying task. From a token-level perspective, different tokens in a response contribute to personalization to varying degrees. Tokens with higher personalization relevance should therefore […]

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Learning effective netlist representations is fundamentally constrained by the scarcity of labeled datasets, as real designs are protected by Intellectual Property (IP) and costly to annotate. Existing work therefore focuses on small-scale circuits with clean labels, limiting scalability to realistic designs. Meanwhile, Large Language Models (LLMs) can generate Register-Transfer-Level (RTL) at scale, but their functional incorrectness has hindered their use in circuit analysis. In this work, we make a key observation: even when LLM-Generated RTL is functionally imperfect, […]

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Dense image captioning is critical for cross-modal alignment in vision-language pretraining and text-to-image generation, but scaling expert-quality annotations is prohibitively expensive. While synthetic captioning via strong vision-language models (VLMs) is a practical alternative, supervised distillation often yields limited output diversity and weak generalization. Reinforcement learning (RL) could overcome these limitations, but its successes have so far been concentrated in verifiable domains that rely on deterministic checkers — a luxury not available in open-ended captioning. We address this bottleneck […]

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Current expansion-based methods for Class Incremental Learning (CIL) effectively mitigate catastrophic forgetting by freezing old features. However, such task-specific features learned from the new task may collide with the old features. From a causal perspective, spurious feature correlations are the main cause of this collision, manifesting in two scopes: (i) guided by empirical risk minimization (ERM), intra-task spurious correlations cause task-specific features to rely on shortcut features. These non-robust features are vulnerable to interference, inevitably drifting into the […]

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Reinforcement Learning from Verifiable Rewards (RLVR) significantly enhances large language models (LLMs) reasoning but severely suffers from calibration degeneration, where models become excessively over-confident in incorrect answers. Previous studies devote to directly incorporating calibration objective into existing optimization target. However, our theoretical analysis demonstrates that there exists a fundamental gradient conflict between the optimization for maximizing policy accuracy and minimizing calibration error. Building on this insight, we propose DCPO, a simple yet effective framework that systematically decouples reasoning […]

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Dubai, UAE, March 9th, 2026/Chainwire/–Walbi, a blockchain-focused trading platform, has introduced no-code AI trading agents for retail crypto users. Traders can now build, test, and launch their own AI-driven agents without writing a single line of code. In short, autonomous trading is no longer limited to developers or quant teams. AI Agent Strategy Development Walbi provides a no-code environment where users can turn trading ideas into working AI agents. Instead of configuring scripts, traders simply describe their strategy […]

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Table of Links Abstract and 1. Introduction Methods Quantitative Results and Creativity Support Index Qualitative Results from Focus Group Discussions Discussion Mitigations and Conclusion and Acknowledgments Ethical Guidance References A. Related Work on Computational Humour, AI and Comedy B. Participant Questionaire C. Focus 4 QUALITATIVE RESULTS FROM FOCUS GROUP DISCUSSIONS In this section, we summarize the major themes that emerged from the focus group discussions. Each is presented alongside supporting quotes from various participants (anonymised as p1, p2, […]

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