February 2026

arXiv:2602.18445v1 Announce Type: new Abstract: The advancement of artificial intelligence has transformed user interface design by enabling adaptive and personalized systems. Alongside these benefits, AI driven interfaces have also enabled the emergence of dark patterns, which are manipulative design strategies that influence user behavior for financial or business gain. As AI systems learn from data that already contains deceptive practices, they can replicate and optimize these patterns in increasingly subtle and personalized ways. This paper examines AI generated […]

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arXiv:2602.18444v1 Announce Type: new Abstract: Conversational AI has significant potential in the healthcare sector, but many existing systems fall short in emotional intelligence, fairness, and politeness, which are essential for building patient trust. This gap reduces the effectiveness of digital health solutions and can increase user anxiety. This study addresses the challenge of integrating ethical communication principles by designing and evaluating LunaAI, a healthcare chatbot prototype. Using a user-centered design approach informed by a structured literature review, we […]

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arXiv:2602.18443v1 Announce Type: new Abstract: Psychosocial online counselling frequently encounters generic subject lines that impede efficient case prioritisation. This study evaluates eleven large language models generating six-word subject lines for German counselling emails through hierarchical assessment – first categorising outputs, then ranking within categories to enable manageable evaluation. Nine assessors (counselling professionals and AI systems) enable analysis via Krippendorff’s $alpha$, Spearman’s $rho$, Pearson’s $r$ and Kendall’s $tau$. Results reveal performance trade-offs between proprietary services and privacy-preserving open-source alternatives, […]

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arXiv:2602.18439v1 Announce Type: new Abstract: Vision-language models like CLIP have demonstrated remarkable zero-shot capabilities, yet their adaptation to federated learning scenarios presents significant challenges, particularly regarding generalization to unseen classes. The original FedTPG paper cite{Qiu2024} addresses this limitation by introducing a text driven prompt generation network that dynamically creates prompts conditioned on class names, enabling better cross-class generalization in federated settings. In this work, we present a faithful replication study of FedTPG, evaluating the pre-trained model on six […]

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arXiv:2602.18438v1 Announce Type: new Abstract: Efficient resource (re-)allocation is a critical challenge in optimizing productivity and sustainability within multi-party supply networks. In this work, we introduce the textsc{Red-Blue Reinforcement} (R-BR) problem, where a service provider under budgetary constraints must minimize client reallocations to reduce the required number of servers they should maintain by a specified amount. We conduct a systematic algorithmic study, providing three exact algorithms that scale well as the input grows (FPT), which could prove useful […]

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arXiv:2602.18437v1 Announce Type: new Abstract: Generating with citations is crucial for trustworthy Large Language Models (LLMs), yet even advanced LLMs often produce mismatched or irrelevant citations. Existing methods over-optimize citation fidelity while overlooking relevance to the user query, which degrades answer quality and robustness in real-world settings with noisy or irrelevant retrieved content. Moreover, the prevailing single-pass paradigm struggles to deliver optimal answers in long-form generation that requiring multiple citations. To address these limitations, we propose FineRef, a […]

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We got new model releases from xAI and Anthropic last week, and I wanted to give my quick impressions to help you know if/when you should care. This is just after a half day of testing, so my impressions may change, but… we’re usually locked in on the vibe pretty quickly. By the way, even if you aren’t interested in Grok, take a read of the analysis below — we’ll talk about subagent systems in a way that will […]

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This article argues that Substack is structurally designed to prevent guru-style power concentration. By aligning incentives around paid subscriptions instead of advertising, enabling peer-driven discovery, prioritizing ownership and exit rights, and building around long-form, trust-based relationships, Substack creates a distributed “garden” ecosystem. In an era where AI can mass-produce authoritative content, platforms that reward originality, depth, and earned trust may become increasingly valuable.

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Post-training large foundation models with reinforcement learning typically relies on massive and heterogeneous datasets, making effective curriculum learning both critical and challenging. In this work, we propose ACTOR-CURATOR, a scalable and fully automated curriculum learning framework for reinforcement learning post-training of large language models (LLMs). ACTOR-CURATOR learns a neural curator that dynamically selects training problems from large problem banks by directly optimizing for expected policy performance improvement. We formulate problem selection as a non-stationary stochastic bandit problem, derive […]

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Emotion recognition from multi-modal physiological and behavioral signals plays a pivotal role in affective computing, yet most existing models remain constrained to the prediction of singular emotions in controlled laboratory settings. Real-world human emotional experiences, by contrast, are often characterized by the simultaneous presence of multiple affective states, spurring recent interest in mixed emotion recognition as an emotion distribution learning problem. Current approaches, however, often neglect the valence consistency and structured correlations inherent among coexisting emotions. To address […]

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