arXiv:2602.06239v1 Announce Type: new Abstract: We introduce PEPO (Pessimistic Ensemble based Preference Optimization), a single-step Direct Preference Optimization (DPO)-like algorithm to mitigate the well-known over-optimization issue in preference learning without requiring the knowledge of the data-generating distribution or learning an explicit reward model. PEPO achieves pessimism via an ensemble of preference-optimized policies trained on disjoint data subsets and then aggregates them through a worst case construction that favors the agreement across models. In the tabular setting, PEPO achieves […]

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arXiv:2603.24765v2 Announce Type: replace-cross Abstract: Online health communities (OHCs) are vital for fostering peer support and improving health outcomes. Support groups within these platforms can provide more personalized and cohesive peer support, yet traditional support group formation methods face challenges related to scalability, static categorization, and insufficient personalization. To overcome these limitations, we propose two novel machine learning models for automated support group formation: the Group specific Dirichlet Multinomial Regression (gDMR) and the Group specific Structured Topic Model […]

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arXiv:2602.20348v1 Announce Type: new Abstract: Experiences of being misunderstood often stem not from a lack of voice, but from mismatches between how individuals express themselves and how others listen. Such communicative mismatches arise across many social settings, including situations involving linguistic and cultural displacement. While prior HCI research has explored empathy through virtual reality, many approaches rely on narrative explanation, positioning users as observers rather than embodied participants. We present 52-Hz Whale Song, an embodied VR experience that […]

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arXiv:2510.16132v2 Announce Type: replace-cross Abstract: In this work, we present the first finite-time analysis of Q-learning with time-varying learning policies (i.e., on-policy sampling) for discounted Markov decision processes under minimal assumptions, requiring only the existence of a policy that induces an irreducible Markov chain over the state space. We establish a last-iterate convergence rate for $mathbb{E}[|Q_k – Q^*|_infty^2]$, implying a sample complexity of order $mathcal{O}(1/xi^2)$ for achieving $mathbb{E}[|Q_k – Q^*|_infty]le xi$. This matches the rate of off-policy Q-learning, […]

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London, UK Busha, a Nigerian SEC-licensed digital asset exchange, expands to the UK to serve families with connections across both regions   Busha selected Uphold as its platform partner thanks to experience in delivering on-chain infrastructure services in the UK   Busha, a crypto platform focused on building the future of money in Africa, has selected Uphold, the modern infrastructure provider for on-chain finance, to support the launch of its services in the UK. Busha serves more than a million […]

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arXiv:2601.08880v1 Announce Type: new Abstract: As automated systems increasingly transition from decision support to direct execution, the problem of accountability shifts from decision quality to execution legitimacy. While optimization, execution, and feedback mechanisms are extensively modeled in contemporary AI and control architectures, the structural role of judgment remains undefined. Judgment is typically introduced as an external intervention rather than a native precondition to execution. This work does not propose a new decision-making algorithm or safety heuristic, but identifies […]

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arXiv:2601.14435v1 Announce Type: new Abstract: Despite its importance for well-being, spiritual care remains under-explored in HCI, while the adoption of technology in clinical spiritual care lags behind other healthcare fields. Prior work derived a definition of “spiritual support” through co-design workshops with stakeholders in online health communities. This paper contributes: (1) a revision of that definition through member checking with professional spiritual care providers (SCPs); (2) a novel design framework — SPIRIT — which can help to expand […]

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arXiv:2602.13227v1 Announce Type: new Abstract: 6G networks are expected to be AI-native, intent-driven, and economically programmable, requiring fundamentally new approaches to network slice orchestration. Existing slicing frameworks, largely designed for 5G, rely on static policies and manual workflows and are ill-suited for the dynamic, multi-domain, and service-centric nature of emerging 6G environments. In this paper, we propose an agentic AI control plane architecture for 6G network slice orchestration, monitoring, and trading that treats orchestration as a holistic control […]

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This paper introduces MR-Search, an in-context meta reinforcement learning (RL) formulation for agentic search with self-reflection. Instead of optimizing a policy within a single independent episode with sparse rewards, MR-Search trains a policy that conditions on past episodes and adapts its search strategy across episodes. MR-Search learns to learn a search strategy with self-reflection, allowing search agents to improve in-context exploration at test-time. Specifically, MR-Search performs cross-episode exploration by generating explicit self-reflections after each episode and leveraging them […]

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shows how stories, like living systems, sustain themselves through feedback and adaptation. Bruce Clarke links literature to cybernetics—the science of self-organizing systems—to reveal that both humans and their narratives evolve by observing, adjusting, and rewriting themselves. In short: storytelling isn’t just art—it’s a survival algorithm for consciousness.

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