arXiv:2603.03417v1 Announce Type: new Abstract: Parallel test-time scaling, which generates multiple candidate solutions for a single problem, is a powerful technique for improving large language model performance. However, it is hindered by two key bottlenecks: accurately selecting the correct solution from the candidate pool, and the high inference latency from generating many full solutions. We argue that both challenges are fundamentally linked to verifier calibration. A well-calibrated verifier not only improves answer selection, but also enables early-stopping strategies […]

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City-scale, in-the-wild Augmented Reality (AR) learning paths must remain operable under Bring Your Own Device (BYOD) heterogeneity, outdoor tracking degradation, public-space safety, and interruption recovery. This study conceptualizes the Art Nouveau Path as an AR learning service and makes a theoretical contribution by proposing a Determinant-driven Requirements traceability model that treats implementation Determinants as Requirements signals and links them to testable Requirements, transfer Artefacts, and evidence anchors for replication. Methods combined 8 Points of Interest (POIs) and 36 […]

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arXiv:2602.09188v1 Announce Type: new Abstract: As chip-to-chip silicon photonics gain traction for their bandwidth and energy efficiency, their circuit-switched nature raises a fundamental question for collective communication: when and how should the interconnect be reconfigured to realize these benefits? Establishing direct optical paths can reduce congestion and propagation delay, but each reconfiguration incurs non-negligible overhead, making naive per-step reconfiguration impractical. We present Harvest, a systematic approach for synthesizing topology reconfiguration schedules that minimize collective completion time in photonic […]

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Federated learning (FL) suffers from performance degradation due to the inevitable presence of noisy annotations in distributed scenarios. Existing approaches have advanced in distinguishing noisy samples from the dataset for label correction by leveraging loss values. However, noisy samples recognition relying on scalar loss lacks reliability for FL under heterogeneous scenarios. In this paper, we rethink this paradigm from a representation perspective and propose method~(textbf{Fed}erated under textbf{R}epresentation textbf{G}emometry), which follows textbf{the principle of “representation geometry priority”} to recognize […]

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arXiv:2602.17787v1 Announce Type: new Abstract: Generative model ecosystems increasingly operate as competitive multi-platform markets, where platforms strategically select models from a shared pool and users with heterogeneous preferences choose among them. Understanding how platforms interact, when market equilibria exist, how outcomes are shaped by model-providers, platforms, and user behavior, and how social welfare is affected is critical for fostering a beneficial market environment. In this paper, we formalize a three-layer model-platform-user market game and identify conditions for the […]

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Summary: The potential of deep learning (DL) in genomic selection (GS) is constrained by the significant technical expertise required to design and implement neural networks. While DL has revolutionized fields like language processing and structural biology, its application in GS has not yet consistently outperformed traditional models like mixed linear models. The key to unlocking DL’s power in GS lies in the exploration of network architectures tailored to genomic data, a process that demands intensive programming and poses […]

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