technocracy

arXiv:2602.11287v1 Announce Type: new Abstract: This paper introduces HiFloat4 (HiF4), a block floating-point data format tailored for deep learning. Each HiF4 unit packs 64 4-bit elements with 32 bits of shared scaling metadata, averaging 4.5 bits per value. The metadata specifies a three-level scaling hierarchy, capturing inter- and intra-group dynamic range while improving the utilization of the representational space. In addition, the large 64-element group size enables matrix multiplications to be executed in a highly fixed-point manner, significantly […]

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arXiv:2601.09033v1 Announce Type: new Abstract: Generative AI (GenAI) is increasingly used in academic writing, yet its effects on students’ writing self-efficacy remain contingent on how assistance is configured. This pilot study investigates how ideation-level, sentence-level, full-process, and no AI support differentially shape undergraduate writers’ self-efficacy using a 2 by 2 experimental design with Korean undergraduates completing argumentative writing tasks. Results indicate that AI assistance does not uniformly enhance self-efficacy full AI support produced high but stable self-efficacy alongside […]

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arXiv:2604.04977v1 Announce Type: new Abstract: Software supply chain security compromises often stem from cascaded interactions of vulnerabilities, for example, between multiple vulnerable components. Yet, Software Bill of Materials (SBOM)-based pipelines for security analysis typically treat scanner findings as independent per-CVE (Common Vulnerabilities and Exposures) records. We propose a new research direction based on learning multi-vulnerability attack chains through a novel SBOM-driven graph-learning approach. This treats SBOM structure and scanner outputs as a dependency-constrained evidence graph rather than a […]

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arXiv:2601.07061v4 Announce Type: replace Abstract: We introduce the setting of continuous index learning, in which a function of many variables varies only along a small number of directions at each point. For efficient estimation, it is beneficial for a learning algorithm to adapt, near each point $x$, to the subspace that captures the local variability of the function $f$. We pose this task as kernel adaptation along a manifold with noise, and introduce Local EGOP learning, a recursive […]

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Published on February 4, 2026 5:39 PM GMT When economists think and write about the post-AGI world, they often rely on the implicit assumption that parameters may change, but fundamentally, structurally, not much happens. And if it does, it’s maybe one or two empirical facts, but nothing too fundamental.  This mostly worked for all sorts of other technologies, where technologists would predict society to be radically transformed e.g. by everyone having most of humanity’s knowledge available for free […]

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arXiv:2602.10289v1 Announce Type: new Abstract: Robots operating in unstructured human environments inevitably encounter failures, especially in robot caregiving scenarios. While humans can often help robots recover, excessive or poorly targeted queries impose unnecessary cognitive and physical workload on the human partner. We present a human-in-the-loop failure-recovery framework for modular robotic policies, where a policy is composed of distinct modules such as perception, planning, and control, any of which may fail and often require different forms of human feedback. […]

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Classifier-free Guidance (CFG) lets practitioners trade-off fidelity against diversity in Diffusion Models (DMs). The practicality of CFG is however hindered by DMs sampling cost. On the other hand, Consistency Models (CMs) generate images in one or a few steps, but existing guidance methods require knowledge distillation from a separate DM teacher, limiting CFG to Consistency Distillation (CD) methods. We propose Joint Flow Distribution Learning (JFDL), a lightweight alignment method enabling guidance in a pre-trained CM. With a pre-trained […]

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arXiv:2604.15383v1 Announce Type: new Abstract: Large audio-language models (LALMs) generalize across speech, sound, and music, but unified decoders can exhibit a emph{temporal smoothing bias}: transient acoustic cues may be underutilized in favor of temporally smooth context that is better supported by language priors, leading to less specific audio-grounded outputs. We propose emph{Temporal Contrastive Decoding} (TCD), a training-free decoding method for unified LALMs that mitigates this effect at inference time. TCD constructs a temporally blurred slow-path view by smoothing […]

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