Foundation vision, audio, and language models enable zero-shot performance on downstream tasks via their latent representations. Recently, unsupervised learning of data group structure with deep learning methods has gained popularity. TURTLE, a state of the art deep clustering algorithm, uncovers data labeling without supervision by alternating label and hyperplane updates, maximizing the hyperplane margin, in a similar fashion to support vector machines (SVMs). However, TURTLE assumes clusters are balanced; when data is imbalanced, it yields non-ideal hyperplanes that […]

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arXiv:2601.03378v1 Announce Type: new Abstract: Repository-level code completion benefits from retrieval-augmented generation (RAG). However, controlling cross-file evidence is difficult because chunk utility is often interaction-dependent: some snippets help only when paired with complementary context, while others harm decoding when they conflict. We propose RepoShapley, a coalition-aware context filtering framework supervised by Shapley-style marginal contributions. Our module ChunkShapley constructs offline labels by (i) single-chunk probing with teacher-forced likelihood to estimate signed, weighted effects, (ii) a surrogate game that captures […]

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arXiv:2507.21429v2 Announce Type: replace Abstract: Gradient descent (GD) on deep neural network loss landscapes is non-convex, yet often converges far faster in practice than classical guarantees suggest. Prior work shows that within locally quasi-convex regions (LQCRs), GD converges to stationary points at sublinear rates, leaving the commonly observed near-exponential training dynamics unexplained. We show that, under a mild local Neural Tangent Kernel (NTK) stability assumption, the loss satisfies a PL-type error bound within these regions, yielding a Locally […]

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arXiv:2601.00238v1 Announce Type: new Abstract: Perching allows unmanned aerial vehicles (UAVs) to reduce energy consumption, remain anchored for surface sampling operations, or stably survey their surroundings. Previous efforts for perching on vertical surfaces have predominantly focused on lightweight mechanical design solutions with relatively scant system-level integration. Furthermore, perching strategies for vertical surfaces commonly require high-speed, aggressive landing operations that are dangerous for a surveyor drone with sensitive electronics onboard. This work presents the preliminary investigation of a perching […]

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arXiv:2503.18896v2 Announce Type: replace-cross Abstract: A statistical model is said to be calibrated if the resulting mean estimates perfectly match the true means of the underlying responses. Aiming for calibration is often not achievable in practice as one has to deal with finite samples of noisy observations. A weaker notion of calibration is auto-calibration. An auto-calibrated model satisfies that the expected value of the responses for a given mean estimate matches this estimate. Testing for autocalibration has only […]

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arXiv:2601.05473v1 Announce Type: new Abstract: This paper presents a conceptual and methodological framework for large language model (LLM) based student simulation in educational settings. The authors identify a core failure mode, termed the “competence paradox” in which broadly capable LLMs are asked to emulate partially knowledgeable learners, leading to unrealistic error patterns and learning dynamics. To address this, the paper reframes student simulation as a constrained generation problem governed by an explicit Epistemic State Specification (ESS), which defines […]

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Humanity, Teilhard argues, is awakening in an evolutionary storm—one pulling us toward a larger, more unified “Super-Humanity.” In this widening scale of mind and world, Christ emerges not as a distant memory but as the universe’s glowing center, the Omega drawing all things together. From this cosmic magnetism arises a “Super-Charity”: love powerful enough to drive evolution itself.

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arXiv:2601.04263v1 Announce Type: new Abstract: Knowledge distillation has proven effective for model compression by transferring knowledge from a larger network called the teacher to a smaller network called the student. Current knowledge distillation in time series is predominantly based on logit and feature aligning techniques originally developed for computer vision tasks. These methods do not explicitly account for temporal data and fall short in two key aspects. First, the mechanisms by which the transferred knowledge helps the student […]

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I’ve observed thousands of founders and thought a lot about what it takes to make a huge amount of money or to create something important. Usually, people start off wanting the former and end up wanting the latter. Here are 13 thoughts about how to achieve such outlier success. Everything here is easier to do once you’ve already reached a baseline degree of success (through privilege or effort) and want to put in the work to turn that […]

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