arXiv:2601.15299v1 Announce Type: new Abstract: Topic modeling is a crucial technique for extracting latent themes from unstructured text data, particularly valuable in analyzing survey responses. However, traditional methods often only consider free-text responses and do not natively incorporate structured or categorical survey responses for topic modeling. And they produce abstract topics, requiring extensive human interpretation. To address these limitations, we propose the Multi-Agent LLM Topic Modeling Framework (MALTopic). This framework decomposes topic modeling into specialized tasks executed by […]

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arXiv:2410.04264v3 Announce Type: replace Abstract: Dynamic feature transformation (the rich regime) does not always align with predictive performance (better representation), yet accuracy is often used as a proxy for richness, limiting analysis of their relationship. We propose a computationally efficient, performance-independent metric of richness grounded in the low-rank bias of rich dynamics, which recovers neural collapse as a special case. The metric is empirically more stable than existing alternatives and captures known lazy-torich transitions (e.g., grokking) without relying […]

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arXiv:2603.09994v1 Announce Type: new Abstract: Compositionality is considered central to language abilities. As performant language systems, how do large language models (LLMs) do on compositional tasks? We evaluate adjective-noun compositionality in LLMs using two complementary setups: prompt-based functional assessment and a representational analysis of internal model states. Our results reveal a striking divergence between task performance and internal states. While LLMs reliably develop compositional representations, they fail to translate consistently into functional task success across model variants. Consequently, […]

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arXiv:2405.19206v2 Announce Type: replace Abstract: Deep neural networks (DNNs) on Riemannian manifolds have garnered increasing interest in various applied areas. For instance, DNNs on spherical and hyperbolic manifolds have been designed to solve a wide range of computer vision and nature language processing tasks. One of the key factors that contribute to the success of these networks is that spherical and hyperbolic manifolds have the rich algebraic structures of gyrogroups and gyrovector spaces. This enables principled and effective […]

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arXiv:2504.04371v3 Announce Type: replace-cross Abstract: Traditional Support Vector Machine (SVM) classification is carried out by finding the max-margin classifier for the training data that divides the margin space into two equal sub-spaces. This study demonstrates limitations of performing Support Vector Classification in non-Euclidean spaces by establishing that the underlying principle of max-margin classification and Karush Kuhn Tucker (KKT) boundary conditions are optimal only in the Euclidean vector spaces. The study establishes a methodology to perform Support Vector Classification […]

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arXiv:2602.20204v1 Announce Type: new Abstract: AI kernel compilation for edge devices depends on the compiler’s ability to exploit parallelism and hide memory latency in the presence of hierarchical memory and explicit data movement. This paper reports a benchmark methodology and corresponding results for three compiler-controlled mechanisms in an MLIR-based compilation pipeline: vectorization (Vec), multi-threading (MT) across hardware contexts, and double buffering (DB) using ping–pong scratchpad buffers to overlap DMA transfers with compute. Using Triton/Inductor-generated kernels, we present an […]

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arXiv:2411.09686v3 Announce Type: replace Abstract: Regressing a function $F$ on $mathbb{R}^d$ without the statistical and computational curse of dimensionality requires special statistical models, for example that impose geometric assumptions on the distribution of the data (e.g., that its support is low-dimensional), or strong smoothness assumptions on $F$, or a special structure $F$. Among the latter, compositional models $F=fcirc g$ with $g$ mapping to $mathbb{R}^r$ with $rll d$ include classical single- and multi-index models, as well as neural networks. […]

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I’m going to introduce a new word. Not because the idea is new. But because the moment demands a name for it. The word is Outtelligence. And if artificial intelligence continues accelerating the way it is, this may become the most important cognitive skill of the next decade. If you study capital markets long enough, you learn something simple: When something becomes abundant, its value drops. Information used to be scarce. Now it’s infinite. Analysis used to be […]

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