arXiv:2511.18060v2 Announce Type: replace Abstract: Wasserstein-Fisher-Rao (WFR) gradient flows have been recently proposed as a powerful sampling tool that combines the advantages of pure Wasserstein (W) and pure Fisher-Rao (FR) gradient flows. Existing algorithmic developments implicitly make use of operator splitting techniques to numerically approximate the WFR partial differential equation, whereby the W flow is evaluated over a given step size and then the FR flow (or vice versa). This works investigates the impact of the order in […]

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Sponsor content from AWS and Effectual.

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How Pattern Recognition Architecture Accidentally Became Behavioral Psychology at Scale The discourse surrounding artificial intelligence has long centered on computational capability — model parameters, benchmark scores, reasoning depth. Yet the most profound transformation in human-AI interaction stems not from architectural sophistication, but from an emergent capability that was never explicitly programmed: affective pattern recognition at the micro-behavioral level. What we’re witnessing isn’t the creation of artificial empathy. It’s something far more consequential: the systematic extraction and modeling of human emotional architecture […]

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arXiv:2602.02599v1 Announce Type: new Abstract: Long-context inference in large language models is increasingly bottlenecked by the memory and compute cost of the KV-Cache. Low-rank factorization compresses KV projections by writing $W approx A * B$, where A produces latent KV states and B can be absorbed into downstream weights. In modern RoPE-based LLMs, this absorption fails: RoPE forces latent KV states to be reconstructed to full dimension, reintroducing substantial memory and compute overhead. We propose RoPE-Aligned Pruning (RAP), […]

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arXiv:2601.07945v1 Announce Type: new Abstract: We propose a deterministic and time-efficient contact-aware path planner for neurovascular navigation. The algorithm leverages information from pre- and intra-operative images of the vessels to navigate pre-bent passive tools, by intelligently predicting and exploiting interactions with the anatomy. A kinematic model is derived and employed by the sampling-based planner for tree expansion that utilizes simplified motion primitives. This approach enables fast computation of the feasible path, with negligible loss in accuracy, as demonstrated […]

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I really think “give AI total control of my computer and therefore my entire life” is going to look so foolish in retrospect that everyone who went for this is going to look as dumb as Jimmy Fallon holding up a picture of his Bored Ape — Christopher Mims, Technology columnist at The Wall Street Journal Tags: ai, security

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Introduction Disclaimer: This article is a cross post from Pytorch Medium Blog Post. One of the most exciting advancements, that has pushed the frontier of the Artificial Intelligence (AI) in recent years, is Deep Reinforcement Learning (DRL). DRL belongs to the family of machine learning algorithms. It assumes that intelligent machines can learn from their actions similar to the way humans learn from experience. Over the recent years we could witness some impressive real-world applications of DRL. The […]

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While InfoNCE powers modern contrastive learning, its geometric mechanisms remain under-characterized beyond the canonical alignment–uniformity decomposition. We present a measure-theoretic framework that models learning as the evolution of representation measures on a fixed embedding manifold. By establishing value and gradient consistency in the large-batch limit, we bridge the stochastic objective to explicit deterministic energy landscapes, uncovering a fundamental geometric bifurcation between the unimodal and multimodal regimes. In the unimodal setting, the intrinsic landscape is strictly convex with a […]

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