arXiv:2601.04342v1 Announce Type: new Abstract: Recent advances in video diffusion models have shifted towards transformer-based architectures, achieving state-of-the-art video generation but at the cost of quadratic attention complexity, which severely limits scalability for longer sequences. We introduce ReHyAt, a Recurrent Hybrid Attention mechanism that combines the fidelity of softmax attention with the efficiency of linear attention, enabling chunk-wise recurrent reformulation and constant memory usage. Unlike the concurrent linear-only SANA Video, ReHyAt’s hybrid design allows efficient distillation from existing […]

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In the dynamic world of software engineering, where technical mastery meets strategic business impact, exceptional professionals distinguish themselves through transformative solutions while nurturing engineering talent. Rishabh Agarwal’s eight-year journey across retail, financial services, consulting, and entertainment sectors exemplifies how curiosity-driven learning, combined with relentless performance optimization, creates compounding organizational value. The Foundation: From Hardware Fascination to Software Innovation Rishabh’s engineering journey began with an unexpected pivot. Initially drawn to computer engineering and fascinated by physical system architecture, he […]

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arXiv:2602.12380v1 Announce Type: new Abstract: Accurate forecasting of Bitcoin (BTC) has always been a challenge because decentralized markets are non-linear, highly volatile, and have temporal irregularities. Existing deep learning models often struggle with interpretability and generalization across diverse market conditions. This research presents a hybrid stacked-generalization framework, TFT-ACB-XML, for BTC closing price prediction. The framework integrates two parallel base learners: a customized Temporal Fusion Transformer (TFT) and an Attention-Customized Bidirectional Long Short-Term Memory network (ACB), followed by an […]

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Automotive radar perception pipelines commonly construct angle-domain representations via beamforming before applying learning-based models. This work instead investigates a representational question: can meaningful spatial structure be learned directly from pre-beamforming per-antenna range-Doppler (RD) measurements? Experiments are conducted on a 6-TX x 8-RX (48 virtual antennas) commodity automotive radar employing an A/B chirp-sequence frequency-modulated continuous-wave (CS-FMCW) transmit scheme, in which the effective transmit aperture varies between chirps (single-TX vs. multi-TX), enabling controlled analysis of chirp-dependent transmit configurations. We operate […]

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arXiv:2602.06164v1 Announce Type: new Abstract: People differ in how much they move their head versus their eyes when shifting gaze, yet such tendencies remain largely unexplored in HCI. We introduce head movement tendencies as a fundamental dimension of individual difference in VR and provide a quantitative account of their population-level distribution. Using a 360{deg} video free-viewing dataset (N=87), we model head contributions to gaze shifts with a hinge-based parametric function, revealing a spectrum of strategies from eye-movers to […]

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People on the orange site are laughing at this, assuming it’s just an ad and that there’s nothing to it. Vulnerability researchers I talk to do not think this is a joke. As an erstwhile vuln researcher myself: do not bet against LLMs on this. Axios: Anthropic’s Claude Opus 4.6 uncovers 500 zero-day flaws in open-source I think vulnerability research might be THE MOST LLM-amenable software engineering problem. Pattern-driven. Huge corpus of operational public patterns. Closed loops. Forward […]

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arXiv:2602.04029v1 Announce Type: new Abstract: Relational Foundation Models (RFMs) facilitate data-driven decision-making by learning from complex multi-table databases. However, the diverse relational databases needed to train such models are rarely public due to privacy constraints. While there are methods to generate synthetic tabular data of arbitrary size, incorporating schema structure and primary–foreign key connectivity for multi-table generation remains challenging. Here we introduce PluRel, a framework to synthesize multi-tabular relational databases from scratch. In a step-by-step fashion, PluRel models […]

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MLP is a heavily used backbone in modern deep learning (DL) architectures for supervised learning on tabular data, and AdamW is the go-to optimizer used to train tabular DL models. Unlike architecture design, however, the choice of optimizer for tabular DL has not been examined systematically, despite new optimizers showing promise in other domains. To fill this gap, we benchmark 15 optimizers on 17 tabular datasets for training MLP-based models in the standard supervised learning setting under a […]

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