Method is introduced, which augments SOAP-style preconditioning with a scalar secant-energy correction adapted to Kronecker geometry and an adaptive basis update followed by variance-state downscaling, and is positioned as a scalable option for stiff, high-accuracy physics-informed training, rather than a uniform replacement for existing optimizers.
Guang-Yuan Wang, Mads Toftrup, Sebastian Loeschcke et al.· 0 citations
Geometry finally makes a commanded 3D target a natural goal interface: it is constructed the goal latent from the target and the current latent, at no cost in success rate, without a goal observation.
F. F. Oberweger, Michael Schwingshackl· 0 citations
It is argued that LASSO, not the highest-discriminating model, is the model best suited to direct clinical deployment, and lessons for the machine learning and healthcare community regarding data infrastructure, model selection, and value of calibration and interpretability in high-stakes decision support are presented.
Asra Aslam, Volodymyr Chapman, M. O'Connell et al.· 0 citations
This work proposes a fully distributed continuous-time algorithm for shared linear equality constraints that converges without multiplier exchange and reaches any GNE, reducing communication overhead and improving privacy.
Sho-An Yin, Mingyi Hong, Nicola Elia· American Control Conference· 1 citation
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A larger-batch configuration reduces time-to-target only when its throughput gain exceeds its samples-to-target penalty, and a larger-batch configuration reduces time-to-target only when its throughput gain exceeds its samples-to-target penalty.
Ziniu Li, Jinbo Wang, Guan-Hua Huang et al.· 0 citations
This work proposes the Multi-Branch Neural Decision Tree with Adaptive Pruning (MBNDT), a single axis-aligned tree trained end-to-end with differentiable multi-way splits that achieves the best average rank and mean balanced accuracy among depth-constrained single-tree baselines.
H. Park, Jeonghoon Choi, Juseong Kim et al.· 0 citations
This work set out to build a strong VGC agent and report what that took, and found that on the live Showdown best-of-three ladder, the agent wins 59% of 150 sets against a human field averaging ${\sim}1320$ Elo.
While surface prompting fails to recover diversity, entrance-targeted interventions succeed: late-layer parameter interpolation with early checkpoints increases solution coverage by 37% at no loss in pass@1 and late-layer parameter interpolation with early checkpoints increases solution coverage by 37% at no loss in pass@1.
This research presents an autonomous AI Coding Agent which establishes a connection between LLM-generated content and production-ready software through its organized methodology for decision making through its tailored Monte Carlo Tree Search method.
Pravin Game, V. Ramakrishnan, Prathamesh Wagh· 0 citations
This work proposes Flow-JEPA (F-JEPA), a conditional flow matching dynamics model that jointly generates a sequence of future latent states conditioned on the current observation and actions, suggesting that conditional flow matching provides a promising alternative to deterministic autoregressive dynamics in JEPA world models.
A curious phenomenon called mode connectivity, the ability to connect neural networks in the loss surface, defies explanation entirely is elucidates, explains and exploits this special structure in the loss landscape.
The mechanism is a halt vector: a difference-of-means direction at layer 18 of this model whose steering strength controls how long it thinks, while a replicated value axis does nothing, and what works is reconstructing the whole steered activation with those dimensions pinned to their natural values.
