HDR-RoPE is proposed, which extends RoPE from independent 2D rotations to higher-dimensional rotations and introduces a Paley-I orthogonal basis to obtain balanced, isotropic, and dense phase mixing within each rotation subspace and significantly enhances channel coupling and rotational degrees of freedom while maintaining orthogonal stability and the relative position closure property.
Yixing Li, Ruobing Xie, Yu-Dong Zhang et al.· 0 citations
An underlying mechanism explaining the gap in uncertainty quantification is identified: path switching, where agents frequently abandon their current search direction in-trajectory, breaking the link between early signal and final outcome.
Zongrun Li, Cheng-Yue Yu, Lei Zang et al.· 0 citations
This work introduces Tabular Synthesis Strategy Designer, which uses an LLM to design synthesis procedures rather than directly generate records, and provides the LLM with tree-derived summaries of variable dependence rather than raw records, which produces Python programs for local execution and evaluation.
Jin-Meng Li, Quan Zhang, Hangting Ye et al.· 0 citations
A target-centric survey of QAT is provided, aimed at clarifying both its theoretical foundations and its evolving implementation landscape and synthesizing cross-target differences in error characteristics, numerical formats, and strategy transferability.
Jiashun Song, Mengjie Zhao, Zijing Wang et al.· 0 citations
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This work considers one-step generators from an optimal transport view, investigating Wasserstein Gradient Flow (WGF) for modeling smooth and controlled distributional evolution in probability space, and proposes a novel reward-guided fine-tuning of a one-step generative model via WGF.
Hoseong Hwang, Woorim Han, Joungin Chun et al.· 0 citations
LLMODE is proposed, a token-efficient framework for irregular spatio-temporal forecasting with a frozen LLM backbone that shows competitive overall performance, with clearer advantages under sparse or dynamically complex irregular sampling.
Di Zhang, Jing-Yang Zhang, Zi-Qian Wang et al.· 0 citations
This work introduces FALCON (Filtration-based hypergrAph aLignment via Cross-scale Optimal traNsport), an unsupervised optimal-transport framework for hypergraph alignment that constructs a filtration-induced sequence of clique-based co-occurrence dissimilarity matrices and jointly aligns all levels through one shared multi-scale Gromov--Wasserstein objective.
Lutz Oettershagen, Honglian Wang, A. Gionis· 0 citations
The construction establishes that learning can change effective epistemic reach even when primitive affordances and deployment resources are held fixed, and opens a complementary evaluation question for learning systems: not only what they infer from available evidence, but what informative evidence experience teaches them to bring within reach.
Adaptive Doubly Robust (ADR) is proposed, which combines adaptive importance weighting with re- ward regression through a control-variate correction and establishes its unbiasedness when the true user behavior model is observed and characterize a sufficient condition under which it reduces vari- ance relative to AIPS.
An extensive fault-injection study covering both computational and memory faults across three T2V models and a representative benchmark reveals reliability risks in deployed T2V systems and motivates further research on improving fault resilience.
Zachary Coalson, A. M. Aahad, S. Doehring et al.· 0 citations
PAC-LLM is proposed, a phase-space-aware adaptive fusion framework for long-term chaotic time series forecasting powered by LLMs that leverages learned phase-space features and textual information to fully enable LLM's time series forecasting capacity.
Control performance guarantee is first established by deriving tracking error bound for the in-network control architecture, while accounting for computational delays, and an explicit expression of the proposed event-trigger condition for exponentially stabilizable systems is derived.
Xiao-Bing Dai, Armin Lederer, Ze-Wen Yang et al.· 0 citations
A weeklong summer workshop brought higher education faculty to campus to explore how AI and machine learning materials can be adapted for their classrooms.
Adaptive AI agents can help make BIM data more machine-readable by navigating IFC models, interpreting inconsistent information, and mapping it to defined standards. In this blog, Alok Rawat shares findings from a real-world pilot in construction workflows. The post Adaptive AI Agents in Construction Workflows appeared first on GPT-Lab.
