Goal Uncertainty Attenuates Sensorimotor Adaptation

Kavli Affiliate: Reza Shadmehr | Authors: Sritej Padmanabhan, Reza S Shadmehr, Roberta Klatzky and Jonathan S Tsay | Summary: Implicit sensorimotor adaptation—the automatic correction of movement errors through feedback and practice—is driven by a perceptual prediction error, the mismatch between the perceived movement outcome and its intended goal. While perceptual uncertainty is known to attenuate […]


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Beyond the Dot: an LRD-like nucleus at the Heart of an IR-Bright Galaxy and its implications for high-redshift LRDs

Kavli Affiliate: Roberto Maiolino | First 5 Authors: Pierluigi Rinaldi, Pierluigi Rinaldi, , , | Summary: Little Red Dots (LRDs) are compact, red sources discovered by JWST at high redshift ($z gtrsim 4$), marked by distinctive "V-shaped" spectral energy distributions (SEDs) and often interpreted as rapidly accreting AGNs. Their evolution remains unclear, as identifying counterparts […]


Continue.. Beyond the Dot: an LRD-like nucleus at the Heart of an IR-Bright Galaxy and its implications for high-redshift LRDs

Towards Greater Leverage: Scaling Laws for Efficient Mixture-of-Experts Language Models

Kavli Affiliate: Jia Liu | First 5 Authors: Changxin Tian, Changxin Tian, , , | Summary: Mixture-of-Experts (MoE) has become a dominant architecture for scaling Large Language Models (LLMs) efficiently by decoupling total parameters from computational cost. However, this decoupling creates a critical challenge: predicting the model capacity of a given MoE configurations (e.g., expert […]


Continue.. Towards Greater Leverage: Scaling Laws for Efficient Mixture-of-Experts Language Models

Towards Greater Leverage: Scaling Laws for Efficient Mixture-of-Experts Language Models

Kavli Affiliate: Jia Liu | First 5 Authors: Changxin Tian, Changxin Tian, , , | Summary: Mixture-of-Experts (MoE) has become a dominant architecture for scaling Large Language Models (LLMs) efficiently by decoupling total parameters from computational cost. However, this decoupling creates a critical challenge: predicting the model capacity of a given MoE configurations (e.g., expert […]


Continue.. Towards Greater Leverage: Scaling Laws for Efficient Mixture-of-Experts Language Models

Improving Multislice Electron Ptychography with a Generative Prior

Kavli Affiliate: David A. Muller | First 5 Authors: Christian K. Belardi, Christian K. Belardi, , , | Summary: Multislice electron ptychography (MEP) is an inverse imaging technique that computationally reconstructs the highest-resolution images of atomic crystal structures from diffraction patterns. Available algorithms often solve this inverse problem iteratively but are both time consuming and […]


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WSM: Decay-Free Learning Rate Schedule via Checkpoint Merging for LLM Pre-training

Kavli Affiliate: Jia Liu | First 5 Authors: Changxin Tian, Changxin Tian, , , | Summary: Recent advances in learning rate (LR) scheduling have demonstrated the effectiveness of decay-free approaches that eliminate the traditional decay phase while maintaining competitive performance. Model merging techniques have emerged as particularly promising solutions in this domain. We present Warmup-Stable […]


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HLFormer: Enhancing Partially Relevant Video Retrieval with Hyperbolic Learning

Kavli Affiliate: Long Zhang | First 5 Authors: Li Jun, Li Jun, , , | Summary: Partially Relevant Video Retrieval (PRVR) addresses the critical challenge of matching untrimmed videos with text queries describing only partial content. Existing methods suffer from geometric distortion in Euclidean space that sometimes misrepresents the intrinsic hierarchical structure of videos and […]


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Explicit Formulas for Estimating Trace of Reduced Density Matrix Powers via Single-Circuit Measurement Probabilities

Kavli Affiliate: Jing Wang | First 5 Authors: , , , , | Summary: In the fields of quantum mechanics and quantum information science, the traces of reduced density matrix powers play a crucial role in the study of quantum systems and have numerous important applications. In this paper, we propose a universal framework to […]


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Mapple: A Domain-Specific Language for Mapping Distributed Heterogeneous Parallel Programs

Kavli Affiliate: Ke Wang | First 5 Authors: Anjiang Wei, Anjiang Wei, , , | Summary: Optimizing parallel programs for distributed heterogeneous systems remains a complex task, often requiring significant code modifications. Task-based programming systems improve modularity by separating performance decisions from core application logic, but their mapping interfaces are often too low-level. In this […]


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