arXiv:2606.00094v2 Announce Type: replace-cross Abstract: Image generative models aim to sample data points from the underlying data manifold, a task that requires learning and decoding a dense, low-dimensional, and compact parameterization space. To achieve this, we propose the Data Manifold-aware Image diffusioN moDel (MIND), a novel framework that explicitly models manifold geometry by integrating discrete […]
Muon$^2$: Boosting Muon via Adaptive Second-Moment Preconditioning
arXiv:2604.09967v2 Announce Type: replace-cross Abstract: Muon has emerged as a promising optimizer for large-scale foundation model pre-training by exploiting the matrix structure of neural network updates through iterative orthogonalization. However, the orthogonalization quality of Muon hinges on the number of Newton–Schulz (NS) iterations performed, which poses efficiency challenges due to its non-trivial computation and communication […]
Reconstructing Synthetic SDO/AIA 193 A EUV Images from He I 10830 A Observations with Diffusion Model Translator
arXiv:2606.08652v1 Announce Type: cross Abstract: Routine full-disk EUV imaging has been available only since the modern era, such as SOHO and SDO. To extend EUV coronal context into earlier periods, we leverage the multi-decade availability of full-disk HeI observations, whose absorption is modulated by coronal irradiance and magnetic topology and is widely used as a […]
LEAP: Learnable End-to-End Adaptive Pruning of Large Language Models
arXiv:2605.17289v2 Announce Type: replace-cross Abstract: Unstructured sparsity is now natively accelerated by recent GPU kernels and dataflow hardware, shifting the bottleneck from inference execution to the pruning algorithm. State-of-the-art methods for unstructured LLM pruning are layer-wise surrogates derived from the Optimal Brain Surgeon principle, and they sacrifice end-to-end accuracy, especially under aggressive sparsity. End-to-end alternatives […]
Exploring Autonomous Agentic Data Engineering for Model Specialization
arXiv:2605.30407v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated strong performance on general tasks, while often struggling to adapt to specialized domains without high-quality domain-specific data. Existing LLM-based data curation methods primarily rely on human-designed workflows, leaving it unexamined whether LLMs can autonomously execute an end-to-end data engineering pipeline for model specialization. We […]
See Less, Specify More: Visual Evidence Budgets for Generalizable VLAs
arXiv:2606.02735v2 Announce Type: replace-cross Abstract: Generalization remains a central bottleneck for vision-language-action (VLA) models: under distractors, appearance shifts, and semantically similar tasks, the policy must often infer local execution details from coarse instructions while also deciding which parts of the image matter for control. We present S2 (See Less, Specify More), a framework for improving […]
Predictable Mean-Field Chaos in Random Recurrent Networks
arXiv:2606.08805v1 Announce Type: cross Abstract: Dynamical mean-field theory recasts deterministic chaos in random recurrent networks as an effective stochastic process. We show that for analytic nonlinearities with sufficiently fast Fourier decay, this stochasticity is only apparent: the continuous past of a realized mean-field trajectory uniquely determines its future. The mean-field theory is therefore not merely […]
Knowledge Graphs and Reasoning LLMs for Finding Simple Yet Effective Transcriptomic Perturbation Predictors
arXiv:2606.08816v1 Announce Type: cross Abstract: Predicting the effect of an unseen gene knockout perturbation on transcriptomic gene expression remains a highly challenging problem for virtual cell models. Recent progress has been made by leveraging biological knowledge graphs to provide a notion of similar perturbation, allowing for improved extrapolation beyond the set of training perturbations. In […]
Report on CHIIR 2026 Workshop on Generative AI and Academic Search (GAI&AS)
arXiv:2606.08936v1 Announce Type: cross Abstract: This report summarizes the CHIIR 2026 Workshop on Generative AI and Academic Search (GAI&AS), which examined how GenAI is reshaping academic search systems and research practices. The workshop brought together researchers in human information interaction and information retrieval to explore key challenges and opportunities in designing and evaluating future academic […]
OnlyDense: Reduced-Order Modeling for Lagrangian simulation
arXiv:2606.09065v1 Announce Type: cross Abstract: In science and engineering, Lagrangian simulation methods such as Smooth Particle Hydrodynamics (SPH) or Material Point Method (MPM) are often employed to study the behavior of dynamic systems. However, these methods can be prohibitively computationally expensive, particularly when simulating multi-scale spatial or temporal phenomena, e.g., void growth and coalescence within […]
Cosmo3DFlow: Wavelet Flow Matching for Spatial-to-Spectral Compression in Reconstructing the Early Universe
arXiv:2602.10172v2 Announce Type: replace-cross Abstract: Reconstructing the early universe from the evolved present-day universe is a challenging and computationally demanding problem in modern astrophysics. We devise a novel generative framework, Cosmo3DFlow, designed to address dimensionality and sparsity, the critical bottlenecks inherent in current state-of-the-art methods for cosmological inference. By integrating 3D Discrete Wavelet Transform (DWT) […]
Crop Recommendation and Agricultural Query Answering System Using Spatio-Temporal Graph Neural Networks and Hybrid Retrieval Augmentation
arXiv:2606.09160v1 Announce Type: cross Abstract: This paper presents a unified system designed to support precision agriculture by integrating advanced weather prediction, crop recommendation, and a question-answering tool for farmers. We propose two deep learning models — a Transformer-based Graph Neural Network and a Spatio-Temporal Graph Convolutional Network (STGCN) — to forecast weather conditions for the […]