Knowledge catalogue
arXiv:2608.10867v1 Announce Type: new Abstract: Gradient-free post-training has emerged as a compelling alternative to gradient-based optimization for large language models (LLMs), but existing approa
arXiv:2608.10500v1 Announce Type: new Abstract: Creating photorealistic and temporally coherent animatable human avatars from RGB videos remains challenging. Current methods struggle to capture realis
arXiv:2608.10435v1 Announce Type: new Abstract: Diffusion models have been widely explored in protein backbone generation due to their powerful generation capabilities.However, in today's AI-driven bi
arXiv:2509.10600v5 Announce Type: replace-cross Abstract: Collegiate cross country teams often build their season schedules on intuition rather than evidence, partly because large-scale performance da
arXiv:2608.10857v1 Announce Type: new Abstract: Determining the complexity, or Intrinsic Dimension (ID), of data is fundamental to efficient and interpretable representation learning. This is particul
arXiv:2608.10045v1 Announce Type: cross Abstract: The problem of learning from pairwise comparisons has been widely studied across many domains such as recommendation systems, social choice, and more
arXiv:2608.10602v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has recently enabled real-time novel view synthesis with impressive quality. However, it struggles to recover accurate surf
arXiv:2608.10181v1 Announce Type: new Abstract: Computer vision saliency models predict where people will look, one map per image, and a billion-dollar predicted-attention industry sells those maps in
arXiv:2608.10843v1 Announce Type: new Abstract: First-order concept synthesis asks a system to infer one formula that classifies labeled objects consistently across several finite relational structure
arXiv:2602.15159v2 Announce Type: replace Abstract: Electronic health records (EHR) arrive masked. Clinicians order measurements selectively, and any patient table thus contains only a subset of the v
arXiv:2608.10517v1 Announce Type: cross Abstract: Accurate physical-layer modeling is increasingly essential for reliable ultra-wideband operation and capacity optimization, especially under the inten
arXiv:2608.11195v1 Announce Type: new Abstract: AI agents are increasingly used in mathematics research, but it is often unclear how to use them effectively. Towards this, we present an extensive case
arXiv:2608.10291v1 Announce Type: cross Abstract: Large-scale multi-modal MRI datasets impose substantial storage and I/O costs, limiting the training of 3D generative models on commodity infrastructu
arXiv:2601.18128v2 Announce Type: replace-cross Abstract: High-dimensional data often exhibit variation that can be captured by lower-dimensional factors. For high-dimensional data from multiple studi
arXiv:2608.10651v1 Announce Type: new Abstract: Voice-guided teleoperation requires systems that adapt to the evolving dynamics of human guidance. Yet most voice-controlled robot systems treat spoken
Remember that much-hyped story about an Australian tech entrepreneur using ChatGPT, Grok, and other AI tools to craft a personalized cancer vaccine for his dog? Well, surprise: he's launched a startup
arXiv:2608.10959v1 Announce Type: new Abstract: Existing vision-language model (VLM) backdoors are usually treated as static vulnerabilities: one-to-one and N-to-N attacks bind one or more triggers to
arXiv:2608.10448v1 Announce Type: new Abstract: Multimodal emotion recognition in conversation (MERC) requires understanding complex interactions between verbal and non-verbal cues. However, most exis
arXiv:2608.10004v1 Announce Type: new Abstract: Concept Bottleneck Models (CBMs) provide an interpretable framework by grounding predictions in human-understandable concepts, enabling semantic inspect
arXiv:2601.07218v2 Announce Type: replace Abstract: We present SceneNAT, a masked non-autoregressive Transformer for 3D indoor scene synthesis from natural language instructions. It generates complete
arXiv:2608.10928v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) improve performance by allocating additional inference-time compute to generate extended chain-of-thought reasoning. Howev
arXiv:2608.09997v1 Announce Type: cross Abstract: Transformers have had a profound impact on the world of language processing and computer vision. As efforts to answer the million-dollar question of `
arXiv:2602.10680v2 Announce Type: replace-cross Abstract: Many real-world datasets contain hidden structure that cannot be detected by simple linear correlations between input features. For example, l
arXiv:2608.08091v1 Announce Type: cross Abstract: Dynamical systems model trajectory data generated by fixed underlying dynamics, with applications ranging from biology to physics. Especially in scien
arXiv:2608.07747v1 Announce Type: new Abstract: We study how to share a single conserved capacity budget across many locations and two service classes when demand is uneven, time-varying, and can exce
arXiv:2608.08401v1 Announce Type: new Abstract: T2-weighted (T2w) brain MRI provides fluid-sensitive soft-tissue contrast that is important for neuro-oncology and radiotherapy planning. However, T2w s
arXiv:2408.07490v4 Announce Type: replace Abstract: In unsupervised image anomaly detection, reconstruction-based methods learn normal patterns for data reconstruction, but often undesirably reconstru
arXiv:2602.00549v2 Announce Type: replace Abstract: While Monte Carlo Tree Search (MCTS) shows promise in Large Language Model (LLM) based Automatic Heuristic Design (AHD), it suffers from a critical
arXiv:2608.08135v1 Announce Type: cross Abstract: Cross-modality medical image translation can reduce the burden of multi-modal acquisitions, yet the field remains constrained by two coupled limitatio
arXiv:2608.08661v1 Announce Type: new Abstract: Degradation information in underwater images plays a dual role: its spatial and spectral cues can guide adaptive restoration, while degradation-entangle
arXiv:2608.09266v1 Announce Type: new Abstract: Medical world models commonly learn from multimodal recordings synchronized onto a fixed-rate grid. This preprocessing resamples each native stream onto
arXiv:2608.08675v1 Announce Type: new Abstract: Long-term time series forecasting benefits from preserving global structure such as trends and seasonality. Recent LLM-based forecasters often improve a
arXiv:2608.09656v1 Announce Type: new Abstract: Visual query localization (VQL) aims to retrieve and re-localize a queried object in egocentric videos, yet remains challenging when object boundaries a
arXiv:2508.16085v2 Announce Type: replace Abstract: Histopathology is essential for cancer diagnosis and treatment selection, and pathology foundation models learn visual representations from whole-sl
arXiv:2511.05033v2 Announce Type: replace Abstract: Epically Powerful is an open-source robotics infrastructure that streamlines the underlying framework of wearable robotic systems - managing communi
arXiv:2608.07490v1 Announce Type: cross Abstract: Large language model agents are increasingly evaluated through games, but most benchmarks emphasize final outcomes rather than how players learn from
Learn how First Orion, a branded communications company, shifted from brittle script-based UI testing to AI-driven QA automation with Amazon Nova Act. By describing tests in plain English instead of m
arXiv:2608.08436v1 Announce Type: new Abstract: Precipitation nowcasting predicts the spatiotemporal evolution of future radar echoes from historical radar echo sequences, thereby estimating the occur
arXiv:2608.09396v1 Announce Type: new Abstract: Invariant learning seeks representations that remain predictive across environments, yet the behavior of its objectives along the regularization path is
arXiv:2608.05235v1 Announce Type: cross Abstract: Research agents increasingly conduct multi-round machine-learning experiments in industrial recommendation settings and retain the resulting trajector
arXiv:2608.08101v1 Announce Type: new Abstract: Generative AI has emerged as one of the most transformative forces in modern artificial intelligence, reshaping how we create, imagine, and interact wit
arXiv:2608.07735v1 Announce Type: new Abstract: Hypercomplex numbers extend the concept of complex numbers by introducing additional imaginary components. Besides increasing dimensionality, operations
Photographers are among the most skeptical audiences for generative AI. Learn how Pixieset used Amazon Bedrock to launch an AI-generated alt text feature to millions of users in four months, reaching
arXiv:2608.08958v1 Announce Type: cross Abstract: Tree Search-based test-time scaling of LLMs is a powerful tool for automated scientific coding. However, pure Tree Search sometimes struggles with sys
arXiv:2608.09177v1 Announce Type: cross Abstract: In recent years, systems that utilize immersive space have been developed in various fields. Immersive spaces often contain considerable amounts of vi
I’ve been thinking about how agents can learn inside world models for years. We decided to scale up our RSI Lab to bridge recursive self-improvement with physical AI and robotics. We are looking for f
arXiv:2608.07941v1 Announce Type: new Abstract: Camouflaged object detection (COD) aims to segment objects that exhibit high visual similarity to their surroundings, which reduces foreground-backgroun
arXiv:2608.09596v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) suffer from two fundamental limitations: over-smoothing, where node representations become indistinguishable with depth,
arXiv:2604.05695v2 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable progress in 2D visual tasks but still struggle to understand physical space in rea
arXiv:2608.07648v1 Announce Type: cross Abstract: Sampling high-dimensional probability distributions is a central task in scientific computing, with applications ranging from Bayesian inference to st
arXiv:2608.09328v1 Announce Type: new Abstract: Model learning by an eavesdropper is treated as an estimation problem in a federated environment. The Fisher Information Matrix for the eavesdropper's e
arXiv:2512.10903v3 Announce Type: replace Abstract: Circuit discovery aims to identify minimal subnetworks that are responsible for specific behaviors in large language models (LLMs). Existing approac
arXiv:2608.08646v1 Announce Type: cross Abstract: Trajectory-User Linking (TUL) aims to identify the owner of an anonymous trajectory from a set of candidate users, providing a basis for user mobility
arXiv:2608.08574v1 Announce Type: new Abstract: Crowdsourced Federated Learning (CrowdFL) extends traditional federated learning by enabling open and heterogeneous participation through a crowdsourcin
arXiv:2608.09260v1 Announce Type: cross Abstract: Large language models (LLMs) have advanced Text-to-SQL by enabling natural language interfaces to databases without task-specific fine-tuning. However
arXiv:2608.09201v1 Announce Type: new Abstract: Dense expert merging combines domain-specialized language models into one single checkpoint, typically by admitting task-vector support in weight space.
arXiv:2608.09006v1 Announce Type: cross Abstract: Large Language Models (LLMs) have achieved remarkable success across a wide range of tasks. However, fine-tuning LLMs for Gloss-Free Sign Language Tra
arXiv:2602.22895v2 Announce Type: replace-cross Abstract: Implementations of symmetric positive definite (SPD) matrix-based neural networks for neural decoding remain fragmented across research codeba
arXiv:2608.07968v1 Announce Type: cross Abstract: Reasoning language models increasingly use test-time compute to improve performance, but existing evaluations typically study this compute one questio
arXiv:2608.09091v1 Announce Type: cross Abstract: Transfer learning is particularly useful in settings with limited training data, and within image classification it is common to transfer learn upon m