Knowledge catalogue
arXiv:2506.21076v4 Announce Type: replace Abstract: Pose stylization, which aims to synthesize stylized content aligning with target poses, serves as a fundamental task across 2D, 3D, and video domain
arXiv:2607.27304v1 Announce Type: cross Abstract: Medical vision-language models (VLMs) generate chain-of-thought (CoT) reasoning before answering clinical questions, but whether this reasoning causal
arXiv:2607.26352v1 Announce Type: cross Abstract: Networking research advances by turning hypotheses into empirical evidence, so accelerating it means reducing the lag between ideation (synthesizing a
arXiv:2607.23377v1 Announce Type: cross Abstract: The largest machine learning models in particle physics are also the most expensive to train, yet the return on scaling a given architecture cannot be
arXiv:2607.27729v1 Announce Type: new Abstract: Point clouds are one of the most fundamental and widely used 3D representations, serving as the most basic geometric representation of 3D shapes. Nevert
arXiv:2607.27764v1 Announce Type: new Abstract: Publishing private face recognition~(FR) training datasets is privacy-sensitive because faces expose identity information. Private FR training dataset p
arXiv:2601.10600v2 Announce Type: replace-cross Abstract: In the context of multi-agent multi-armed bandits (MA-MAB), fairness is often reduced to outcomes: maximizing welfare, reducing inequality, or
arXiv:2607.27537v1 Announce Type: new Abstract: Predicting future structural MRI of a brain is challenging because longitudinal changes are often subtle and confined to specific anatomical regions, wh
arXiv:2607.27210v1 Announce Type: new Abstract: The exponential growth of scholarly publications requires automated tools for effective information synthesis. However, simple, single-shot prompting me
After trying several AI headshot apps, I decided that they all suck. I've always liked Marco Grob's TIME cover portraits so I've been refining some prompts to generate these types of headshots. Both b
arXiv:2607.26787v1 Announce Type: new Abstract: Markov Decision Processes (MDPs) are widely used as decision-making models, commonly specified over factored state spaces through state variables and th
arXiv:2607.27591v1 Announce Type: cross Abstract: Feed-forward networks (FFNs) dominate memory traffic and computation in large language model (LLM) inference, making them a primary target for activat
arXiv:2607.27224v1 Announce Type: cross Abstract: External and synthetic control arms (ECAs) are entering psychiatric drug development, but the field lacks a benchmark that evaluates the properties re
arXiv:2607.26464v1 Announce Type: cross Abstract: Physical Unified Device Architecture (PUDA) is an AI-native hardware harness for self-driving laboratories (SDLs). Rather than building a human-center
arXiv:2406.12413v3 Announce Type: replace-cross Abstract: We study the problem of allocating a set of indivisible goods to a set of agents with additive valuation functions, aiming to achieve approxim
arXiv:2607.28422v1 Announce Type: new Abstract: Fault-tolerant quantum computing (FTQC) relies on quantum error correction to suppress physical errors and preserve logical information at scale. In pra
arXiv:2607.28415v1 Announce Type: cross Abstract: Latent world models enable efficient planning by predicting future states in a compact representation space, but their performance depends critically
arXiv:2605.05905v2 Announce Type: replace Abstract: Objective perturbation is a standard mechanism in differentially private empirical risk minimization. In particular, Linear Objective Perturbation (
arXiv:2607.28405v1 Announce Type: cross Abstract: World Action Models (WAMs) jointly predict future observations and actions, but their iterative denoising and closed-loop execution make efficient dep
arXiv:2607.28227v1 Announce Type: cross Abstract: GUI agents have the potential to become a general purpose executor over existing digital devices. To advance them toward real-world use, we envision a
arXiv:2607.27235v1 Announce Type: cross Abstract: Training deep learning models on radiological images requires integrating heterogeneous datasets across different sources, file formats, directory lay
arXiv:2607.28045v1 Announce Type: new Abstract: Recent advances in 4D radar enable robust perception in adverse weather; however, the inherent sparsity, noise, and limited positional precision of rada
arXiv:2509.25228v3 Announce Type: replace Abstract: Accurate density estimation is crucial for understanding complex high-dimensional data, but it becomes challenging when the data lies on or near low
arXiv:2607.27626v1 Announce Type: new Abstract: Safety-critical IoT systems such as industrial closed-loop control, V2X coordination, and remote teleoperation require every sensor's peak Age of Inform
arXiv:2607.27783v1 Announce Type: new Abstract: Large Language Models (LLMs) explore problems through chain-of-thought, but this exploration is buried in unstructured prose. On high-stakes tasks, user
arXiv:2607.27692v1 Announce Type: new Abstract: Top-K sparse attention reduces the cost of Softmax and value aggregation by attending to only a small subset of key--value (KV) entries. However, identi
arXiv:2607.27568v1 Announce Type: new Abstract: Recognition accuracy obtained during a recording session does not persist when a user puts on the electrodes again after the electrodes had previously b
arXiv:2607.27251v1 Announce Type: new Abstract: Transformer-based surrogate models are increasingly used to replace expensive first-principles simulation in engineering design. But conventional transf
arXiv:2607.27782v1 Announce Type: new Abstract: Flow-matching Vision-Language-Action (VLA) policies have shown strong potential for robotic manipulation but often suffer from compounding errors caused
arXiv:2603.11325v2 Announce Type: replace Abstract: Low-field to high-field MRI synthesis has emerged as a promising strategy to improve image quality when access to high-field scanners is limited. Ho
arXiv:2607.27631v1 Announce Type: cross Abstract: Reinforcement learning has emerged as an effective paradigm for enhancing the mathematical reasoning capabilities of large language models. Among exis
arXiv:2607.28509v1 Announce Type: new Abstract: Existing video captioning models generate natural descriptions of video content but cannot explicitly ground local visual elements to multiple reference
arXiv:2603.13707v3 Announce Type: replace-cross Abstract: Humanoid loco-manipulation requires coordinated task-space motion planning with stable loco-manipulation command tracking under complex robot-
arXiv:2607.27699v1 Announce Type: new Abstract: We propose RefineSVG, a single-step closed-loop visual feedback framework that enables multimodal large language models (MLLMs) to perform high-fidelity
arXiv:2607.28344v1 Announce Type: cross Abstract: Motivated by marginal distribution flows of reflected diffusions in bounded domains, we investigate when a density/flux pair solving a no-flux continu
arXiv:2607.28144v1 Announce Type: cross Abstract: We present ReGenVC, an end-to-end generative video codec that compresses talking-head video to an ultra-low bitrate and decodes it in real time. The e
arXiv:2605.05623v2 Announce Type: replace Abstract: Hyperspectral in situ sensing has shown promise in retrieving aquatic biogeochemical (BGC) parameters, such as total suspended solids, dissolved org
arXiv:2607.27260v1 Announce Type: new Abstract: Multimodal continual learning (MMCL) aims to learn emerging knowledge from multimodal data while preserving knowledge. To mitigate forgetting, current M
arXiv:2602.04635v2 Announce Type: replace Abstract: Robots are finding wider adoption in human environments, increasing the need for natural human-robot interaction. However, understanding a natural l
arXiv:2607.28008v1 Announce Type: new Abstract: Representation engineering reads and steers capability directions in large language models, yet methods are typically evaluated on paper-specific synthe
arXiv:2112.07752v4 Announce Type: replace-cross Abstract: Researchers have formalized reinforcement learning (RL) in different ways. If an agent in one RL framework is to run within another RL framewo
arXiv:2607.27274v1 Announce Type: new Abstract: EEG-based disease diagnosis requires one prediction per subject, yet common pipelines segment recordings into short instances, inherit the subject label
arXiv:2607.28128v1 Announce Type: new Abstract: LLM tutoring poses a measurement problem: can a general-purpose helpfulness rubric distinguish direct answer-giving from pedagogical guidance? We audit
arXiv:2607.28627v1 Announce Type: new Abstract: Long visual context poses a challenge for vision-language models: performance degrades as the number of distractors grows, and processing all tokens at
arXiv:2607.27209v1 Announce Type: cross Abstract: Peer review at ML conferences increasingly relies on reviewer scores as the primary decision instrument. As submissions have scaled from thousands to
arXiv:2607.27797v1 Announce Type: new Abstract: Predictive process monitoring (PPM) leverages event logs to forecast the future of running process instances, for instance, predicting the next activity
arXiv:2603.09411v2 Announce Type: replace Abstract: We present RiO-DETR: DETR for Real-time Oriented Object Detection, the first real-time oriented detection transformer to the best of our knowledge.
arXiv:2607.27775v1 Announce Type: new Abstract: Generative models synthesize magnitude spectra with high fidelity, while phase is delegated to a recovery module---Griffin--Lim, a vocoder, or a latent
arXiv:2607.27271v1 Announce Type: new Abstract: Code models are increasingly trained with execution feedback, but most training signals still stop at correctness. This leaves an important gap for syst
arXiv:2603.01229v3 Announce Type: replace Abstract: Robotic manipulation policies have made rapid progress in recent years, yet most existing approaches give limited consideration to memory capabiliti
arXiv:2607.28581v1 Announce Type: new Abstract: High-fidelity 3D generation predominantly relies on scaling model capacity and data, which incurs prohibitive computational costs. This paradigm typical
arXiv:2607.27881v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have attracted growing interest as a scalable approach to robotic manipulation. While these models are effective act
Physical AI is forcing the technology industry to rethink the entire computing stack. Robots, autonomous systems and intelligent devices need economical inference, secure data access and infrastructur
arXiv:2607.27815v1 Announce Type: cross Abstract: Local differential privacy (LDP) protocols are vulnerable to poisoning attacks. Existing research have proposed efficient defense strategies for singl
arXiv:2508.15860v4 Announce Type: replace-cross Abstract: Finite Scalar Quantization (FSQ) offers simplified training but suffers from residual magnitude decay in multi-stage settings, where subsequen
arXiv:2607.27645v1 Announce Type: cross Abstract: Wavelength selection is one of the important preprocessing methods in near-infrared spectroscopy to improve prediction accuracy and interpretability o
arXiv:2607.27744v1 Announce Type: new Abstract: Modern recommendation models gain prediction quality by scaling feature-interaction and sequence modules, but production cost constraints cap how far sy
arXiv:2607.28156v1 Announce Type: new Abstract: Existing multimodal long-term memory agents use external memory to overcome the limited context available for long videos. However, most methods emphasi
arXiv:2607.28164v1 Announce Type: new Abstract: We propose S-Avatar, a novel method for generating photorealistic 3D head avatars from a single image using a diffusion-guided 3D model generation modul
arXiv:2607.27913v1 Announce Type: new Abstract: Foundation models offer a promising paradigm for Electroencephalography (EEG) analysis, leveraging generalizable representations from vast unlabeled dat