Knowledge catalogue
arXiv:2604.27329v1 Announce Type: cross Abstract: 3D shapes from scanning, reconstruction, or AI-generated content often lack simple quad mesh layouts -- critical for efficient editing and modeling. E
arXiv:2604.28185v1 Announce Type: new Abstract: Recent visual generation models have made major progress in photorealism, typography, instruction following, and interactive editing, yet they still str
arXiv:2603.16496v2 Announce Type: replace Abstract: Large language model (LLM) agents increasingly rely on external memory to support long-horizon interaction, personalized assistance, and multi-step
arXiv:2510.06002v3 Announce Type: replace Abstract: In high-stakes legal domains, retrieval must preserve not only semantic relevance, but also the hierarchy, temporality, and causal provenance of leg
arXiv:2604.26417v1 Announce Type: new Abstract: Emotion perception and adaptive expression are fundamental capabilities in human-agent interaction. While recent advances in speech emotion captioning (
arXiv:2504.09925v3 Announce Type: replace Abstract: We introduce FLARE, a family of vision language models (VLMs) with a fully vision-language alignment and integration paradigm. Unlike existing appro
arXiv:2509.21983v2 Announce Type: replace-cross Abstract: Constructing robots to accomplish long-horizon tasks is a long-standing challenge within artificial intelligence. Approaches using generative
arXiv:2511.20714v2 Announce Type: replace-cross Abstract: World models serve as core simulators for fields such as agentic AI, embodied AI, and gaming, capable of generating long, physically realistic
arXiv:2602.20426v2 Announce Type: replace Abstract: While most efforts to improve LLM-based tool-using agents focus on the agent itself - through larger models, better prompting, or fine-tuning - agen
arXiv:2604.25945v1 Announce Type: cross Abstract: Wireless radiance field (WRF) reconstruction aims to learn a continuous, queryable representation of radio frequency characteristics over 3D space and
arXiv:2603.15262v2 Announce Type: replace Abstract: Modern e-commerce search is evolving to resolve complex user intents. While Large Language Models (LLMs) offer strong reasoning, existing LLM-based
arXiv:2603.12249v2 Announce Type: replace-cross Abstract: Constructing scientific multimodal document reasoning datasets for foundation model training involves an inherent trade-off among scale, faith
arXiv:2604.26633v1 Announce Type: cross Abstract: The bottleneck in learning-based industrial defect detection is often limited not by model capacity, but by the scarcity of labeled defect data: defec
arXiv:2512.20340v3 Announce Type: replace Abstract: Although diffusion transformer (DiT)-based video virtual try-on (VVT) has made significant progress in synthesizing realistic videos, existing metho
arXiv:2510.14438v2 Announce Type: replace Abstract: The hallmark of Deep Research agents lies in compositional reasoning, the capacity to aggregate distributed, heterogeneous information into coherent
arXiv:2604.25138v1 Announce Type: cross Abstract: Spectrum cartography reconstructs spatial radio fields from sparse and heterogeneous wireless measurements, underpinning many sensing and optimization
arXiv:2604.24767v1 Announce Type: cross Abstract: Congenital heart disease (CHD) is the most common type of birth defect, impacting about 1% of live births worldwide. Echocardiography, the gold-standa
arXiv:2604.25477v1 Announce Type: new Abstract: Recent image editing models have achieved strong visual fidelity but often struggle with tasks requiring complex reasoning. To investigate and enhance t
DeepSeek-V4 Pro is now available on Together AI with 512K context, controllable reasoning modes, and cached-input pricing for long-context reasoning workloads like code agents, document intelligence,
arXiv:2604.25794v1 Announce Type: cross Abstract: Knowledge distillation (KD) represents a vital mechanism to transfer expertise from complex teacher networks to efficient student models. However, in
arXiv:2604.25319v1 Announce Type: new Abstract: The exponential surge in high-resolution remote sensing data faces a severe bottleneck in satellite-to-ground transmission. Limited downlink bandwidth f
arXiv:2604.25289v1 Announce Type: cross Abstract: Practically, training diffusion models typically requires explicit time conditioning to guide the network through the denoising sampling process. Espe
arXiv:2601.02078v2 Announce Type: replace Abstract: The development of robust and generalizable robot learning models is critically contingent upon the availability of large-scale, diverse training da
arXiv:2604.25296v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have shown transformative potential in medical applications, yet their performance is hindered by conventional
arXiv:2604.24907v1 Announce Type: cross Abstract: We introduce a new family of temporal logics intended for specifications in motion planning (MP). It builds upon the signal temporal logic (STL), whic
arXiv:2411.14721v2 Announce Type: replace Abstract: Molecule discovery is a pivotal research field, impacting everything from medicine to materials. Recently, Large Language Models (LLMs) have been wi
arXiv:2604.25819v1 Announce Type: new Abstract: In this work, we propose Mutual Forcing, a framework for fast autoregressive audio-video generation with long-horizon audio-video synchronization. Our a
arXiv:2602.17697v2 Announce Type: replace Abstract: Large Language Models (LLMs) are being increasingly used across a wide range of tasks. However, their substantial computational demands raise concer
arXiv:2212.14245v2 Announce Type: replace Abstract: In computer vision, correcting the exposure level is a fundamental task for enhancing the visual quality of observations with inappropriate lightnes
arXiv:2604.25654v1 Announce Type: new Abstract: Although the cultural (mis)alignment of Large Language Models (LLMs) has attracted increasing attention -- often framed in terms of cultural bias -- unt
arXiv:2604.24043v1 Announce Type: new Abstract: Designing heuristics for combinatorial optimization problems (COPs) is a fundamental yet challenging task that traditionally requires extensive domain e
arXiv:2604.24473v1 Announce Type: new Abstract: Multiple myeloma is managed through sequential lines of therapy over years to decades, with each decision depending on cumulative disease history distri
arXiv:2604.23758v1 Announce Type: new Abstract: The discovery of novel materials is critical for global energy and quantum technology transitions. While deep learning has fundamentally reshaped this l
arXiv:2604.22777v1 Announce Type: new Abstract: Fault diagnosis of general aviation aircraft faces challenges including scarce real fault data, diverse fault types, and weak fault signatures. This pap
arXiv:2604.23124v1 Announce Type: cross Abstract: As software systems grow in complexity, they must satisfy an increasing number of competing quality attributes, making it essential to balance them in
arXiv:2604.22821v1 Announce Type: cross Abstract: Voice assistants increasingly rely on Speech Language Models (SpeechLMs) to interpret spoken queries and execute complex tasks, yet existing benchmark
arXiv:2604.24512v1 Announce Type: new Abstract: As LLM agents transition to autonomous digital coworkers, maintaining deterministic goal-directedness in non-linear multi-turn conversations emerged as
arXiv:2604.23121v1 Announce Type: cross Abstract: Have you ever post-trained a generalist vision-language-action (VLA) policy on a small demonstration dataset, only to find that it stops responding to
arXiv:2604.24024v1 Announce Type: new Abstract: Conventional multi-projector calibration requires projecting and capturing structured light patterns for each projector sequentially, causing calibratio
arXiv:2604.23282v1 Announce Type: new Abstract: Text-based person anomaly search retrieves specific behavioral events from surveillance archives using natural-language queries. Although recent pose-aw
arXiv:2604.23803v1 Announce Type: new Abstract: Egocentric video provides a unique view into human perception and interaction, with growing relevance for augmented reality, robotics, and assistive tec
arXiv:2604.23508v1 Announce Type: new Abstract: Burst image super resolution (BISR) aims to construct a single high-resolution (HR) image by aggregating information from multiple low-resolution (LR) f
arXiv:2508.20324v4 Announce Type: replace Abstract: Reinforcement Learning has emerged as a dominant post-training approach to elicit agentic RAG behaviors such as search and planning from language mo
arXiv:2510.13312v2 Announce Type: replace Abstract: We present ChatR1, a reasoning framework based on reinforcement learning (RL) for conversational question answering (CQA). Reasoning plays an import
arXiv:2604.23307v1 Announce Type: cross Abstract: Dual-target molecule generation, which focuses on discovering compounds capable of interacting with two target proteins, has garnered significant atte
arXiv:2604.22820v1 Announce Type: cross Abstract: Long-horizon tool-using tasks sometimes benefit from revisiting earlier subtasks for recovery and exploration, but added multi-agent workflow flexibil
arXiv:2601.16046v2 Announce Type: replace-cross Abstract: Language-driven dexterous grasp generation requires the models to understand task semantics, 3D geometry, and complex hand-object interactions
arXiv:2604.14989v2 Announce Type: replace Abstract: Recent advances in large language models (LLMs) have sparked growing interest in automatic RTL optimization for better performance, power, and area
arXiv:2503.18836v2 Announce Type: replace-cross Abstract: Magnetic resonance imaging (MRI) is a vital diagnostic tool, but its inherently long acquisition times reduce clinical efficiency and patient
arXiv:2603.04751v2 Announce Type: replace Abstract: Integrating web search tools has significantly extended the capability of LLMs to address open-world, real-time, and long-tail problems. However, ev
arXiv:2604.23989v1 Announce Type: cross Abstract: Recent work on large language models (LLMs) has emphasized the importance of scaling inference compute. From this perspective, the state-of-the-art me
arXiv:2510.19889v2 Announce Type: replace-cross Abstract: The traffic assignment problem is essential for traffic flow analysis, traditionally solved using mathematical programs under the Equilibrium
arXiv:2512.00127v3 Announce Type: replace-cross Abstract: Getting language models to reason correctly about code requires training on data where each reasoning step can be checked. Current synthetic C
arXiv:2603.17834v2 Announce Type: replace-cross Abstract: Diffusion models and flow matching have become a cornerstone of robotic imitation learning, yet they suffer from a structural inefficiency whe
arXiv:2604.23904v1 Announce Type: cross Abstract: Synthetic data offers a promising tool for privacy-preserving data release, augmentation, and simulation, but its use in causal inference requires pre
arXiv:2604.24238v1 Announce Type: new Abstract: Diffusion models are a leading paradigm for data generation, but training-free editing typically re-runs the full denoising trajectory for every edit st
arXiv:2601.19906v2 Announce Type: replace-cross Abstract: Targeting error-tolerant applications, approximate computing relaxes rigid functional equivalence to significantly improve power, performance,
arXiv:2511.07940v2 Announce Type: replace Abstract: Talking Face Generation (TFG) methods based on Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have recently achieved impressive prog
arXiv:2604.23858v1 Announce Type: new Abstract: Video generation, while capable of generating realistic videos, is computationally expensive and slow, prohibiting real-time applications. In this paper
arXiv:2604.23324v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) have demonstrated impressive performance in learning representations from graph-structured data. However, their message-p