Knowledge catalogue
arXiv:2604.08159v1 Announce Type: new Abstract: The rapid advancement of facial forgery techniques poses severe threats to public trust and information security, making facial DeepFake detection a cri
arXiv:2603.16570v2 Announce Type: replace Abstract: Recent advances in image restoration have enabled high-fidelity recovery of faces from degraded inputs using reference-based face restoration models
arXiv:2604.07350v1 Announce Type: cross Abstract: Large Chunk Test-Time Training (LaCT) has shown strong performance on long-context 3D reconstruction, but its fully plastic inference-time updates rem
arXiv:2604.06795v1 Announce Type: cross Abstract: Federated Learning (FL) enables decentralized model training across multiple clients without exposing private data, making it ideal for privacy-sensit
arXiv:2604.06833v1 Announce Type: cross Abstract: As high quality public data becomes scarce, Federated Learning (FL) provides a vital pathway to leverage valuable private user data while preserving p
arXiv:2604.02360v1 Announce Type: cross Abstract: The transformative potential of large language models (LLMs) in education, such as improving accessibility and personalized learning, is being eclipse
arXiv:2604.08526v1 Announce Type: new Abstract: Given a person and a garment image, virtual try-on (VTO) aims to synthesize a realistic image of the person wearing the garment, while preserving their
arXiv:2604.06253v1 Announce Type: cross Abstract: Cross-lingual code generation is critical in enterprise environments where multiple programming languages coexist. However, fine-tuning large language
arXiv:2604.07879v1 Announce Type: new Abstract: Diffusion-based image generation models have advanced rapidly but pose a safety risk due to their potential to generate Not-Safe-For-Work (NSFW) content
arXiv:2604.06985v1 Announce Type: cross Abstract: Frailty and functional decline strongly influence treatment tolerance and outcomes in older patients with cancer, yet assessment is typically limited
arXiv:2604.07429v1 Announce Type: new Abstract: Towards an embodied generalist for real-world interaction, Multimodal Large Language Model (MLLM) agents still suffer from challenging latency, sparse f
arXiv:2604.07728v1 Announce Type: new Abstract: High-fidelity interactive digital assets are essential for embodied intelligence and robotic interaction, yet articulated objects remain challenging to
arXiv:2604.08230v1 Announce Type: new Abstract: Object detection models trained on a source domain often exhibit significant performance degradation when deployed in unseen target domains, due to vari
arXiv:2412.19685v2 Announce Type: replace-cross Abstract: Existing facial forgery detection methods typically focus on binary classification or pixel-level localization, providing little semantic insi
arXiv:2604.06989v1 Announce Type: cross Abstract: We present the first generative approach to photomosaic creation. Traditional photomosaic methods rely on a large number of tile images and color-base
arXiv:2604.06215v1 Announce Type: cross Abstract: The governance of frontier general-purpose artificial intelligence has become a public-sector problem of institutional design, not merely a technical
arXiv:2604.06684v1 Announce Type: new Abstract: Clinical Reasoning on Electronic Health Records (EHRs) is a fundamental yet challenging task in modern healthcare. While in-context learning (ICL) offer
arXiv:2604.07808v1 Announce Type: new Abstract: Full-parameter fine-tuning of large language models is constrained by substantial GPU memory requirements. Low-rank adaptation methods mitigate this cha
arXiv:2604.06358v1 Announce Type: cross Abstract: Exploring ensemble simulations is increasingly important across many scientific domains. However, supporting flexible post-hoc exploration remains cha
arXiv:2604.08046v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) significantly enhances Large Language Models (LLMs) by providing access to external knowledge. However, current res
arXiv:2604.06195v1 Announce Type: cross Abstract: Large language models often produce unsupported claims. We frame this as a misclassification error at the output boundary, where internally generated
arXiv:2604.07833v1 Announce Type: new Abstract: Embodied agents are evolving from passive reasoning systems into active executors that interact with tools, robots, and physical environments. Once gran
arXiv:2604.06285v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) have become essential for tasks such as image synthesis, captioning, and retrieval by aligning textual and visual inform
arXiv:2604.07812v1 Announce Type: new Abstract: In multimodal large language models (MLLMs), the surge of visual tokens significantly increases the inference time and computational overhead, making th
Nous Research draws its name from the ancient Greek concept of *nous* — the faculty of directly perceiving truth, reason, and divine reality — while its flagship model series, **Hermes**, is named ...
arXiv:2604.07993v1 Announce Type: new Abstract: Humans achieve complex manipulation through coordinated whole-body control, whereas most Vision-Language-Action (VLA) models treat robot body parts larg
arXiv:2504.13015v3 Announce Type: replace Abstract: Deep learning-based point cloud modeling has been widely investigated as an indispensable component of general shape analysis. Recently, transformer
arXiv:2512.09928v2 Announce Type: replace Abstract: Vision-Language-Action (VLA) models have recently enabled robotic manipulation by grounding visual and linguistic cues into actions. However, most V
arXiv:2604.06614v1 Announce Type: cross Abstract: Prompt learning has gained significant attention as a parameter-efficient approach for adapting large pre-trained vision-language models to downstream
More organizations are using natural language to query data instead of writing manual SQL. But moving an AI agent from a prototype to a production-ready tool requires rigorous, repeatable testing. Pri
arXiv:2604.06715v1 Announce Type: cross Abstract: Remote sensing semantic segmentation requires models that can jointly capture fine spatial details and high-level semantic context across complex scen
arXiv:2601.10198v3 Announce Type: replace Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities in reasoning and generation, serving as the foundation for advanced persona s
I was unable to retrieve the specific content of the tweet at the provided URL. The tweet (status ID 2042595487107133795) is from Mario Zechner (@badlogicgames), the creator of the libGDX cross-pla...
arXiv:2604.07795v1 Announce Type: new Abstract: Recent generative models can create visually plausible 3D representations of objects. However, the generation process often allows for implicit control
arXiv:2601.02627v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are becoming useful in many domains due to their impressive abilities that arise from large training datasets and
arXiv:2604.07958v1 Announce Type: new Abstract: Current video editing models often rely on expensive paired video data, which limits their practical scalability. In essence, most video editing tasks c
arXiv:2508.13657v2 Announce Type: replace-cross Abstract: Combined Algorithm Selection and Hyperparameter Optimization (CASH) has been fundamental to traditional AutoML systems. However, with the adva
arXiv:2604.06485v1 Announce Type: cross Abstract: 'Best-of-N' selection is a popular inference-time scaling method for code generation using Large Language Models (LLMs). However, to reliably identify
arXiv:2602.09987v5 Announce Type: replace-cross Abstract: Influence functions are commonly used to attribute model behavior to training documents. We explore the reverse: crafting training data that i
arXiv:2604.06796v1 Announce Type: cross Abstract: Latent variable models, including variational autoencoders (VAE), remain a central tool in modern deep generative modeling due to their scalability an
arXiv:2604.08337v1 Announce Type: new Abstract: Current vision-language pre-training (VLP) paradigms excel at global scene understanding but struggle with instance-level reasoning due to global-only s
arXiv:2602.22545v2 Announce Type: replace Abstract: Tau positron emission tomography (tau-PET) is an important in vivo biomarker of Alzheimer's disease, but its cost, limited availability, and acquisi
arXiv:2505.13126v3 Announce Type: replace-cross Abstract: Using LLMs not to predict plans but to formalize an environment into the Planning Domain Definition Language (PDDL) has been shown to improve
arXiv:2604.03044v2 Announce Type: replace-cross Abstract: We introduce JoyAI-LLM Flash, an efficient Mixture-of-Experts (MoE) language model designed to redefine the trade-off between strong performan
arXiv:2604.03815v2 Announce Type: replace-cross Abstract: Graph transformers have shown promise in overcoming limitations of traditional graph neural networks, such as oversquashing and difficulties i
arXiv:2604.07970v1 Announce Type: cross Abstract: Multi-Agent Path Finding (MAPF) is a fundamental coordination problem in large-scale robotic and cyber-physical systems, where multiple agents must co
arXiv:2604.07969v1 Announce Type: new Abstract: We present Kathleen, a text classification architecture that operates directly on raw UTF-8 bytes using frequency-domain processing -- requiring no toke
arXiv:2604.06691v1 Announce Type: new Abstract: Real world deployment of multi agent reinforcement learning MARL systems is fundamentally constrained by limited compute memory and inference time. Whil
arXiv:2604.07802v1 Announce Type: new Abstract: Large-scale vision-language models (VLMs) exhibit remarkable zero-shot capabilities, yet the internal mechanisms driving their anomaly detection (AD) pe
arXiv:2604.06255v1 Announce Type: cross Abstract: Stellar astrophysics relies critically on accurate descriptions of the physical conditions inside stars. Traditional solvers such as exttt{MESA} (Mo
arXiv:2604.07146v2 Announce Type: replace Abstract: Knowledge-based visual question answering (KB-VQA) requires vision-language models to understand images and use external knowledge, especially for r
arXiv:2511.17844v4 Announce Type: replace-cross Abstract: Fine-tuning large-scale text-to-video diffusion models to add new generative controls, such as those over physical camera parameters (e.g., sh
arXiv:2509.17183v3 Announce Type: replace-cross Abstract: Alignment plays a crucial role in Large Language Models (LLMs) in aligning with human preferences on a specific task/domain. Traditional align
arXiv:2509.09926v5 Announce Type: replace Abstract: Long-tailed semi-supervised learning (LTSSL) presents a formidable challenge where models must overcome the scarcity of tail samples while mitigatin
arXiv:2502.17421v4 Announce Type: replace-cross Abstract: As Large Language Models (LLMs) can now process extremely long contexts, efficient inference over these extended inputs has become increasingl
arXiv:2508.20765v2 Announce Type: replace-cross Abstract: The automatic understanding of video content is advancing rapidly. Empowered by deeper neural networks and large datasets, machines are increa
arXiv:2510.24561v2 Announce Type: replace-cross Abstract: LoRA has become a widely adopted method for PEFT, and its initialization methods have attracted increasing attention. However, existing method
arXiv:2503.18018v2 Announce Type: replace Abstract: We demonstrate that large language models' (LLMs) mathematical reasoning is culturally sensitive: testing 14 models from Anthropic, OpenAI, Google,
arXiv:2603.04300v2 Announce Type: replace Abstract: Foundation models in general promise to accelerate scientific computation by learning reusable representations across problem instances, yet constra
arXiv:2604.07276v1 Announce Type: cross Abstract: GROMACS is a de-facto standard for classical Molecular Dynamics (MD). The rise of AI-driven interatomic potentials that pursue near-quantum accuracy a