Knowledge catalogue
arXiv:2602.20200v2 Announce Type: replace-cross Abstract: Hierarchical Vision-Language-Action (VLA) models have rapidly become a dominant paradigm for robotic manipulation. It typically comprising a V
arXiv:2603.20380v2 Announce Type: replace-cross Abstract: Industry practitioners and academic researchers regularly use multi-agent systems to accelerate their work, but the applications through which
arXiv:2605.17294v1 Announce Type: new Abstract: High-resolution image editing is essential for professional and creative applications, yet existing multimodal diffusion-based editors remain computatio
arXiv:2605.16359v1 Announce Type: cross Abstract: Vision-language models improve perception by feeding increasingly long visual token sequences into language backbones, but the resulting inference cos
arXiv:2605.17355v1 Announce Type: new Abstract: As a modern commodity, language has become a vast repository of socially and psychologically significant traits and concepts, reflecting the ways people
arXiv:2508.10678v2 Announce Type: replace Abstract: In practical application scenarios, moving infrared small target detection (MIRSTD) remains highly challenging due to the target's small size, weak
A developer created a terminal user interface (TUI) tool that enables local large language models (LLMs) running on Ollama or cloud platforms to debate each other as a method for detecting and reducin
The search results focus on OpenCode + Ollama integration but don't specifically cover the AgentRouter tool. Let me provide a knowledge base entry based on what the title and context suggest: A tool d
arXiv:2603.00607v2 Announce Type: replace-cross Abstract: Multi-subject image generation requires seamlessly harmonizing multiple reference identities within a coherent scene. However, existing method
arXiv:2505.06852v2 Announce Type: replace Abstract: Random forest regression is a powerful non-parametric method that adapts to local data characteristics through data-driven partitioning, making it e
arXiv:2605.16665v1 Announce Type: new Abstract: Continental-scale knowledge of subsurface temperature is limited by the cost and sparsity of borehole measurements, but such information is essential fo
arXiv:2603.20873v2 Announce Type: replace Abstract: Federated learning (FL) is a communication-efficient collaborative learning framework that enables model training across multiple agents with privat
Its a reason why there may be increased demand for some jobs disrupted by AI: when your local midsized insurance broker wanted software, there was no way they could even consider building internally.
arXiv:2605.16812v1 Announce Type: new Abstract: While Local Differential Privacy (LDP) serves as a foundational primitive for distributed data collection, its stringent noise injection requirement oft
arXiv:2602.01705v3 Announce Type: replace-cross Abstract: Reinforcement learning has become a central paradigm for improving LLM reasoning, but most existing methods optimize policies over discrete to
arXiv:2605.17902v1 Announce Type: new Abstract: Stochastic-process-based degradation modeling is a core approach for estimating the distribution of remaining useful life (RUL); however, the selection
arXiv:2605.16357v1 Announce Type: cross Abstract: WiFi fingerprint-based indoor localization has been widely studied, but most existing approaches focus on absolute positioning and rely on dense coord
arXiv:2605.17419v1 Announce Type: cross Abstract: Rainfall-induced landslides pose a growing risk worldwide as climate change intensifies extreme rainfall events. To provide sufficient evacuation time
arXiv:2605.17980v1 Announce Type: new Abstract: Diffusion-based methods demonstrate significant potential for remote sensing image super-resolution at large scaling factors, particularly in reference-
arXiv:2605.17205v1 Announce Type: new Abstract: Linguistic annotation of transcribed speech is essential for research in language acquisition, language disorders, and sociolinguistics, yet remains lab
arXiv:2603.20216v2 Announce Type: replace-cross Abstract: Diffusion language models (DLMs) have emerged as a promising alternative to autoregressive (AR) models, offering sub-linear generation latency
arXiv:2605.17303v1 Announce Type: new Abstract: Recovering a dynamic 3D scene from a long monocular video is crucial for dense geometry, camera motion, and temporal correspondence to remain consistent
LTX 2.3's image-to-video (i2v) feature exhibits gradual video saturation and brightness degradation over extended generation sequences, though users have developed Python code aimed at recovering thes
arXiv:2509.15357v2 Announce Type: replace Abstract: Diffusion models have achieved strong results in text-to-image generation, but important limitations remain as prompts become more structured and mu
arXiv:2605.18652v1 Announce Type: new Abstract: Recent GUI agents have made substantial progress in visual grounding and action prediction, yet they remain brittle in long-horizon tasks that require m
arXiv:2605.17758v1 Announce Type: new Abstract: Synthetic data is widely used in healthcare to create datasets that are similar to original data but without the privacy concerns. Generating and evalua
arXiv:2601.21458v2 Announce Type: replace Abstract: Modern deepfakes have evolved into localized and intermittent manipulations that require fine-grained temporal localization to mitigate severe digit
arXiv:2605.17661v1 Announce Type: cross Abstract: Autonomous agile robots need more than metric geometry: they must understand objects, rooms, places, and spatial relations for search, inspection, exp
arXiv:2605.17885v1 Announce Type: cross Abstract: Although artificial intelligence (AI) now matches or exceeds human performance across numerous cognitive tasks, creativity remains a highly contested
A Reddit user seeks guidance on configuring ComfyUI workflows to texture 3D models using orthographic reference images from multiple angles. The discussion likely covers best practices for using Stabl
arXiv:2605.16979v1 Announce Type: new Abstract: Mobile robots operating in human-centered environments must generate not only collision-free paths but also trajectories that follow local behavioral co
arXiv:2605.18481v1 Announce Type: new Abstract: Interpreting the decisions of deep image classifiers remains challenging, particularly in black-box settings where model internals are inaccessible. We
arXiv:2605.16707v1 Announce Type: cross Abstract: The rapid evolution of digital health technologies is redefining healthcare services worldwide. The integration of wireless communication and Internet
arXiv:2605.16391v1 Announce Type: cross Abstract: Inertial measurement units (IMUs) are fundamental sensing components in multi-source integrated navigation systems, and their performance directly det
arXiv:2603.14371v2 Announce Type: replace-cross Abstract: Embodied AI agents increasingly require parallel execution of multiple tasks, such as manipulation, conversation, and memory construction, fro
arXiv:2605.16863v1 Announce Type: cross Abstract: Compositional diffusion models offer a promising route to long-horizon planning by denoising multiple overlapping sub-trajectories while ensuring that
arXiv:2605.18067v1 Announce Type: new Abstract: Deploying large language model (LLM) on edge device enables personalized LLM agents for various users. The growing availability of diverse personalized
arXiv:2406.14427v4 Announce Type: replace Abstract: A central challenge for intelligent agents in an uncertain world is striking the right balance between utility maximization and resource use, not on
arXiv:2605.17039v1 Announce Type: new Abstract: The wide adoption of residential photovoltaic (PV) systems introduces new challenges for generation fraud detection (FD). Unlike traditional electricity
arXiv:2605.16346v1 Announce Type: cross Abstract: LLM-based multi-agent systems (LLM-MAS) have become a promising paradigm for solving complex tasks through role specialization, tool use, memory, and
arXiv:2605.18032v1 Announce Type: cross Abstract: Multi-agent LLM workflows -- systems composed of multiple role-specific LLM calls -- often outperform single-prompt baselines, but they remain difficu
arXiv:2602.21426v2 Announce Type: replace Abstract: We consider the problem of sampling from a posterior distribution arising in Bayesian inverse problems in science, engineering, and imaging. Our met
arXiv:2605.16647v1 Announce Type: cross Abstract: Fully homomorphic encryption (FHE) changes sequence-model design because rotations, encrypted products, ciphertext materialization, multiplicative dep
arXiv:2605.17869v1 Announce Type: new Abstract: A widespread assumption in local feature research holds that classical handcrafted descriptors are accuracy-limited relics best replaced by learned alte
arXiv:2605.17552v1 Announce Type: new Abstract: Federated learning on edge devices must cope with non-IID client data and tight memory budgets. Adaptive optimizers like Adam stabilize training under d
arXiv:2605.16604v1 Announce Type: new Abstract: Efficient agentic systems should incur expensive frontier-model costs only on decisions where a cheaper local model is likely to fail. Existing LLM casc
arXiv:2509.13270v2 Announce Type: replace-cross Abstract: We introduce RadGame, an AI-powered gamified platform for radiology education that targets two core skills: localizing findings and generating
arXiv:2605.16535v1 Announce Type: cross Abstract: Industrial multi-label document understanding pipelines score candidate labels and threshold or rank them to form a label set per document. This early
arXiv:2605.17566v1 Announce Type: new Abstract: With the rapid progress of multimodal foundation models and predictive pre-training, an important open question is how to equip 3D point clouds with a p
arXiv:2605.18729v1 Announce Type: cross Abstract: The ability to navigate and interact with complex environments is central to real-world embodied agents, yet navigation in unseen environments remains
arXiv:2510.26635v3 Announce Type: replace-cross Abstract: Summary: SAMRI is an MRI-specialized adaptation of the Segment Anything Model achieving superior whole-body MRI segmentation, particularly for
arXiv:2605.16515v1 Announce Type: new Abstract: Animals are described as effectively camouflaged when they blend seamlessly with their surrounding, yet no standardized quantitative measure of this sea
arXiv:2605.17249v1 Announce Type: new Abstract: Vision-Language Navigation (VLN) approaches have currently followed two primary paradigms: the end-to-end Vision-Language Model (VLM) policy fine-tuned
arXiv:2605.18018v1 Announce Type: cross Abstract: We present SWIM (See What I Mean), a novel training strategy that aligns vision and language representations to enable fine-grained object understandi
arXiv:2605.16842v1 Announce Type: new Abstract: Diffusion Multi-Modal Large Language Models (dMLLMs) are powerful for image generation, but optimizing them through reinforcement learning (RL) remains
arXiv:2605.16650v1 Announce Type: cross Abstract: Evaluating multi-turn dialogue systems remains challenging because response quality depends not only on the current prompt, but also on previously est
arXiv:2510.23641v2 Announce Type: replace-cross Abstract: Transformers are very effective in capturing both global and local correlations within high-energy particle collisions, but they present deplo
arXiv:2605.18466v1 Announce Type: new Abstract: Segmenting vocal tract articulators in real-time MRI (rtMRI) is a challenging dynamic image segmentation problem characterized by low contrast, rapid mo
arXiv:2605.18003v1 Announce Type: cross Abstract: Deploying adaptive intelligence at the edge remains challenging due to the high computational and energy cost of training neural models. Spiking Neura
arXiv:2605.16671v1 Announce Type: new Abstract: Rapid biodiversity loss underscore the urgency of effective monitoring, yet manual surveys remain resource-intensive. While on-device AI offers a scalab