Knowledge catalogue
I also agree with this comment that the current view of FDEs is likely too limited to actually catalyze organizational transformation. @emollick Are they doing org change, though? The army of forward
arXiv:2605.08577v1 Announce Type: new Abstract: In modern GANs, maintaining an Exponential Moving Average (EMA) of the generator's weights is a standard practice, as such an averaged model consistentl
arXiv:2605.08180v1 Announce Type: cross Abstract: Modern IoT and sensor networks generate vast amounts of data, posing significant challenges for storage, transmission, and real-time processing. Tradi
arXiv:2601.21926v4 Announce Type: replace Abstract: Diffusion-based visuomotor policies built on 3D visual representations have achieved strong performance in learning complex robotic skills. However,
arXiv:2602.03677v2 Announce Type: replace Abstract: Modality following is the ability to selectively leverage multimodal contexts based on user instructions. It is fundamental to the safety and reliab
arXiv:2605.09132v1 Announce Type: new Abstract: Vision--language models (VLMs) show promise for clinical decision support in radiology because they enable joint reasoning over radiological images and
arXiv:2605.09440v1 Announce Type: cross Abstract: Clinical reports are often fragmented across healthcare institutions because privacy regulations and data silos limit direct information sharing. When
arXiv:2605.09487v1 Announce Type: new Abstract: Modern embodied agents achieve impressive performance, but their task knowledge is often stored in neural weights, latent state, or prompt-bound memory,
arXiv:2605.10253v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) is a widely adopted paradigm for enhancing LLMs in medical applications by incorporating expert multimodal knowle
arXiv:2605.09060v1 Announce Type: new Abstract: Multilingual vision-language models exhibit systematic performance gaps across languages, but the mechanism remains ambiguous: cross-language divergence
arXiv:2605.08626v1 Announce Type: cross Abstract: Large language models (LLMs) are transforming society, powering applications from smartphone assistants to autonomous driving. Yet cloud-based LLM ser
arXiv:2605.06366v2 Announce Type: replace Abstract: Diffusion language models (DLMs) have recently emerged as competitive alternatives to autoregressive (AR) language models, yet differences in their
arXiv:2605.08209v1 Announce Type: new Abstract: Deep learning methods are widely used under diverse resource constraints, resulting in models of varying sizes, such as the Vision Transformer (ViT) ser
arXiv:2511.17994v2 Announce Type: replace Abstract: We study differentially private model training with stochastic gradient descent under learning rate scheduling and correlated noise. Although correl
arXiv:2605.08805v1 Announce Type: new Abstract: Audio-Visual Segmentation (AVS) targets pixel level localization of sounding emitting objects in videos. However, existing models rely on dense cross-mo
arXiv:2605.10641v1 Announce Type: cross Abstract: Large Vision-Language Models (VLMs) are successful in addressing a multitude of vision-language understanding tasks, such as Visual Question Answering
arXiv:2605.08448v1 Announce Type: new Abstract: Semi-supervised learning approaches have been investigated as a means to enhance the analysis of social media data in disaster management contexts. In t
arXiv:2605.10760v1 Announce Type: new Abstract: Collaborative photorealistic 3D reconstruction from multiple agents enables rapid large-scale scene capture for virtual production and cooperative multi
arXiv:2605.08094v1 Announce Type: cross Abstract: Accurate clinical diagnosis requires extensive domain knowledge and complex clinical reasoning capabilities. Although large language models (LLMs) hol
arXiv:2605.08827v1 Announce Type: new Abstract: The safety of mental health AI is often judged at the wrong temporal scale. Current evaluations typically score isolated responses, endpoint outcomes, o
arXiv:2605.09990v1 Announce Type: new Abstract: Data-intensive applications, ranging from large-scale retrieval systems to advanced data pipelines, are increasingly bottlenecked by the processing of h
arXiv:2605.08277v1 Announce Type: cross Abstract: Many-shot jailbreaking (MSJ) causes safety-aligned language models to answer harmful queries by preceding them with many harmful question-answer demon
arXiv:2605.08117v1 Announce Type: cross Abstract: Inertial Measurement Unit (IMU)-based Human Activity Recognition (HAR) aims to interpret and classify user behaviors from temporal motion signals. Rec
arXiv:2605.09999v1 Announce Type: new Abstract: Diffusion-based trajectory planners can synthesize rich, multimodal robot motions, but their iterative denoising makes online planning and control prohi
arXiv:2605.09301v1 Announce Type: cross Abstract: The Capacitated Vehicle Routing Problem (CVRP) underpins modern last-mile logistics. Current Neural Combinatorial Optimization (NCO) methods construct
arXiv:2605.09595v1 Announce Type: cross Abstract: Reinforcement learning (RL) has enabled robust quadruped locomotion over complex terrain, but most learned controllers are trained offline with backpr
arXiv:2605.08144v1 Announce Type: cross Abstract: Diffusion models have achieved remarkable success across a wide range of generative tasks, yet their training paradigm largely treats injected noise a
arXiv:2605.08758v1 Announce Type: cross Abstract: Driven by the rapid expansion of e-commerce and small-batch production, the size of the intralogistics load unit of finished goods, semi-finished good
arXiv:2511.01292v2 Announce Type: replace-cross Abstract: Pretrained Transformers can perform in-context learning (ICL) from a few demonstrations, but this ability can fail sharply when the test distr
arXiv:2605.08169v1 Announce Type: cross Abstract: Automated culprit identification in surveillance systems is a critical task that requires high accuracy along with computational efficiency for real-t
arXiv:2512.02066v2 Announce Type: replace-cross Abstract: Quantum machine learning has emerged as a promising approach to improve feature extraction and classification tasks in high-dimensional data d
arXiv:2605.10377v1 Announce Type: new Abstract: Cooperative multi-agent reinforcement learning often assumes a fixed execution team, yet many decentralized systems must operate with varying numbers of
arXiv:2605.10118v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have demonstrated exceptional general reasoning capabilities. However, their performance in embodied navigation remains hi
arXiv:2605.10349v1 Announce Type: cross Abstract: Annotating bounding boxes is costly and limits the scalability of object detection. This challenge is compounded by the need to preserve high accuracy
arXiv:2605.09915v1 Announce Type: cross Abstract: The implicit policy of maintaining relatively stable acceptance rates at top AI conferences, despite exponentially growing submissions, introduces a c
arXiv:2605.09365v1 Announce Type: new Abstract: Enterprise workloads are dominated by deterministic, structured, and knowledge-dependent tasks operating under strict cost, latency, and reliability con
arXiv:2605.08574v1 Announce Type: new Abstract: As synthetic images become increasingly realistic, reliable synthetic image detection techniques are of pressing need to prevent their misuse. Despite s
arXiv:2605.08879v1 Announce Type: new Abstract: Unconstrained fine-tuning of flow-matching Vision-Language-Action (VLA) models drives dense parameter overwrites, degrading pre-trained capabilities. We
arXiv:2605.08651v1 Announce Type: cross Abstract: Video anomaly detection (VAD) systems often prioritize accuracy while overlooking privacy concerns, limiting their suitability for real-world deployme
arXiv:2605.08152v1 Announce Type: cross Abstract: The intersection of Artificial Intelligence (AI) and distributed systems has given rise to Federated Learning (FL), a paradigm that enables decentrali
arXiv:2605.10730v1 Announce Type: new Abstract: We present Qwen-Image-2.0, an omni-capable image generation foundation model that unifies high-fidelity generation and precise image editing within a si
arXiv:2605.10430v1 Announce Type: cross Abstract: Estimating heterogeneous treatment effects with machine learning has attracted substantial attention in both academic research and industrial practice
arXiv:2605.08776v1 Announce Type: new Abstract: Reasoning-centric large language models (LLMs) achieve strong performance by generating intermediate reasoning trajectories, but often incur excessive t
arXiv:2512.20012v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are emerging as key enablers of automation in domains such as telecommunications, assisting with tasks including
arXiv:2605.09410v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models remain brittle in long-horizon, contact-rich manipulation because success-only imitation provides little supervisi
arXiv:2605.08238v1 Announce Type: cross Abstract: Cardiac magnetic resonance (CMR) segmentation underpins quantitative assessment of ventricular structure and function, yet reliable delineation remain
arXiv:2605.09666v1 Announce Type: cross Abstract: Multiple Sclerosis (MS) is a chronic autoimmune disease that can significantly reduce the quality of life of a patient. Existing treatment options can
arXiv:2605.09905v1 Announce Type: cross Abstract: Automatic sleep staging commonly adopts Transformers under the assumption that they learn complex long-range dependencies. We challenge this view by r
arXiv:2603.05066v2 Announce Type: replace Abstract: Single-task RL agents are typically trained under a fixed reward function, which limits their robustness to reward misspecification and their abilit
arXiv:2602.01861v3 Announce Type: replace-cross Abstract: Room impulse responses (RIRs) are essential for many acoustic signal processing tasks, yet measuring them densely across space is often imprac
arXiv:2603.14694v2 Announce Type: replace-cross Abstract: Rapid structural damage assessment from remote sensing imagery is essential for timely disaster response. Within human-machine systems (HMS) f
arXiv:2605.10235v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have expanded the context window to beyond 128K tokens, enabling long-document understanding and multi-s
arXiv:2510.03648v2 Announce Type: replace Abstract: Continuous learning of novel classes is crucial for edge devices to preserve data privacy and maintain reliable performance in dynamic environments.
arXiv:2510.20129v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) remain vulnerable to jailbreak attacks, where adversarially crafted prompts induce policy-violating responses des
SAP SE today introduced at Sapphire 2026, the company’s annual conference, what it calls Autonomous Enterprise, a suite of artificial intelligence tools and agents designed to enhance how humans and A
In today's hyper-connected market, an enterprise's most valuable asset — mission-critical data — often remains trapped in legacy silos. For years, leadership teams have navigated a data pipeline dilem
arXiv:2605.10142v1 Announce Type: cross Abstract: Artificial intelligence models are increasingly scaled to improve predictive accuracy, yet it remains unclear whether scale improves the quality of po
arXiv:2601.22638v2 Announce Type: replace-cross Abstract: The exponential growth of machine learning submissions has strained the traditional peer review process, resulting in slow feedback loops for
arXiv:2605.08313v1 Announce Type: cross Abstract: Large language models (LLMs) rely on deterministic pseudorandom number generators (PRNGs) for autoregressive sampling, creating a critical supply-chai
arXiv:2603.26089v2 Announce Type: replace-cross Abstract: The ability to represent oneself and others as agents with knowledge, intentions, and belief states that guide their behavior - Theory of Mind