Knowledge catalogue
arXiv:2607.16097v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has become central to improving large language models (LLMs) on complex reasoning tasks, yet RL post-training is l
arXiv:2608.09287v1 Announce Type: cross Abstract: Data-Free Knowledge Distillation (DFKD) transfers knowledge from a pretrained teacher model to a compact student model by synthesizing semantically in
arXiv:2608.07525v1 Announce Type: cross Abstract: Hallucination remains a persistent challenge for Multimodal Large Language Models (MLLMs), severely limiting their reliability in high-stakes applicat
arXiv:2608.08359v1 Announce Type: new Abstract: Ordinal regression, also called ordinal classification, is classification of ordinal data, in which the underlying target variable is categorical and co
arXiv:2608.09143v1 Announce Type: new Abstract: Instruction-driven editing of 3D human motion requires precise spatiotemporal localization, rich semantic grounding, and strict preservation of unmodifi
arXiv:2608.08627v1 Announce Type: new Abstract: Sparse mixture-of-experts (MoE) layers expand recommendation capacity through conditional computation, yet a trained checkpoint still stores and routes
arXiv:2608.07864v1 Announce Type: new Abstract: Industrial anomaly inspection faces a major challenge due to the lack of real-world anomaly samples. While generative models are used to create anomaly
arXiv:2608.08676v1 Announce Type: cross Abstract: Semantic vision encoders have become a central visual interface for multimodal understanding and semantic conditioning in image generation. However, t
arXiv:2608.09356v1 Announce Type: new Abstract: Closely related languages written in different scripts expose little surface overlap to multilingual models, limiting cross-lingual transfer. We compare
arXiv:2608.09209v1 Announce Type: new Abstract: Neural language models trained on large crowdsourced corpora frequently exploit spurious surface patterns tied to target labels without true linguistic
arXiv:2608.09098v1 Announce Type: new Abstract: End-to-end planning has shown strong promise for autonomous driving, but most existing methods are designed for structured urban roads and generalize po
arXiv:2608.09154v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly expected to follow long lists of constraints in complex instructions, and synthesizing instructions from a
arXiv:2608.09630v1 Announce Type: cross Abstract: Unsupervised domain adaptation is a widespread set of methods that leverages the knowledge of a labeled source domain to train a model to perform well
arXiv:2409.07558v2 Announce Type: replace Abstract: Rigid point cloud registration is a fundamental problem and highly relevant in robotics and autonomous driving. Nowadays deep learning methods can b
arXiv:2608.08791v1 Announce Type: new Abstract: Diffusion language models use broad context to create text, suggesting they might handle input noise better than standard models. Testing reveals this i
arXiv:2608.09438v1 Announce Type: new Abstract: Diffusion transformer (DiT), a rapidly emerging architecture for image generation, has gained much attention. However, despite ongoing efforts to improv
arXiv:2608.08771v1 Announce Type: new Abstract: Myopia-induced posterior-pole remodeling is frequently accompanied by Optic Disc (OD) deformation and Peripapillary Atrophy (PPA), both of which provide
arXiv:2608.07870v1 Announce Type: new Abstract: Improving sample efficiency remains a core challenge in reinforcement learning (RL), especially in real-world settings like robotics, where data collect
arXiv:2608.08622v1 Announce Type: new Abstract: Large vision-language models (LVLMs) have demonstrated strong performance in open-ended video understanding, yet they remain prone to fluent responses u
arXiv:2608.09448v1 Announce Type: cross Abstract: Test-time training (TTT) offers a lightweight way to adapt vision--language--action (VLA) policies from unlabeled deployment streams, but it remains d
arXiv:2603.02984v2 Announce Type: replace-cross Abstract: Normalizing flows can be used to construct unbiased, reduced-variance estimators for lattice field theory observables that are defined by a de
arXiv:2511.18121v2 Announce Type: replace-cross Abstract: While Multimodal Large Language Models (MLLMs) excel on benchmarks, their processing paradigm differs from the human ability to integrate visu
arXiv:2608.07994v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) is essential for enterprise knowledge question answering (QA), particularly in domains with complex product documen
arXiv:2608.08477v1 Announce Type: new Abstract: We present VectraYX-Vision-1B, a sub-2B vision-language model (VLM) for Spanish/LATAM cybersecurity imagery, coupling a frozen SigLIP-so400m encoder to
arXiv:2608.09286v1 Announce Type: cross Abstract: Global medium-range weather forecasting requires modeling structured yet state-dependent interactions among heterogeneous atmospheric fields. Existing
arXiv:2608.09537v1 Announce Type: new Abstract: Foundation world models have made remarkable progress in planning, simulation, and embodied intelligence. However, optimizing a pretrained world model t
arXiv:2608.07978v1 Announce Type: cross Abstract: Multi-agent large language model(LLM)systems are applied to structural design,yet most use one-shot generation and cannot verify their output,leaving
arXiv:2608.09276v1 Announce Type: new Abstract: Planetary geology relies on historical, interpretive reasoning to reconstruct past events from diverse observations. Here, we present a step toward an a
arXiv:2608.09698v1 Announce Type: cross Abstract: Great fiction earns its verisimilitude through precise details, from how a longsword is gripped to pierce armor gaps to why a bleeding corpse cannot y
arXiv:2608.08558v1 Announce Type: new Abstract: World Action Models (WAMs) improve robot policy learning by jointly modeling future visual dynamics and actions. However, their scalability and generali
arXiv:2608.09573v1 Announce Type: new Abstract: Natural-language-driven 'vibe coding' enables the one-shot generation of visually rich and interactive web applications, yet reliable assessment of thei
arXiv:2608.09110v1 Announce Type: new Abstract: Reconstructing 3D shapes from a single image remains a fundamental yet challenging problem in computer vision. Traditional monocular 3D generation pipel
arXiv:2602.14633v2 Announce Type: replace Abstract: We introduce VIGIL (Visual Inconsistency & Generative In-context Lucidity), a benchmark dataset and framework that provides a fine-grained categoriz
arXiv:2608.07886v1 Announce Type: cross Abstract: Vision-language grounding connects language to visual content, yet most existing formulations reduce grounding to a unidirectional localization proble
arXiv:2608.07726v1 Announce Type: new Abstract: Estimating volumetric mechanical properties, including Young's modulus, Poisson's ratio, and density at each voxel, is intrinsically ambiguous from visi
arXiv:2510.16598v2 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) encounter significant computational and memory bottlenecks from the massive number of visual tokens generat
arXiv:2608.09122v1 Announce Type: cross Abstract: The localized depiction of perceptual quality has long been a crucial, yet underexplored, challenge in image quality assessment (IQA). Existing approa
arXiv:2608.08832v1 Announce Type: new Abstract: Distributed deployment of large vision foundation models often partitions a ViT backbone and exchanges intermediate token features between computing nod
arXiv:2608.08630v1 Announce Type: new Abstract: Vision Language Models (VLMs) face significant challenges with ultra-long, interleaved image-text sequences due to the quadratic complexity of self-atte
arXiv:2608.08366v1 Announce Type: new Abstract: Spatial transcriptomics can resolve gene expression at single-cell resolution, but it is costly, limited to targeted panels of a few hundred to a few th
arXiv:2608.08569v1 Announce Type: new Abstract: Recent advancements in Speech Large Language Models have demonstrated remarkable capabilities in understanding complex audio tasks. Despite this progres
arXiv:2608.08219v1 Announce Type: cross Abstract: Video anomaly detection (VAD) is a critical yet challenging task due to the complex and diverse nature of real-world scenarios. Traditional deep learn
arXiv:2608.08725v1 Announce Type: new Abstract: Vision-language-action (VLA) policies generate robot controls autoregressively, making closed-loop latency dominated by repeated target-model forward pa
arXiv:2608.09494v1 Announce Type: cross Abstract: In this paper we provide Monte Carlo and deep neural network approximations for stochastic representations of solutions to linear elliptic partial dif
arXiv:2608.08617v1 Announce Type: new Abstract: Group discussion-based teaching is widely used to foster collaborative learning, yet teachers in physical classrooms often struggle to simultaneously mo
arXiv:2608.09321v1 Announce Type: new Abstract: Cross-view geo-localization is challenging due to drastic viewpoint changes and large appearance discrepancies between street-level and satellite imager
arXiv:2608.09447v1 Announce Type: cross Abstract: On-policy distillation (OPD) aligns a student with a teacher on trajectories sampled from the student itself, reducing the train-test state mismatch o
arXiv:2401.04013v2 Announce Type: replace Abstract: Deep learning models, such as wide neural networks, can be conceptualized as nonlinear dynamical physical systems characterized by a multitude of in
arXiv:2608.07593v1 Announce Type: cross Abstract: Context-aware recommender systems have long recognized that factors such as location, time, and weather shape where and what people choose to eat. Exi
arXiv:2506.01952v2 Announce Type: replace-cross Abstract: Powered by large language models (LLMs), web browsing agents operate graphical user interfaces in a human-like manner, offering a transparent
arXiv:2608.08287v1 Announce Type: new Abstract: GPU language comparisons are almost always run on tiled dense linear algebra, where every toolchain is good and the differences are small. We implement
arXiv:2608.08453v1 Announce Type: new Abstract: Under the current standard, Agent Skills are SKILL.md files that combine instructions with supporting resources, enabling Large Language Model (LLM) age
arXiv:2608.07565v1 Announce Type: cross Abstract: Conversational assistants increasingly recommend follow-up edits to help users continue a task. Existing systems primarily target text-only interactio
arXiv:2608.08944v1 Announce Type: cross Abstract: A failed retrieval-augmented generation (RAG) answer can be consistent with several unseen responses to evidence repair. We introduce Pair-ID, an offl
arXiv:2608.08577v1 Announce Type: new Abstract: Fraud operations must allocate events among automatic approval, analyst review, and automatic blocking even though the labels needed to evaluate these a
arXiv:2608.09080v1 Announce Type: cross Abstract: Large Language Models (LLMs) have achieved strong performance in medical question answering and clinical reasoning tasks. However, their reliability u
arXiv:2607.10202v2 Announce Type: replace Abstract: Forced-choice probes with counterbalanced orientations are a standard tool for measuring language-model 'value dispositions,' and a concentration/ex
arXiv:2608.09490v1 Announce Type: new Abstract: Task arithmetic treats fine-tuning displacements as composable directions in weight space, yet it remains unclear when parameter addition reflects predi
arXiv:2608.08132v1 Announce Type: new Abstract: Reconstruction of 3D objects from a single image is a challenging research problem in computer vision. The key challenge is the lack of critical informa
arXiv:2608.07911v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models have outgrown accelerator memory, and offloading expert weights to host memory is now standard. This makes expert cache