Knowledge catalogue
arXiv:2606.03698v1 Announce Type: new Abstract: A central goal of large language model (LLM) research is to build agentic systems that can plan, act, and adapt through sustained interaction with dynam
arXiv:2606.03291v1 Announce Type: new Abstract: Large language models (LLMs) can memorize sensitive facts, motivating unlearning methods that remove targeted knowledge without costly retraining. Howev
arXiv:2507.10419v3 Announce Type: replace-cross Abstract: We propose LoRA-MCL, a training scheme that extends next-token prediction in language models with a method designed to decode diverse, plausib
arXiv:2606.02630v1 Announce Type: cross Abstract: Patient-facing medical chatbots are commonly evaluated on single-turn prompts, yet real users push back after refusals, add urgency, and invoke author
arXiv:2606.03005v1 Announce Type: cross Abstract: Despite rapid progress, multimodal large language models (MLLMs) still fail on tasks that humans solve effortlessly, such as navigating a grid maze fr
arXiv:2601.23169v2 Announce Type: replace Abstract: Current neural architectures lack a principled way to handle interchangeable tokens, i.e., symbols that are semantically equivalent yet distinguisha
arXiv:2606.03910v1 Announce Type: cross Abstract: Disaggregated LLM inference forces the KV cache to traverse the datacenter network before decoding begins, so transfer time enters directly into the T
arXiv:2602.03681v2 Announce Type: replace Abstract: The quadratic computational complexity of softmax transformers has become a bottleneck in long-context scenarios. In contrast, linear attention mode
arXiv:2602.18690v2 Announce Type: replace-cross Abstract: Humans rehearse possible futures offline, as in mental practice and perhaps dreaming, suggesting that world models may support task learning a
arXiv:2606.03756v1 Announce Type: cross Abstract: We introduce Neural Navigation Functions (Neural-NF), a learned reactive navigation function capable of zero-shot transfer across unseen environment g
arXiv:2606.02993v1 Announce Type: new Abstract: Understanding how structured internal structure emerges during neural network training is central to the study of deep learning. We investigate this phe
arXiv:2606.03486v1 Announce Type: cross Abstract: Large language models remain vulnerable to jailbreak attacks that hide harmful intent behind seemingly ordinary requests such as role-play, translatio
arXiv:2606.03990v1 Announce Type: cross Abstract: We investigate whether neuron populations within neural networks evolve predictably with scale, extending scaling laws beyond macroscopic observables
arXiv:2606.02788v1 Announce Type: cross Abstract: Reconstructing the direction of incoming neutrinos in the IceCube Neutrino Observatory is an important problem in astrophysics. The public IceCube--Ne
arXiv:2606.03986v1 Announce Type: new Abstract: Previous work has evaluated physics reasoning in foundation models using synthetic or semi-synthetic scenes and visual question-answering tasks. However
arXiv:2509.01641v3 Announce Type: replace-cross Abstract: We propose a novel diffusion model, termed the non-identical diffusion model, and investigate its application to wireless orthogonal frequency
arXiv:2606.03551v1 Announce Type: new Abstract: Simulation has become a core infrastructure for robotics research. Unlike previous simulators, NVIDIA Isaac Sim leverages GPU acceleration to enable lar
arXiv:2606.03159v1 Announce Type: cross Abstract: As autonomous vehicle capabilities advance, the safe evaluation of driving policies in long-tail scenarios remains a critical bottleneck. In closed-lo
arXiv:2507.23035v4 Announce Type: replace Abstract: Large language models (LLMs) have demonstrated impressive capabilities across a wide range of applications, but demand substantial memory and comput
arXiv:2512.19347v3 Announce Type: replace Abstract: Robot manipulation has increasingly adopted data-driven generative policy frameworks, yet the field faces a persistent trade-off: diffusion models s
arXiv:2606.03735v1 Announce Type: cross Abstract: This paper presents a systematic study of pathfinding algorithms in the context of Dynamic Multi-Agent Pathfinding (D-MAPF), a setting that combines d
arXiv:2512.18268v3 Announce Type: replace Abstract: With the popularity of drone technologies, aerial photography has become prevalent in many daily scenarios such as environment monitoring, structure
arXiv:2606.02750v1 Announce Type: new Abstract: Representations extracted from large language models (LLMs) play an important role in many downstream applications. However, the structure of these repr
arXiv:2606.02778v1 Announce Type: cross Abstract: I present EXOVEIL, a transit detection system that learns what a star's brightness should look like and flags when reality disagrees. Unlike existing
arXiv:2606.03831v1 Announce Type: new Abstract: This paper investigates non-stationary online learning using the metric of interval regret, which requires an online algorithm to perform well over ever
arXiv:2606.03161v1 Announce Type: cross Abstract: OpenAgenet, abbreviated as OAN, is an open infrastructure project for trusted Agent interconnection. It addresses a problem that becomes visible when
arXiv:2606.03163v1 Announce Type: cross Abstract: This paper describes the technical architecture of OpenAgenet / OAN. OAN is a protocol-neutral trust layer for open Agent interconnection. It specifie
arXiv:2606.03392v1 Announce Type: new Abstract: Embodied AI in the real world requires both accurate hardware and robust vision-language-action (VLA) policies. We present OpenEAI-Platform, a fully ope
arXiv:2606.03798v1 Announce Type: new Abstract: Fin Ray-inspired soft grippers offer a promising solution for gently handling delicate, irregular objects, especially in agriculture. The objective of t
arXiv:2602.10949v2 Announce Type: replace-cross Abstract: Effective initialization in deep networks requires an understanding of random neural networks. In this work, a rigorous probabilistic analysis
arXiv:2606.03419v1 Announce Type: cross Abstract: The 2026 disproof of Erdos's unit-distance conjecture and Sawin's subsequent explicit quantitative refinement show that the maximum number u(n) of uni
arXiv:2505.08886v2 Announce Type: replace Abstract: The rising incidence of skin cancer, coupled with limited public awareness and a shortfall in clinical expertise, underscores an urgent need for adv
arXiv:2603.18639v3 Announce Type: replace Abstract: Recent progress in video generation has led to substantial improvements in visual fidelity, yet ensuring physically consistent motion remains a fund
arXiv:2606.02623v1 Announce Type: cross Abstract: Solving time-dependent partial differential equations (PDEs) is an important problem in computational science and engineering. Physics-informed neural
arXiv:2606.02946v1 Announce Type: new Abstract: Live streaming has emerged as a primary medium for social interaction and digital commerce, yet it is increasingly plagued by sophisticated risks. A fun
arXiv:2606.03518v1 Announce Type: new Abstract: As AI systems evolve from passive models into autonomous active agents capable of initiating actions, collaborating, and delegating tasks, the tradition
arXiv:2606.03890v1 Announce Type: new Abstract: Multimodal agents in robotics, AR, and autonomous driving must reason about places and layouts from continuous egocentric streams, often using evidence
arXiv:2606.03264v1 Announce Type: new Abstract: We introduce PaddleOCR-VL-1.6, an upgraded compact document parsing model built upon PaddleOCR-VL-1.5. Although PaddleOCR-VL-1.5 establishes a strong 0.
arXiv:2602.07768v3 Announce Type: replace-cross Abstract: Distilling knowledge from large Vision-Language Models (VLMs) into lightweight networks is crucial yet challenging in Fine-Grained Visual Clas
arXiv:2511.19959v2 Announce Type: replace Abstract: Federated learning (FL) has been extensively studied as a privacy-preserving training paradigm. Recently, federated block coordinate descent scheme
arXiv:2606.03556v1 Announce Type: new Abstract: Vision-language-action (VLA) models are gaining attention in robotics, yet their robustness to adversarial attacks remains largely unexplored. Existing
arXiv:2606.02995v1 Announce Type: cross Abstract: Large language models remain vulnerable to jailbreak backdoor attacks, where adversaries poison safety alignment data to embed hidden triggers that by
arXiv:2606.03915v1 Announce Type: new Abstract: We propose PatchScene, a novel diffusion-based framework for large-scale LiDAR scene completion. Unlike existing methods that rely on global latent repr
arXiv:2606.02877v1 Announce Type: new Abstract: Integrating transcriptomics and histopathology can improve cancer risk modelling, yet practical use is constrained by the limited availability of RNA pr
arXiv:2606.03236v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have substantially advanced mobile agents, yet proactive mobile assistance remains challenging because agents m
arXiv:2606.03441v1 Announce Type: cross Abstract: Autonomous vision-based perching of quadrotors on moving inclined platforms is critical for air-ground collaboration but remains challenging due to th
arXiv:2606.03479v1 Announce Type: new Abstract: Dynamic 3D Gaussian Splatting (3DGS) methods reconstruct time-varying scenes from synchronized multi-camera video using photometric supervision. When a
arXiv:2606.03376v1 Announce Type: cross Abstract: Hallucination has recently garnered significant research attention in Large Vision-Language Models (LVLMs). Direct Preference Optimization (DPO) aims
arXiv:2606.03420v1 Announce Type: new Abstract: We present PHAF-Personalized Hand Avatars in a Flash, a personalized photo-realistic hand avatar which provides high quality multi-view renders from jus
arXiv:2602.04899v2 Announce Type: replace-cross Abstract: We present a data poisoning attack -- Phantom Transfer -- with the property that, even if you know precisely how the poison was placed into an
arXiv:2511.13020v2 Announce Type: replace-cross Abstract: Although hyperspectral imaging offers unparalleled non-invasive physiological insight, its bulky hardware, slow acquisition, and regulatory bu
arXiv:2606.03598v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have achieved remarkable success in language-conditioned robotic manipulation. However, deploying these models in
arXiv:2606.03099v1 Announce Type: cross Abstract: Deep Image Search requires multi-step reasoning over rich contextual cues, such as time, location, and event relations. However, most existing LLM-bas
arXiv:2511.10055v2 Announce Type: replace Abstract: The performance of image generation has been significantly improved in recent years. However, the study of image screening is rare, and its performa
arXiv:2606.03620v1 Announce Type: cross Abstract: Self-distilled policy optimization (SDPO) has become a popular paradigm for LLM post-training, where a model learns from its own predictions condition
arXiv:2602.09708v2 Announce Type: replace-cross Abstract: We propose physics-informed spectral diffusion (PISD), a methodology that combines generative latent diffusion models with physics-informed ma
arXiv:2602.05302v3 Announce Type: replace Abstract: We present an in-depth evaluation of LLMs' ability to negotiate, a central business task requiring strategic reasoning, theory of mind, and economic
arXiv:2409.08958v3 Announce Type: replace-cross Abstract: Physics-informed neural networks (PINNs) have emerged as a powerful deep learning approach for solving partial differential equations (PDEs) i
arXiv:2606.02919v1 Announce Type: new Abstract: We present a diffusion-based method for relighting dynamic portrait videos with photorealism and temporal consistency. Our method is fueled by a hybrid
arXiv:2606.03989v1 Announce Type: new Abstract: Images composed of 2D pixel arrays are the standard input to computer vision algorithms, yet many underlying computations can be distributed across pixe