Knowledge catalogue
arXiv:2603.14504v2 Announce Type: replace-cross Abstract: Optimizing the noise samples of diffusion and flow models is an increasingly popular approach to align these models to target rewards at infer
arXiv:2607.04751v1 Announce Type: cross Abstract: Big goals are hard to achieve all at once; breaking them into small steps is wiser. We present Trust Region Policy Distillation (TOP-D), which transfo
arXiv:2607.04484v1 Announce Type: new Abstract: Vision and vision-language models rely on high-level visual representations that are increasingly used across recognition, retrieval, and multimodal rea
arXiv:2607.03791v1 Announce Type: cross Abstract: The Traveling Salesperson Problem (TSP) has long served as a benchmark for evaluating the strength of optimization techniques in the classical theory
arXiv:2603.07606v2 Announce Type: replace Abstract: Interpretable machine learning is essential in high-stakes domains where decision-making requires accountability, transparency, and trust. While rul
arXiv:2607.04684v1 Announce Type: new Abstract: Spatio-temporal action detection in videos requires jointly localizing actors in space and identifying action boundaries over time. A common challenge i
arXiv:2512.04632v2 Announce Type: replace Abstract: Orthogonality-based optimizers, such as Muon, have recently shown strong performance across large-scale training and community-driven efficiency cha
arXiv:2607.04728v1 Announce Type: cross Abstract: Reinforcement learning (RL) post-training for large language models (LLMs) follows a efficient paradigm of 'rollout then update', which inevitably res
arXiv:2607.04487v1 Announce Type: cross Abstract: Neural autoregressive solvers for the Multi-Attribute Vehicle Routing Problem (MAVRP) reach competitive cost but offer no per-step justification, a pr
arXiv:2607.03321v1 Announce Type: new Abstract: Continuum manipulators mounted on multi-rotor UAVs enable compliant aerial manipulation, but payloads and propeller downwash amplify out-of-plane bendin
arXiv:2607.04425v1 Announce Type: cross Abstract: Recent advances in multimodal foundation models and agent systems have driven GUI agents from single-platform task execution toward cross-platform int
arXiv:2607.03248v1 Announce Type: cross Abstract: The alignment of large language models with human preferences is commonly achieved through Reinforcement Learning from Human Feedback or Direct Prefer
arXiv:2607.04430v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in question answering (QA) systems, yet they may generate hallucinated or misaligned responses wi
arXiv:2607.05025v1 Announce Type: new Abstract: Vibration-based damage identification in civil infrastructure is a challenging, ill-posed inverse problem due to measurement noise, sparse sensor arrays
arXiv:2607.02569v1 Announce Type: new Abstract: This paper presents a safety-centered empirical evaluation of uncertainty-aware last-layer adaptation for referable diabetic retinopathy screening using
arXiv:2510.25599v2 Announce Type: replace Abstract: Regression tasks, notably in safety-critical domains, require reliable uncertainty quantification, yet the literature remains largely classification
arXiv:2607.03176v1 Announce Type: new Abstract: Understanding how households consume electricity in response to socioeconomic and climatic drivers is important for decision-makers designing energy pol
arXiv:2510.16923v3 Announce Type: replace-cross Abstract: Deep learning models deployed in safety critical applications like autonomous driving use simulations to test their robustness against adversa
arXiv:2607.05196v1 Announce Type: cross Abstract: Audio intelligence involves understanding, reasoning about, and generating both audio and speech. In this work, we introduce Nemotron-Labs-Audex-30B-A
arXiv:2607.04233v1 Announce Type: cross Abstract: Gradient based optimization methods are nowadays the methods of choice for training deep neural networks (DNNs) in artificial intelligence (AI) system
arXiv:2603.24690v2 Announce Type: replace Abstract: In-context learning (ICL) enables fast task adaptation from demonstrations without per-task parameter updates but remains highly sensitive to exampl
arXiv:2607.04498v1 Announce Type: new Abstract: With the proliferation of AI-generated content, sophisticated multimedia manipulation has raised critical concerns about malicious applications such as
arXiv:2607.04923v1 Announce Type: new Abstract: The diagnosis of spinal diseases is often assisted by 3D imaging techniques in clinical practice. However, precise 3D spinal assessment is limited by th
arXiv:2607.05133v1 Announce Type: new Abstract: World Action Models (WAMs) have shown strong potential for improving action generalization in autonomous driving by using future video prediction as den
arXiv:2510.08377v4 Announce Type: replace Abstract: Unified multimodal models have shown promising results in multimodal content generation and editing but remain largely limited to the image domain.
arXiv:2603.27044v3 Announce Type: replace-cross Abstract: Deep Reinforcement Learning (DRL) is widely recognized as sample-inefficient, a limitation attributable in part to the high dimensionality and
arXiv:2607.04882v1 Announce Type: new Abstract: Clandestine tunneling beneath oil and gas pipelines enables fuel theft, smuggling, and sabotage, yet conventional monitoring detects damage only after a
arXiv:2607.04222v1 Announce Type: new Abstract: Interpretability methods aim to reveal the features represented inside large language models (LLMs). Many existing methods begin with labeled examples o
arXiv:2607.05006v1 Announce Type: new Abstract: While various works address reflective symmetry understanding in 3D data and images, pixel-level semantic left-right prediction of in-the-wild images re
arXiv:2607.05277v1 Announce Type: cross Abstract: Defenses that provide security guarantees against prompt injection attacks rely on strict isolation between trusted instructions and untrusted data. I
arXiv:2310.00505v3 Announce Type: replace-cross Abstract: Fetal health classification is a critical task in obstetrics, enabling early identification and management of potential health problems. Howev
arXiv:2607.04688v1 Announce Type: cross Abstract: Synthesis planning aiming to find pathways of reactions for a target molecule is one of the most important and challenging tasks in drug discovery. Re
arXiv:2607.03900v1 Announce Type: new Abstract: Test-time adaptation (TTA) has emerged as a popular paradigm for improving the performance of vision-language models (e.g., CLIP) on downstream tasks. A
arXiv:2503.06269v3 Announce Type: replace-cross Abstract: Traditional white-box methods for creating adversarial perturbations against LLMs typically rely only on gradient computation from the targete
arXiv:2603.03283v2 Announce Type: replace Abstract: We dream of a future where point clouds from all domains can come together to shape a single model that benefits them all. Toward this goal, we pres
arXiv:2607.03675v1 Announce Type: new Abstract: Shapley values are widely used to attribute value to training data based on their marginal contribution to performance on a validation set. Existing pra
arXiv:2607.02893v1 Announce Type: cross Abstract: Low-bit quantization shrinks language models but treats precision as a single global hyper-parameter: every weight uses the same bit-width. We introdu
arXiv:2602.02948v3 Announce Type: replace Abstract: Inverse problems are fundamental to many scientific and engineering disciplines; they arise when one seeks to reconstruct hidden, underlying quantit
arXiv:2510.11098v5 Announce Type: replace-cross Abstract: Recent advances in large audio language models (LALMs) have greatly enhanced multimodal conversational systems. However, existing benchmarks r
arXiv:2510.05681v2 Announce Type: replace-cross Abstract: Vision-Language-Action models (VLAs) have demonstrated remarkable performance in robot control. However, they remain fundamentally limited in
arXiv:2607.02931v1 Announce Type: new Abstract: AI tools are accelerating scientific publication while the systems that review it struggle to keep up, and independent verification of published researc
arXiv:2603.18797v2 Announce Type: replace Abstract: Spatial graphs provide a lightweight and elegant representation of curvilinear anatomical structures such as blood vessels, lung airways, and neuron
arXiv:2607.05230v1 Announce Type: new Abstract: Pre-term infants are susceptible to potentially harmful apnoea-related cessations of breathing due to immature respiratory control. However, reliable re
arXiv:2607.04674v1 Announce Type: new Abstract: Recovering dynamic environment maps from a single in-the-wild video is crucial for photorealistic rendering, yet remains a challenge. Recent video gener
arXiv:2607.02927v1 Announce Type: cross Abstract: Video understanding is moving beyond closed-context perception toward open-world evidence exploration, a paradigm formalized as Video Deep Research (V
arXiv:2607.03118v1 Announce Type: new Abstract: We introduce Vidu S1, a real-time interactive video generation model supporting voice control of digital characters. Users can control video generation
arXiv:2607.03657v1 Announce Type: cross Abstract: Gloss-free Sign Language Translation (SLT) translates sign language videos into spoken-language sentences without gloss annotations, avoiding costly l
arXiv:2607.04984v1 Announce Type: new Abstract: Continual Generalized Category Discovery (C-GCD) aims to incrementally identify novel categories from sequential unlabeled data while preserving recogni
arXiv:2607.03589v1 Announce Type: new Abstract: State Space Models (SSMs) have emerged as an alternative to Vision Transformers, yet most vision SSMs inherit directional token scanning from causal seq
arXiv:2607.05247v1 Announce Type: new Abstract: Dense spatial perception is essential for physical intelligence, where visual systems are expected to recover structured, metric, and actionable represe
arXiv:2607.02819v1 Announce Type: cross Abstract: Cloud-edge Large Vision-Language Model (LVLM) inference enables efficient deployment by splitting computation between edge devices and cloud servers.
arXiv:2509.25533v2 Announce Type: replace-cross Abstract: As Vision Language Models (VLMs) are deployed across safety-critical applications, understanding and controlling their behavioral patterns has
arXiv:2508.08521v2 Announce Type: replace-cross Abstract: Vision Language Models (VLMs) are increasingly being used in a broad range of applications, bringing their security and behavioral control to
arXiv:2607.02995v1 Announce Type: cross Abstract: Vision-language models can exhibit visual concept-conditioned divergence: given images containing demographic features, corporate logos, or ideologica
arXiv:2511.20272v2 Announce Type: replace Abstract: While Multimodal Large Language Models (MLLMs) have become adept at recognizing objects, they often lack the intuitive, human-like understanding of
arXiv:2607.04517v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models are commonly treated as end-to-end action policies conditioned on natural-language task descriptions. In practice, h
arXiv:2607.05180v1 Announce Type: cross Abstract: Adverse driving conditions, such as bad weather, remain a principal barrier to autonomous driving because they degrade two things at once: what the ve
arXiv:2407.11691v5 Announce Type: replace Abstract: We present VLMEvalKit: an open-source toolkit for evaluating large multi-modality models based on PyTorch. The toolkit aims to provide a user-friend
arXiv:2410.00296v2 Announce Type: replace Abstract: Vision-language Models (VLMs) are essential for contextual understanding of both visual and textual information. However, their vulnerability to adv
arXiv:2607.02707v1 Announce Type: new Abstract: Feed-forward 3D models are commonly trained using either expensive geometric supervision or self-supervised photometric objectives, both of which provid