Knowledge catalogue
arXiv:2606.04420v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) approximate solutions of ODEs and PDEs by minimising a weighted combination of residual, boundary, initial, and
arXiv:2606.04405v1 Announce Type: cross Abstract: Modern Transformer architectures frequently employ normalization mechanisms such as RMSNorm and Query-Key Normalization, making parts of the model app
arXiv:2606.04176v1 Announce Type: new Abstract: We study a distributional generalization of the matrix completion problem in which each entry of the target matrix is a probability distribution rather
arXiv:2606.05008v1 Announce Type: cross Abstract: As multi-modal models advance towards long-form video understanding, memory emerges as a critical capability. Despite substantial efforts in developin
arXiv:2606.04829v1 Announce Type: new Abstract: Building a general-purpose whole-body controller is essential for enabling diverse motion capabilities in humanoid robots across a wide range of downstr
arXiv:2606.05068v1 Announce Type: new Abstract: Conventional Generative Adversarial Networks (GANs) for Single Image Super-Resolution (SISR) often struggle with hallucinated artifacts, largely because
arXiv:2606.04534v1 Announce Type: new Abstract: Agile quadrotor flight in cluttered scenes requires more than a reactive mapping from a depth image to a control command: the vehicle must remember whic
arXiv:2602.02405v2 Announce Type: replace-cross Abstract: Improving the reasoning capabilities of large language models (LLMs) typically relies either on the model's ability to sample a correct soluti
arXiv:2606.04880v1 Announce Type: new Abstract: Selection is a core operation in interactive image editing. To be practical, a user should be able to specify and disambiguate the desired selection reg
arXiv:2606.04513v1 Announce Type: new Abstract: Lane-level maps are critical infrastructure for autonomous driving and lane-level navigation, yet constructing and maintaining standardized lane network
arXiv:2606.04027v1 Announce Type: cross Abstract: Diffusion large language models (dLLMs) generate text by iteratively denoising partially masked sequences under bidirectional context, exposing a safe
arXiv:2510.01532v2 Announce Type: replace Abstract: In semi-supervised segmentation, capturing meaningful semantic structures from unlabeled data is essential. This is particularly challenging in hist
arXiv:2606.04931v1 Announce Type: new Abstract: Mean-based algorithms are a class of online learning algorithms that assign low probability to actions with low average rewards. Recent work indicates t
arXiv:2606.04767v1 Announce Type: cross Abstract: The robustness of deep neural networks is crucial for safety-critical deployments, yet existing evaluation methods are often attack-dependent and lack
arXiv:2606.04326v1 Announce Type: cross Abstract: Concept bottleneck models predict outcomes from high-level concepts detected in inputs. Although concepts provide a simple way to reap benefits from i
arXiv:2511.00801v4 Announce Type: replace Abstract: Text-guided medical image editing must satisfy the requested pathology while preserving anatomy, modality-specific appearance, and clinical plausibi
arXiv:2603.18577v2 Announce Type: replace Abstract: Text-guided image editors can now manipulate authentic medical scans with high fidelity, enabling lesion implantation/removal that threatens clinica
arXiv:2606.04442v1 Announce Type: cross Abstract: AI systems increasingly need to combine two demanding capabilities: navigating multi-session conversation history and performing deep reading comprehe
arXiv:2511.07107v3 Announce Type: replace Abstract: Ensuring the safety of Large Language Models (LLMs) is critical for real-world deployment. However, current safety measures often fail to address im
arXiv:2506.05233v2 Announce Type: replace-cross Abstract: Sequence modeling is currently dominated by causal transformer architectures that use softmax self-attention. Although widely adopted, transfo
arXiv:2606.04621v1 Announce Type: new Abstract: We present MeshFlow, a new method for generating artist-like 3D meshes. Current mesh generators often adopt Auto-Regressive (AR) next-token prediction,
arXiv:2606.04366v1 Announce Type: new Abstract: Conventional patchified Transformers operate on uniform spatial partitions, distributing computational effort evenly across the domain irrespective of l
arXiv:2606.04688v1 Announce Type: new Abstract: Autoregressive mesh generation has gained attention by tokenizing meshes into sequences and training models in a language-modeling fashion. However, exi
arXiv:2606.05031v1 Announce Type: new Abstract: Generative visual models fundamentally struggle with precise spatial control. This arises from a core disconnect: models can process textual description
arXiv:2606.04191v1 Announce Type: cross Abstract: We describe our approach to the CTF4Science Lorenz challenge, a benchmark that mixes short-horizon forecasting, long-time distribution matching, and t
arXiv:2601.07036v2 Announce Type: replace-cross Abstract: Hybrid reasoning language models are commonly controlled through high-level Think/No-think instructions to regulate reasoning behavior, yet we
arXiv:2606.04171v1 Announce Type: cross Abstract: File-type classification underlies many workflows like malware triage, forensic carving, packet inspection, and storage indexing. Learned systems such
arXiv:2606.04569v1 Announce Type: new Abstract: Underground mines present extreme conditions for autonomous robot navigation: GPS is denied, lighting is degraded, and tunnel topology is loop-rich and
arXiv:2606.04627v1 Announce Type: new Abstract: Mobile agents are increasingly expected to operate everyday applications from screenshots and language goals, where reliable control requires reasoning
arXiv:2606.04413v1 Announce Type: new Abstract: Helpful-only models, that is, models that are trained to always follow user intent, are valuable for dangerous capability evaluations and other areas of
arXiv:2606.04231v1 Announce Type: cross Abstract: Recent advances in multimodal retrieval-augmented generation (MM-RAG) have shifted toward minimal parsing, relying on page-level images for producing
arXiv:2505.22988v3 Announce Type: replace-cross Abstract: The goal of quantization is to produce a compressed model whose output distribution is as close to the original model's as possible. To do thi
arXiv:2606.04499v1 Announce Type: cross Abstract: Cancer care requires a longitudinal approach in which treatments are planned and delivered over time according to the needs of each individual patient
arXiv:2606.04349v1 Announce Type: cross Abstract: Conventional Post-Training Quantization (PTQ) methods struggle with 4-bit Omni-modal Large Language Models (OLLMs) due to the extreme distribution het
arXiv:2606.04414v1 Announce Type: new Abstract: Multi-view cardiac magnetic resonance (CMR) imaging provides complementary anatomical information and is widely used for noninvasive disease assessment.
arXiv:2602.11189v2 Announce Type: replace-cross Abstract: Modeling peptide cyclization is critical for the virtual screening of candidate peptides with desirable physical and pharmaceutical properties
arXiv:2606.04158v1 Announce Type: new Abstract: Multi-agent Next-Best-View (NBV) selection for safe path planning in uncertain and unknown environments requires informative, safety-aware, and efficien
arXiv:2606.04992v1 Announce Type: new Abstract: The handling and assembly of instruments during surgery imposes high cognitive demands on scrub nurses, particularly when instruments are unfamiliar. We
arXiv:2606.05150v1 Announce Type: cross Abstract: The radial basis function neural network (RBFN) trained with a gradient descending algorithm provides an effective fully connected structure in both s
arXiv:2606.04365v1 Announce Type: cross Abstract: Radiology reports describe kidney lesions by type, size, enhancement, and attenuation, yet existing 3D methods predict only at the patient or organ le
arXiv:2606.04581v1 Announce Type: cross Abstract: Speculative inference (SPIN) was originally developed as an efficient architecture to accelerate Large Language Models (LLMs). In this work, we propos
arXiv:2606.04730v1 Announce Type: new Abstract: With the advent of Large Language Models, single-task and token-based multi-task models have evolved into instruction-based systems that infer task and
arXiv:2606.04847v1 Announce Type: cross Abstract: Native GPU kernel generation turns high-level tensor programs into executable, efficient low-level code. Existing Large Language Models (LLMs) struggl
arXiv:2606.04757v1 Announce Type: cross Abstract: We study decentralized stochastic smooth convex optimization, where M workers minimize an average objective using local stochastic gradients and neigh
arXiv:2606.04067v1 Announce Type: cross Abstract: As LLMs become increasingly woven into everyday workflows, user queries sent to cloud hosted LLMs routinely mix task-essential content with task non-e
arXiv:2606.04562v1 Announce Type: new Abstract: Purpose The WHO's COVID-19 non-pharmaceutical interventions (e.g., lockdowns, vaccinations) effectively curb transmission but impose heavy economic stra
arXiv:2606.04324v1 Announce Type: new Abstract: One of the primary challenges in Bayesian inference on the parameters of a diffusion model from discrete observations is the unavailability of an analyt
arXiv:2506.23546v2 Announce Type: replace-cross Abstract: Fixed points of recurrent neural networks can be leveraged to store and generate information. These fixed points can be captured by the Boltzm
arXiv:2606.04008v1 Announce Type: cross Abstract: Radiated noise in unmanned underwater vehicles (UUVs) is an important indicator for characterizing acoustic signatures and evaluating platform perform
arXiv:2606.04994v1 Announce Type: new Abstract: Accurate computational prediction of T cell receptor (TCR) antigen specificity would transform the study of T cell biology and enable scalable immune en
arXiv:2606.04773v1 Announce Type: cross Abstract: Reliable evaluation of human motion understanding is fundamental to advancing embodied AI, robotics, and animation. However, existing benchmarks suffe
arXiv:2606.04957v1 Announce Type: cross Abstract: System-generated logs underpin security monitoring, yet their rigid template-based format hinders both automated analysis and human comprehension. We
arXiv:2606.04340v1 Announce Type: new Abstract: A sentence like 'The authors that no critics recommended have ever received acknowledgment for a best-selling novel' is sometimes rated as acceptable ev
arXiv:2606.04265v1 Announce Type: cross Abstract: The Schrodinger Bridge Problem constructs a stochastic process that connects an initial distribution to a terminal distribution with minimum energy. T
arXiv:2606.04806v1 Announce Type: cross Abstract: LLMs and agentic systems are increasingly deployed in social environments, making normative competence critical for safe and appropriate behavior. How
arXiv:2606.04402v1 Announce Type: new Abstract: Modern reasoning models can allocate different amounts of test-time computation, such as thinking tokens, model calls, or compute budget, to different t
arXiv:2606.04193v1 Announce Type: cross Abstract: Current AI agent observability is structurally compromised: the entity producing the activity log is the same entity whose activity is being logged. A
arXiv:2606.04028v1 Announce Type: new Abstract: The IEEE P3109 draft standard defines a parameterized family of binary floating-point formats and associated operations, with a focus on facilitating ma
arXiv:2606.04820v1 Announce Type: cross Abstract: CutMix has become the de facto standard mixing augmentation, yet its label assignment rests on a flawed assumption: The area of the pasted patch faith
arXiv:2511.05722v3 Announce Type: replace-cross Abstract: Large language models (LLMs) such as GPT-5 and Gemini 3 have pushed the frontier of automated reasoning and code generation. Yet current bench