Knowledge catalogue
arXiv:2605.15196v1 Announce Type: new Abstract: Video generation powers a vast array of downstream applications. However, while the de facto standard, i.e., latent diffusion models, typically employ h
arXiv:2605.14019v1 Announce Type: cross Abstract: Regret is the cost of uncertainty in algorithmic decision-making. Quantifying regret typically requires computationally expensive simulation via Sampl
arXiv:2603.12554v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has been effective for post-training autoregressive (AR) language models, but extending these methods to diffusion
arXiv:2605.14126v1 Announce Type: cross Abstract: Fast Healthcare Interoperability Resources (FHIR) is the dominant standard for interoperable exchange of healthcare data. In FHIR, electronic health r
arXiv:2605.14366v1 Announce Type: new Abstract: Extending large language models (LLMs) to low-resource languages often incurs an 'alignment tax': improvements in the target language come at the cost o
arXiv:2605.14063v1 Announce Type: new Abstract: Continual test-time adaptation (CTTA) updates a pretrained model online on an unlabeled, non-stationary stream while anchoring it to a frozen source che
arXiv:2605.14563v1 Announce Type: cross Abstract: Automated code documentation is essential for modern software development, providing the contextual grounding that both human developers and coding ag
arXiv:2605.14686v1 Announce Type: new Abstract: Tabular data sharing under privacy constraints is increasingly important for research and collaboration. Synthetic data generators (SDGs) are a promisin
arXiv:2512.12083v3 Announce Type: replace Abstract: Semantic-rich features from Vision Foundation Models (VFMs) have been leveraged to enhance Latent Diffusion Models (LDMs). However, raw VFM features
arXiv:2605.14913v1 Announce Type: new Abstract: Linear attention has emerged as a promising direction for scaling Vision Transformers beyond the quadratic cost of dense self-attention. A prevalent str
arXiv:2603.16039v2 Announce Type: replace-cross Abstract: Recent work has made clear that the residual pathway is not mere optimization plumbing; it is part of the model's representational machinery.
arXiv:2605.14558v1 Announce Type: cross Abstract: Agentic reinforcement learning trains large language models using multi-turn trajectories that interleave long reasoning traces with short environment
arXiv:2605.14075v1 Announce Type: cross Abstract: Large language models (LLMs) have revolutionized natural language processing. Understanding their internal mechanisms is crucial for developing more i
arXiv:2605.13932v1 Announce Type: new Abstract: Robust prediction of molecular properties under extreme out-of-distribution (OOD) scenarios is a pivotal bottleneck in AI-driven drug discovery. Current
arXiv:2512.21651v2 Announce Type: replace Abstract: Large Language Models (LLMs) deliver strong performance across a wide range of NLP tasks, but their massive sizes hinder deployment on resource-cons
arXiv:2605.14145v1 Announce Type: new Abstract: The field of deep visual recognition is undergoing a paradigm shift toward universal representations. The Platonic Representation Hypothesis suggests th
arXiv:2511.13026v3 Announce Type: replace Abstract: Self-reflection mechanisms that rely on purely text-based rethinking processes perform well in most multimodal tasks. However, when directly applied
arXiv:2605.14497v1 Announce Type: cross Abstract: Offline-to-online reinforcement learning harnesses the stability of offline pretraining and the flexibility of online fine-tuning. A key challenge lie
arXiv:2605.14925v1 Announce Type: new Abstract: Drone-view geo-localization aims to match a query drone image, often captured under adverse weather conditions (e.g., rain, snow, fog), against a galler
arXiv:2603.02115v2 Announce Type: replace-cross Abstract: General-purpose robot reward models are typically trained to predict absolute task progress from expert demonstrations, providing only local,
arXiv:2602.05285v2 Announce Type: replace Abstract: A core challenge in structural biophysics is generating biomolecular conformations that are both physically plausible and consistent with experiment
arXiv:2605.14152v1 Announce Type: cross Abstract: Safety evaluations for large language models (LLMs) increasingly target high-stakes National Security and Public Safety (NSPS) risks, yet multilingual
arXiv:2605.15154v1 Announce Type: cross Abstract: Feature attribution analysis is critical for interpreting machine learning models and supporting reliable data-driven decisions. However, feature attr
arXiv:2605.14359v1 Announce Type: cross Abstract: Vector quantization is a fundamental tool for compressing high-dimensional embeddings, yet existing multi-codebook methods rely on static codebooks th
arXiv:2605.14543v1 Announce Type: cross Abstract: Inpatient medication recommendation requires clinicians to repeatedly select specific medications, doses, and routes as a patient's condition evolves.
arXiv:2605.13872v1 Announce Type: cross Abstract: This article introduces S-AI-Recursive, a bio-inspired Sparse Artificial Intelligence architecture in which reasoning is operationalized as a hormonal
arXiv:2408.16307v3 Announce Type: replace-cross Abstract: Automatic controller tuning is attractive for robotics and mechatronic systems whose dynamics are difficult to model accurately, but direct bl
arXiv:2605.14174v1 Announce Type: new Abstract: Safe navigation for mobile robots demands policies that remain reliable under the high-consequence perception uncertainty of cluttered environments. Yet
arXiv:2605.15088v1 Announce Type: new Abstract: We present SAGE3D, a hybrid Transformer-based model for corner detection in airborne LiDAR point clouds. We propose a multi-stage solution built on a hi
arXiv:2605.15178v1 Announce Type: new Abstract: We introduce SANA-WM, an efficient 2.6B-parameter open-source world model natively trained for one-minute generation, synthesizing high-fidelity, 720p,
arXiv:2605.14984v1 Announce Type: cross Abstract: Generating a street-level 3D scene from a single satellite image is a crucial yet challenging task. Current methods present a stark trade-off: geometr
arXiv:2505.09552v3 Announce Type: replace-cross Abstract: Mixed-effects models are widely used to model data with hierarchical grouping structures and high-cardinality categorical predictor variables.
arXiv:2605.14662v1 Announce Type: cross Abstract: The multi-path Traveling Salesman Problem with stochastic travel costs arises in hybrid vehicle routing applications designed for Smart City and City
arXiv:2506.20425v3 Announce Type: replace-cross Abstract: Linear mixed models (LMMs), which incorporate fixed and random effects, are key tools for analyzing heterogeneous data, such as in personalize
arXiv:2605.14567v1 Announce Type: cross Abstract: We propose a simple mechanism by which scaling laws emerge from feature learning in multi-layer networks. We study a high-dimensional hierarchical tar
arXiv:2510.23818v2 Announce Type: replace Abstract: As large language models (LLMs) continue to scale in size, the computational overhead has become a major bottleneck for task-specific fine-tuning. W
arXiv:2605.14399v1 Announce Type: new Abstract: Many multimodal learning tasks require supervision that remains consistent across edits, viewpoints, and scene-level interventions. However, such superv
arXiv:2605.14704v1 Announce Type: cross Abstract: In real-world scenes, target objects may reside in regions that are not visible. While humans can often infer the locations of occluded objects from c
arXiv:2605.14923v1 Announce Type: new Abstract: General scene perception has progressed from object recognition toward open-vocabulary grounding, part localization, and affordance prediction. Yet thes
arXiv:2605.14600v1 Announce Type: new Abstract: Scientific progress depends on sequences of enabling contributions, yet existing AI4Science benchmarks largely focus on citation prediction, literature
arXiv:2507.07776v3 Announce Type: replace Abstract: Unrestricted adversarial attacks aim to fool computer vision models without being constrained by ell_p-norm bounds to remain imperceptible to humans
arXiv:2605.14926v1 Announce Type: new Abstract: Achieving pixel-level accurate segmentation of structural cracks across diverse scenarios remains a formidable challenge. Existing methods face signific
arXiv:2504.09549v3 Announce Type: replace Abstract: Aerial-Ground Person Re-IDentification (AG-ReID) aims to retrieve specific persons across cameras with different viewpoints. Previous works focus on
arXiv:2605.14683v1 Announce Type: new Abstract: High-resolution seafloor mapping necessitates stable and precise positioning for underwater robots. This paper introduces a novel mathematical model for
arXiv:2605.14982v1 Announce Type: cross Abstract: We address the discounted reward setting in reinforcement learning (RL). To mitigate the value approximation challenges in policy gradient methods, ac
arXiv:2604.02482v2 Announce Type: replace Abstract: This paper aims to address the challenge of data generation beyond the training data and proposes a framework for Structural Extrapolated Data GEner
arXiv:2605.14894v1 Announce Type: new Abstract: Recent breakthroughs in video diffusion models have significantly accelerated the development of video editing techniques. However, existing methods oft
arXiv:2605.13862v1 Announce Type: cross Abstract: We present Seed3D 2.0, an advanced 3D content generation system built on Seed3D 1.0, with substantial improvements across generation fidelity, simulat
arXiv:2510.16196v2 Announce Type: replace-cross Abstract: Understanding how the brain encodes visual information is a central challenge in neuroscience and machine learning. A promising approach is to
arXiv:2605.14551v1 Announce Type: new Abstract: Instance normalization (IN) is widely used in non-stationary multivariate time series forecasting to reduce distribution shifts and highlight common pat
arXiv:2605.14746v1 Announce Type: new Abstract: While large language models (LLMs) are trained to align with human values, their generations may still violate safety constraints. A growing line of wor
arXiv:2605.15155v1 Announce Type: cross Abstract: Reinforcement learning (RL) has emerged as a central paradigm for post-training LLM agents, yet its trajectory-level reward signal provides only coars
arXiv:2605.14037v1 Announce Type: cross Abstract: Under modern test-time compute and agentic paradigms, language models process ever-longer sequences. Efficient text generation with transformer archit
arXiv:2605.14228v1 Announce Type: cross Abstract: Background: Abilities for effective self-regulated learning (SRL) are critical for lifelong learning, particularly during adolescence when these skill
arXiv:2605.14318v1 Announce Type: new Abstract: Predictive maintenance in complex systems is often complicated by the heterogeneity and redundancy of monitored variables,which can obscure fault-releva
arXiv:2605.15026v1 Announce Type: cross Abstract: Online OS tuning can improve long-running services, but existing controllers are poorly matched to live hosts. They treat scheduler, power, memory, an
arXiv:2605.15050v1 Announce Type: new Abstract: Recently, deep generative models have been used for posterior inference in inverse problems, including high-stakes applications in medical imaging and s
arXiv:2408.11186v4 Announce Type: replace-cross Abstract: We study sequential multi-issue trading between two greedily rational agents who exchange resources from a finite set of categories. Each agen
arXiv:2510.18766v2 Announce Type: replace Abstract: A future lunar habitat, as part of the Artemis program, will require a significant amount of logistics infrastructure. Cargo that is transported to
arXiv:2605.14033v1 Announce Type: new Abstract: Scientific theory shift in AI agents requires more than fitting equations to data. An artificial scientific agent must detect whether an existing repres