Knowledge catalogue
arXiv:2609.08961v1 Announce Type: cross Abstract: For a finite set O of Boolean functions, we consider the class of propositional formulas built using the functions in O as connectives. We determine,
arXiv:2609.08333v1 Announce Type: cross Abstract: In molecular discovery, molecule size is coupled to composition, structure, and other target properties. Yet most 3D generators require molecule size
arXiv:2609.08032v1 Announce Type: new Abstract: We introduce FlexMoGen, a novel framework for flexible human motion synthesis conditioned on both natural language descriptions and motion style referen
arXiv:2609.08620v1 Announce Type: cross Abstract: We address the challenge of scalable uncertainty quantification in large-scale scientific applications, where complex state-of-the-art machine learnin
arXiv:2510.01912v2 Announce Type: replace Abstract: Reconstructing a three-dimensional hyperspectral cube from a two-dimensional compressed measurement is a severely ill-posed inverse problem. Existin
arXiv:2609.07137v1 Announce Type: new Abstract: Recent image-to-3D generation models built on flow-matching diffusion Transformers (DiT) can produce high-fidelity meshes, yet their post-training strat
arXiv:2609.06173v1 Announce Type: new Abstract: Post-deployment drift poses a critical risk to algorithmic accountability, particularly when ground truth labels are delayed and performance degradation
arXiv:2609.08743v1 Announce Type: new Abstract: Object-centric manipulation policies improve generalization by modeling object motion instead of directly predicting robot actions. However, existing me
arXiv:2609.07617v1 Announce Type: new Abstract: With the rise of live sports betting in recent years, tennis forecasting has expanded from pre-match prediction to models that update win probabilities
arXiv:2507.23768v2 Announce Type: replace-cross Abstract: Existing methods for transfer learning struggle to deal with situations where the source datasets are limited and not guaranteed to be well-al
arXiv:2609.06826v1 Announce Type: cross Abstract: This paper examines the theory of Invariant Structural Learning (ISL), which proposes a non-optimization approach to concept formation. Learning is in
arXiv:2609.07853v1 Announce Type: new Abstract: Semantic and goal-oriented communication is increasingly studied for 6G, but generalization beyond seen data remains a key weakness under tight rate bud
arXiv:2609.08305v1 Announce Type: new Abstract: Automating filament tracing in Cryo-Electron Microscopy (Cryo-EM) is essential for 3D helical reconstruction but challenged by intersecting topologies a
arXiv:2609.08886v1 Announce Type: new Abstract: Language-guided robots need persistent scene memories to follow instructions, revisit objects, and resolve references to objects encountered over time.
arXiv:2609.05912v1 Announce Type: new Abstract: Single image super-resolution aims to reconstruct high-resolution images from low-resolution inputs. This paper proposes FreeTransformSR, a novel lightw
arXiv:2609.07048v1 Announce Type: new Abstract: Vision-language models (VLMs) have recently shown excellent progress in open-ended image-to-text generation. However, their multimodal nature makes them
arXiv:2609.08391v1 Announce Type: new Abstract: Remote sensing multimodal large language models (RS-MLLMs) have advanced scene understanding and visual question answering over satellite imagery, yet l
arXiv:2609.07346v1 Announce Type: new Abstract: Radiance field representations such as 3D Gaussian Splatting (3DGS) enable high-quality novel view synthesis but can introduce complex, view-dependent a
arXiv:2609.06208v1 Announce Type: new Abstract: Understanding human attention is fundamental for scene interpretation, yet existing approaches often rely on heavily trained models that lack interpreta
arXiv:2609.08316v1 Announce Type: new Abstract: Multi-modal large language models (MLLMs) have demonstrated significant potential in image quality assessment (IQA) by bridging visual perception with d
arXiv:2601.18524v2 Announce Type: replace Abstract: Accurate prediction of nuclear magnetic resonance (NMR) chemical shifts is fundamental to spectral analysis and molecular structure elucidation, yet
arXiv:2609.07111v1 Announce Type: new Abstract: Quadruped robot locomotion policies are often trained using reinforcement learning, which in turn relies heavily on hand-crafted reward functions. Desig
arXiv:2609.05905v1 Announce Type: cross Abstract: LLM agents are increasingly used for live forecasting, where they retrieve up-to-date information and produce estimates for unresolved future events.
arXiv:2506.12045v2 Announce Type: replace Abstract: Accurate reconstruction of latent environmental fields from sparse, indirect observations is a fundamental challenge across scientific domains, from
arXiv:2609.06912v1 Announce Type: new Abstract: Tabular foundation models (TFMs) are commonly pretrained on large collections of procedurally generated synthetic tasks, yet it remains unclear how well
arXiv:2609.08636v1 Announce Type: new Abstract: Egocentric 4D interaction forecasting aims to anticipate both where future interactions will occur in 3D and how the human body will move to realize the
arXiv:2609.06660v1 Announce Type: new Abstract: Accurate aerodynamic prediction is critical for designing fuel-efficient and safe transportation systems such as aircraft and automobiles, yet tradition
arXiv:2609.06017v1 Announce Type: new Abstract: The appearance of a smoky scene is shaped by two processes that a camera records together: the participating medium alters scene radiance in a view-depe
arXiv:2609.05662v1 Announce Type: new Abstract: Full-page end-to-end Optical Music Recognition seeks to transcribe entire music pages directly into symbolic notation, avoiding the limitations of tradi
arXiv:2609.05846v1 Announce Type: cross Abstract: In function-on-function regression, the coefficient surface eta(s,t) may exhibit complex support structure---from localized patches to global patterns
arXiv:2609.07497v1 Announce Type: new Abstract: Existing SLAM systems lack modeling of the functional relations required for fine-grained robotic interaction. Functional 3D scene graphs can represent
arXiv:2603.05567v2 Announce Type: replace Abstract: Dual-target structure-based drug design aims to generate a single ligand together with two pocket-specific binding poses, each compatible with a cor
arXiv:2509.23552v2 Announce Type: replace Abstract: Antimicrobial Resistance (AMR) is a rapidly escalating global health crisis. While genomic sequencing enables rapid prediction of resistance phenoty
arXiv:2609.08385v1 Announce Type: new Abstract: Floorplans are compact, appearance-invariant maps ideal for indoor localization, yet existing methods rely on depth networks that are brittle in clutter
arXiv:2609.06619v1 Announce Type: new Abstract: Western blot (WB) images are widely used as key evidence in biomedical research. Recent scientific misconduct cases reveal that WB imagery is increasing
arXiv:2609.05744v1 Announce Type: new Abstract: Reconstructing continuous terrain manifolds from massive, unstructured airborne LiDAR point clouds remains challenging in complex Wildland-Urban Interfa
arXiv:2609.05588v1 Announce Type: cross Abstract: World-action models (WAM) predict future states to guide robot actions, enabling learning from both action-free video and action-labeled interaction.
arXiv:2609.06823v1 Announce Type: new Abstract: The next generation of artificial intelligence systems will likely be natively temporal and multimodal in both inputs and outputs: able to converse, per
arXiv:2609.07231v1 Announce Type: new Abstract: Estimating the 6D pose of textureless objects without prior CAD models remains a critical challenge due to the lack of appearance features. While recent
arXiv:2511.09801v3 Announce Type: replace-cross Abstract: This work extends the recently introduced Alpha-Procrustes family of Riemannian metrics for symmetric positive definite (SPD) matrices by inco
arXiv:2609.05822v1 Announce Type: new Abstract: Deploying advanced HVAC (Heating, Ventilation and Air Conditioning) controllers at scale remains difficult because performance often depends on accurate
arXiv:2603.20589v2 Announce Type: replace Abstract: Generating data from discrete distributions is important for a number of application domains including text, tabular data, and genomic data. Several
arXiv:2609.07511v1 Announce Type: cross Abstract: Autonomous driving relies on High Definition (HD) maps for safe navigation. Traditional HD maps construction is costly in hardware, data and human res
arXiv:2609.05902v1 Announce Type: new Abstract: Recent image generation systems increasingly combine multimodal understanding, reasoning, and synthesis, suggesting that they may do more than render pl
arXiv:2609.06628v1 Announce Type: new Abstract: Object-centric video models represent scenes with slots, yet exposed geometry can vary in meaning with appearance. In Invariant Slot Attention (ISA), ex
arXiv:2609.05761v1 Announce Type: new Abstract: Visual geolocation benchmarks typically ask a model where an image was captured without accounting for the location context that users often provide. We
arXiv:2609.08153v1 Announce Type: cross Abstract: Generative diffusion models have emerged as a class of powerful techniques for various imaging applications, including but not limited to synthesis, r
arXiv:2609.08379v1 Announce Type: new Abstract: Accurate diagnostic and risk-prediction models are important for supporting clinical decision-making during infectious disease outbreaks. However, priva
arXiv:2603.25565v2 Announce Type: replace Abstract: Current Large Multimodal Models (LMMs) in Earth observation are predominantly evaluated on planar optical tasks and often neglect the vertical dimen
arXiv:2609.06331v1 Announce Type: new Abstract: Real-time control often sits between two limiting regimes. Predictive optimization and model-based control are powerful when dynamics, parameters, objec
arXiv:2609.08354v1 Announce Type: new Abstract: Several geometry-aware approaches to low-rank adaptation have emerged for parameter-efficient fine-tuning of large pre-trained models. These methods aim
arXiv:2609.06004v1 Announce Type: new Abstract: Quantitative spatial reasoning in visual-language models (VLMs) aims to infer spatial distances and directional relationships among objects in 3D space
arXiv:2609.05927v1 Announce Type: new Abstract: Robot manipulation foundation models require scalable evaluation and data generation across diverse scenarios, with simulation providing an environment
arXiv:2609.07006v1 Announce Type: new Abstract: Compared with relying solely on initial observations and language instructions, predicting goal images with generative models as high-level visual guida
arXiv:2601.09233v3 Announce Type: replace Abstract: The prevailing post-training paradigm for Large Reasoning Models (LRMs)---Supervised Fine-Tuning (SFT) followed by Reinforcement Learning (RL)-suffe
arXiv:2609.05994v1 Announce Type: new Abstract: Humanoid loco-manipulation requires accurate whole-body motion tracking in the world frame for physical interaction. While local references preserve mot
arXiv:2609.06256v1 Announce Type: new Abstract: Vision-language-action (VLA) models have shown promising generalization for language-conditioned robot manipulation, but deploying them in unstructured
arXiv:2609.09082v1 Announce Type: new Abstract: Open vocabulary 3D semantic segmentation methods typically lift CLIP features into 3D. This embeds points in a joint vision-language space known to beha
arXiv:2609.08607v1 Announce Type: new Abstract: We present GOLF, the first-place solution to the SHOW3D Interaction Field Estimation Challenge at HANDS@ECCV 2026. Given synchronized egocentric stereo
arXiv:2609.08136v1 Announce Type: new Abstract: This paper introduces rlaopt, a PyTorch-based package for large-scale optimization and scientific computing using randomized numerical linear algebra (R