Knowledge catalogue
arXiv:2607.09185v2 Announce Type: replace Abstract: Action-conditioned world models (ACWMs) aim to simulate future observations conditioned on embodied actions. However, learning controllable ACWMs re
arXiv:2609.32427v1 Announce Type: new Abstract: CityToolVQA addresses the weak performance of Vision-Language Models (VLMs) on quantitative tasks in urban low-altitude visual question answering. We di
arXiv:2609.32767v1 Announce Type: new Abstract: Stacking and palletising demand precise placement: error left in one layer is inherited by the next, and a flat pad offers no feature to funnel a wrong
arXiv:2609.35476v1 Announce Type: new Abstract: Embodied co-painting requires a robot to repeatedly update a shared physical canvas while human intent evolves over interaction. Existing reference-driv
arXiv:2609.33671v1 Announce Type: cross Abstract: When must a foundation-model safety gateway generate tokens, and when should it directly output a calibrated decision? We study calibrated standalone
arXiv:2609.34887v1 Announce Type: cross Abstract: Tabular foundation models (TFMs) provide predictive distributions for regression, but their prediction regions can exhibit undercoverage or overcovera
arXiv:2609.34807v1 Announce Type: new Abstract: Spatially conditioned diffusion models can embed words and contours in natural-looking images, but vision-language models (VLMs) may fail to recognize t
arXiv:2609.32513v1 Announce Type: new Abstract: Handwritten mathematical expression recognition (HMER) refers to the task of recognizing and converting handwritten mathematics into a parsable markup l
arXiv:2609.34965v1 Announce Type: new Abstract: Many physiological time series, such as cardiac and brain recordings, exhibit cyclostationarity: their statistics vary periodically with an underlying c
arXiv:2609.33177v1 Announce Type: cross Abstract: World-Action Models (WAMs) offer visual foresight for robotic manipulation, but pixel-space models repeatedly reconstruct entire future scenes, incurr
arXiv:2609.35066v1 Announce Type: new Abstract: Most neural constructive solvers for the vehicle routing problem (VRP) use route-by-route construction, extending one route until completion before star
arXiv:2609.34286v1 Announce Type: new Abstract: World models for manipulation are typically trained from video, yet the events that determine how manipulation unfolds, such as making and releasing con
arXiv:2609.32363v1 Announce Type: new Abstract: Probabilistic time series forecasting requires modeling and predicting complex and time-varying distributions. Recently, Denoising Diffusion Probabilist
arXiv:2609.33803v1 Announce Type: new Abstract: Reward models underpin the alignment of large language models, yet the dominant designs reduce each prompt--response pair to a point estimate or to a di
arXiv:2609.34408v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs) have shown strong promise for multimodal reasoning, yet often struggle with tasks requiring concepts beyond what is
arXiv:2609.35289v1 Announce Type: new Abstract: Denoising Diffusion Probabilistic Models have shown remarkable performance in unconditional image generation. In order to generate images with desired s
arXiv:2609.32215v1 Announce Type: new Abstract: Transformers have redefined sequential recommendation by effectively modeling dynamic user behaviors and long-range dependencies. However, they remain i
arXiv:2609.35634v1 Announce Type: new Abstract: Electrocardiogram (ECG) recordings are corrupted by non-stationary noise sources that degrade diagnostic reliability, particularly in ambulatory and lon
arXiv:2609.32453v1 Announce Type: new Abstract: Robotic manipulation is inherently history-dependent, yet most pretrained robotic policies condition on only the current observation or a short temporal
arXiv:2512.09814v2 Announce Type: replace Abstract: Personalized Text-to-Image (PT2I) generation aims to produce customized images based on reference images. A prominent interest pertains to the integ
arXiv:2609.34346v1 Announce Type: new Abstract: Temporally dense optical flow is essential for dynamic perception in immersive VR/AR systems, where rapid head, hand, and object motion must be continuo
arXiv:2609.35570v1 Announce Type: cross Abstract: Vision-language navigation (VLN) models perform well but target compute-rich platforms, limiting deployment on memory- and power-constrained robotic e
arXiv:2609.31632v1 Announce Type: new Abstract: Electroencephalography (EEG) analysis is evolving from short-segment classification toward long-horizon interpretation that demands iterative evidence a
arXiv:2609.35047v1 Announce Type: cross Abstract: A robot should be able to learn through experiments how unfamiliar objects behave and interact, then plan with that knowledge. It need not start from
arXiv:2609.33758v1 Announce Type: new Abstract: Reliable document digitization in uncontrolled capture settings is challenging because real images exhibit multiple interacting degradations rather than
arXiv:2601.22944v3 Announce Type: replace Abstract: Out-of-distribution generalization becomes challenging when spurious correlations vary across environments while difficult or underrepresented sampl
arXiv:2609.34018v1 Announce Type: cross Abstract: Simulation-trained manipulation policies can exploit privileged state information to learn effective contact-rich behaviours, but deployment requires
arXiv:2609.35269v1 Announce Type: cross Abstract: The rising number of concept unlearning techniques for text-to-image (T2I) diffusion models has produced a fragmented evaluation landscape. Methods ar
arXiv:2509.15880v2 Announce Type: replace Abstract: Geometry-grounded vision models, such as VGGT, have emerged as robust visual encoders, providing essential geometric priors for robotic manipulation
arXiv:2609.31915v1 Announce Type: new Abstract: Hybrid quantum methods have received limited study for resource-constrained facial biometrics. We present a hybrid quantum-classical facial recognition
arXiv:2609.32479v1 Announce Type: new Abstract: We propose a training recipe that treats charged-particle trajectory parameter regression on high-energy physics detector data as a sequence-modeling ta
arXiv:2603.20212v2 Announce Type: replace-cross Abstract: Reward models (RMs) are critical for aligning Large Language Models via Reinforcement Learning from Human Feedback (RLHF). While Generative Re
arXiv:2609.32353v1 Announce Type: new Abstract: Visual token reduction is an effective way to accelerate multimodal large language models (MLLMs), but performance deteriorates rapidly under extremely
arXiv:2609.33582v1 Announce Type: new Abstract: Recent real-world image super-resolution (SR) methods often adapt text-to-image (T2I) backbones with ControlNet-style branches or spatial conditioning t
arXiv:2609.33167v1 Announce Type: new Abstract: We present FloodDiffusion 2 (FD2), an efficient and controllable framework that builds upon FloodDiffusion (FD1), a state-of-the-art streaming motion ge
arXiv:2609.35673v1 Announce Type: new Abstract: Tool-based image editing (image retouching) is commonly formulated with autoregressive multimodal large language models (MLLMs) that sequentially genera
arXiv:2609.33353v1 Announce Type: new Abstract: Multi-class anomaly classification in industrial vision remains challenging due to noisy/incomplete anomaly representations and the unknown number of an
arXiv:2602.00974v2 Announce Type: replace Abstract: Label-supervised manifold alignment bridges the gap between unsupervised and correspondence-based paradigms by leveraging shared label information t
arXiv:2609.34358v1 Announce Type: new Abstract: In agentic AI systems, frozen foundation models are increasingly deployed as closed-weight API endpoints, making downstream adaptation possible only thr
Claude.ai achieved roughly a three‑fold speed increase in just two weeks by implementing targeted engineering optimisations. The blog post on claude.dev explains the key changes—such as streamlined co
arXiv:2609.32273v1 Announce Type: new Abstract: Accurate delineation of whole tumor (WT), tumor core (TC), and enhancing tumor (ET) from multimodal magnetic resonance imaging remains challenging under
arXiv:2609.33981v1 Announce Type: new Abstract: Exploration--exploitation is a central trade-off in bandit learning. While classical algorithms such as upper confidence bound methods and Thompson Samp
arXiv:2609.34362v1 Announce Type: new Abstract: World Action Models (WAMs) augment robot action generation with future visual supervision. Existing WAMs commonly fuse main and wrist observations into
arXiv:2609.32510v1 Announce Type: new Abstract: Cloud removal methods are typically specialized to individual datasets and input configurations, limiting reuse across sensors, spectral bands, and obse
arXiv:2412.08911v4 Announce Type: replace Abstract: Multi-objective learning endeavors to concurrently optimize multiple objectives using a single model, aiming to achieve high and balanced performanc
arXiv:2609.33400v1 Announce Type: new Abstract: Action boundary detection partitions untrimmed video into intervals without assigning action classes. We present a boundary-detection adapter operating
arXiv:2609.35267v1 Announce Type: new Abstract: Large-scale 3D multi-agent path finding becomes increasingly difficult under dense traffic. Priority Inheritance with Backtracking (PIBT) scales well, b
arXiv:2609.35038v1 Announce Type: cross Abstract: Federated Bayesian Optimization (FBO) enables distributed agents to collaboratively optimize expensive black-box objectives without sharing raw local
arXiv:2609.35587v1 Announce Type: new Abstract: GPU libraries such as CUTLASS expose tens of thousands of semantically equivalent kernels for a single operation, making exhaustive autotuning expensive
arXiv:2609.31765v1 Announce Type: new Abstract: Accurate and rapid prediction of the aerodynamic drag coefficient (C_D) is essential for vehicle design, particularly during early-stage styling iterati
arXiv:2609.35709v1 Announce Type: new Abstract: Vision-language-action (VLA) models using discrete action tokens have proven effective for controling robotic arms on manipulation tasks. For a humanoid
arXiv:2609.33523v1 Announce Type: new Abstract: We present In-Token Learning, an image restoration framework that adapts a pretrained diffusion transformer using conditional rectified flow matching. C
arXiv:2609.31997v1 Announce Type: new Abstract: Decoding imagined speech is an important goal for brain-computer interfaces but remains challenging due to weak neural responses, low signal-to-noise ra
arXiv:2609.33874v1 Announce Type: new Abstract: JET uses pretrained language and vision-language models to select among a finite set of answers without additional training. It evaluates candidate like
arXiv:2609.34972v1 Announce Type: new Abstract: Long visual token sequences often account for a substantial fraction of the computational overhead in multimodal large language models~(MLLMs). Existing
arXiv:2609.34045v1 Announce Type: cross Abstract: Between them, the members of a research group or a circle of friends own several consumer computers, none large enough to run a capable large language
arXiv:2609.32182v1 Announce Type: new Abstract: Vision-language models are increasingly used to understand long videos and continuous streams. However, dense visual tokens accumulate with video durati
arXiv:2609.34507v1 Announce Type: cross Abstract: Financial candlestick forecasting is fundamental to quantitative investment, yet it remains exceptionally challenging due to extremely low signal-to-n
arXiv:2609.31888v1 Announce Type: cross Abstract: X-ray diffraction (XRD) is a experimental technique for determining the phase composition and structure of crystalline materials. However, interpretin
arXiv:2609.34060v1 Announce Type: new Abstract: Prompt-specialized multi-agent systems enable multiple agents to share a model while performing complementary roles to solve complex tasks. However, age