Knowledge catalogue
arXiv:2609.34083v1 Announce Type: new Abstract: Recommendation models with sparse embeddings and a shared consumer often exhibit the one-epoch phenomenon: a second epoch lowers training loss while sha
arXiv:2609.33595v1 Announce Type: cross Abstract: Joint-embedding world models enable visual planning by learning action-conditioned dynamics in latent space. Yet they are commonly trained for one-ste
arXiv:2609.31812v1 Announce Type: cross Abstract: Pruning reduces the number of weights in magnetic resonance imaging (MRI) reconstruction networks. Equal sparsity, however, can retain weights with di
arXiv:2609.32841v1 Announce Type: new Abstract: One-step text-guided diffusion editing is efficient but prone to spatially misallocated updates that distort the edited object and alter the background.
arXiv:2609.31667v1 Announce Type: new Abstract: Accurate bus Estimated Time of Arrival (ETA) prediction is vital for urban mobility, passenger satisfaction, and transit efficiency, yet existing models
arXiv:2609.35521v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) expose features that help us understand and steer language models, but faithful reconstruction does not guarantee informati
arXiv:2609.34857v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) trains reasoning models to produce correct answers, but does not ensure that their stated confiden
arXiv:2609.31740v1 Announce Type: new Abstract: Accurate coronary artery segmentation on coronary computed tomography angiography (CCTA) is essential for diagnosing coronary artery disease. Deep netwo
arXiv:2505.11294v3 Announce Type: replace Abstract: Hierarchical Gaussian Process (H-GP) models divide problems into different subtasks, allowing different components to address each part, making them
arXiv:2609.35407v1 Announce Type: new Abstract: Text-to-motion generation and motion-to-text captioning are two fundamental tasks in human motion modeling, both grounded in the same underlying motion-
arXiv:2511.12257v2 Announce Type: replace-cross Abstract: This paper proposes a novel Bayesian framework for solving Poisson inverse problems by devising a Monte Carlo sampling algorithm which account
arXiv:2609.31978v1 Announce Type: cross Abstract: Generating independent, equilibrium samples of molecular systems at scale remains a central obstacle in computational statistical mechanics. Boltzmann
arXiv:2609.33052v1 Announce Type: cross Abstract: Financial question answering often requires precise numerical extraction, unit handling, and arithmetic over tables and text, but applying expensive v
arXiv:2609.35436v1 Announce Type: cross Abstract: Recently, deep neural networks on manifold-valued representations have garnered significant attention across various machine learning applications. On
arXiv:2609.33037v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) lets language models answer questions more accurately by consulting relevant documents. Many valuable collections
arXiv:2609.35139v1 Announce Type: new Abstract: Multi-document retrieval-augmented generation (RAG) requires a language model to process multiple retrieved text chunks before answering a question. Pre
arXiv:2609.33342v1 Announce Type: new Abstract: Molecular property prediction is central to drug development and materials discovery, but experiments are costly and labeled data are scarce. Context-aw
arXiv:2609.33549v1 Announce Type: cross Abstract: Automatic relevance determination (ARD), the default tool for variable selection in Gaussian-process (GP) regression, ranks inputs by inverse lengthsc
arXiv:2609.33143v1 Announce Type: cross Abstract: Quarter-ahead revenue forecasting requires company-scale numerical accuracy, strict temporal validity, and company-specific interpretation of narrativ
arXiv:2609.34501v1 Announce Type: new Abstract: Adversarial attacks against object detectors are traditionally studied from an optimization perspective, where attack difficulty is regarded as an outco
arXiv:2609.33178v1 Announce Type: new Abstract: Molecularly guided pathology foundation models (PFMs) exploit transcriptomic or proteomic information to enrich whole-slide image (WSI) representations,
arXiv:2609.31706v1 Announce Type: new Abstract: Visual Language Models (VLMs) have achieved remarkable success across diverse tasks, yet they struggle with high-resolution inputs where critical inform
arXiv:2609.34191v1 Announce Type: new Abstract: Transactional basket data can reveal associations among items, but observed co-occurrence conflates item-specific relations with basket-size structure a
arXiv:2609.35621v1 Announce Type: new Abstract: Serving long documents to a Large Language Model (LLM) repeatedly is expensive: computations grow with context length, and the memory footprint of the k
arXiv:2609.34144v1 Announce Type: new Abstract: Interactive world models must respond quickly to controls while preserving scene consistency. Existing acceleration methods can miss heterogeneous contr
arXiv:2609.34302v1 Announce Type: new Abstract: Multi-camera pedestrian localization is useful for wide-area monitoring in public and commercial spaces. However, deploying these systems often requires
arXiv:2412.12906v3 Announce Type: replace Abstract: Recently, generalizable feed-forward methods based on 3D Gaussian Splatting have gained significant attention for their potential to reconstruct 3D
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:2609.33003v1 Announce Type: new Abstract: Vision-language verifiers and routers must distinguish errors repairable by more reasoning from those caused by visual evidence never reaching the langu
arXiv:2609.32247v1 Announce Type: cross Abstract: Observationally equivalent causal models can still disagree about what happens under intervention, because interventions create inputs that never occu
arXiv:2609.32559v1 Announce Type: new Abstract: Accurate classification of anterior segment diseases is crucial for ophthalmic screening and diagnosis. However, slit-lamp image analysis remains challe
arXiv:2609.33496v1 Announce Type: cross Abstract: Post-training quantization (PTQ) is the standard way to run modern diffusion models on memory-constrained accelerators, yet every existing diffusion P
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:2609.34628v1 Announce Type: cross Abstract: Neural network verification (NNV) formally verifies that a network satisfies a specified property for all inputs within a defined region. Modern NNV t
arXiv:2609.31702v1 Announce Type: new Abstract: Reliable breast ultrasound lesion segmentation requires accurate boundaries and evaluation that prevents patients or duplicate images from crossing data
arXiv:2609.33834v1 Announce Type: new Abstract: Diffusion models are now widely used in Bayesian inverse problems in imaging as priors, where latent diffusion models are often used for larger scale pr
arXiv:2607.17568v2 Announce Type: replace Abstract: Resource-constrained deployment requires sustaining large language model (LLM) capabilities as models scale under fixed memory and computation budge
arXiv:2606.18286v2 Announce Type: replace Abstract: Supervised fine-tuning of code LLMs typically applies uniform cross-entropy loss to all response tokens, implicitly assuming that every token provid
arXiv:2503.19225v4 Announce Type: replace Abstract: We introduce CoinFT, a capacitive 6-axis force/torque (F/T) sensor that is compact, light, low-cost, and robust with an average root-mean-squared er
arXiv:2609.33754v1 Announce Type: new Abstract: Organizations seek analytical value from AI, yet relevant data are often fragmented across organizations and constrained by privacy. This is acute in fi
arXiv:2609.32566v1 Announce Type: new Abstract: Intelligence is defined in many ways. One of these definitions defines intelligence as the pursuit of learnable novelty. However, learnable novelty can
arXiv:2609.33580v1 Announce Type: new Abstract: Learning-augmented algorithms for metrical task systems (MTS) can exploit predictions of canonical dual values, but existing formulations typically requ
arXiv:2609.32671v1 Announce Type: cross Abstract: Cross-modal retrieval has been advanced by vision-language pre-trained models that encode images and texts into a shared dense embedding space. While
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:2604.18441v2 Announce Type: replace-cross Abstract: We introduce a robust conformal prediction method based on the half-mass radius P(x):=inf{r>0:P(B(x,r))>1/2}. Its empirical version is exactly
Congrats to @hardmaru @risi1979 @yujin_tang and Risto! Neuroevolution is where I got started in AI, and neuroevolution is still a playground for original ideas with neural networks. This book is worth
arXiv:2609.35083v1 Announce Type: cross Abstract: Biological machine learning was long bottlenecked by the ability to synthesize designed DNA. Variational synthesis models control chemical reactions t
arXiv:2604.01601v2 Announce Type: replace Abstract: In-context fine-tuning (IC-Train), training an LLM with labeled examples in-context, is increasingly used in place of standard fine-tuning for domai
arXiv:2609.32038v1 Announce Type: new Abstract: Extended Reality (XR) users do not directly perceive the output of a rendering engine. Instead, rendered images pass through a post-processing pipeline
arXiv:2609.32393v1 Announce Type: cross Abstract: Plug-and-Play (PnP) methods solve imaging inverse problems by incorporating deep denoisers into iterative optimization algorithms. Although practical
arXiv:2601.21338v2 Announce Type: replace Abstract: Hyperspectral image super-resolution (HSI-SR) aims to recover spatial detail while preserving the spectral shape on which quantitative analysis reli
arXiv:2609.33047v1 Announce Type: new Abstract: Matrix optimizers such as Muon transform each momentum matrix through an approximate orthogonalization, typically implemented by a small number of Newto
arXiv:2609.32870v1 Announce Type: cross Abstract: Self-play proposer--solver methods improve reasoning by generating tasks and learning from verified solutions. However, for evidence-identifiable task
arXiv:2609.32932v1 Announce Type: new Abstract: Finite computational resources force a tradeoff between automatic System 1 processes and costly System 2 thinking. Large language models (LLMs) can spen
arXiv:2609.31658v1 Announce Type: new Abstract: Ship classification from Synthetic Aperture Radar (SAR) imagery is a critical computer vision task, yet the robustness of models under deployment shifts
arXiv:2609.31785v1 Announce Type: cross Abstract: Audio-only pedestrian detection is attractive for urban sensing but limited by weak acoustic cues. An appealing strategy is cross-modal knowledge dist
arXiv:2601.07519v2 Announce Type: replace-cross Abstract: Unpredictable fetal motion during MRI scans can result in oblique 2D slices that are difficult to interpret and often leads to prolonged scan
arXiv:2609.33110v1 Announce Type: new Abstract: Joint-Embedding Predictive Architectures (JEPAs) provide a framework for learning compact representations without directly reconstructing high-dimension
arXiv:2609.33879v1 Announce Type: cross Abstract: Neural surrogates can accelerate large-scale optimization by replacing expensive or intractable model components with efficient learned approximations
arXiv:2609.33021v1 Announce Type: cross Abstract: We study decentralized optimization with cross-coupled mixed affine constraints, where local and shared variables interact through two affine channels