Knowledge catalogue
arXiv:2608.11634v1 Announce Type: new Abstract: Dropout regularization is commonly used to reduce overfitting by removing parts of a neural network during training. For Convolutional Neural Networks (
arXiv:2608.11810v1 Announce Type: new Abstract: Air traffic controllers perceive traffic complexity through the radar display, suggesting that a computer vision model operating on the same imagery may
arXiv:2608.11555v1 Announce Type: new Abstract: Practitioners enrich customer-return models with ever more signals (lifetime value, category, recency/frequency, calendar, geography), and the temporal-
arXiv:2608.11716v1 Announce Type: new Abstract: Chain of Thought (CoT) lifts the expressive ceiling of bounded-depth Transformers, with characterizations tying the number of CoT steps to circuit compl
arXiv:2608.11283v1 Announce Type: cross Abstract: Computation-ready metal-organic framework (MOF) databases are essential for high-throughput screening, yet many reported crystal structures remain che
arXiv:2608.11994v1 Announce Type: new Abstract: We propose claim-level falsification as a principle for test-time scaling and instantiate it through Claim-Level Reliability Assessment (CLR), a trainin
arXiv:2608.11287v1 Announce Type: cross Abstract: Long-tailed classification poses a reliability challenge because models trained on imbalanced data are unevenly reliable across frequent and underrepr
arXiv:2505.23696v2 Announce Type: replace Abstract: Solving systems of polynomial equations, particularly those with finitely many solutions, is a crucial challenge across many scientific fields. Trad
arXiv:2608.12158v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have made rapid progress, yet they still exhibit object hallucination, generating plausible but incorrect descr
arXiv:2608.11235v1 Announce Type: new Abstract: Diffusion language models (DLMs) update many tokens in parallel, yet practical decoders often use a fixed denoising horizon. Many predictions stabilize
arXiv:2608.11269v1 Announce Type: cross Abstract: Omics datasets, particularly single-cell RNA sequencing data, are high-dimensional, sparse, noisy, and dominated by zero values, making faithful low-d
arXiv:2608.11267v1 Announce Type: cross Abstract: Distance-based reliability estimation assumes that a representation's geometry reflects its trustworthiness, yet this assumption is rarely tested unde
arXiv:2512.14426v2 Announce Type: replace-cross Abstract: Extended object tracking involves estimating both the physical extent and kinematic parameters of a target object, where typically multiple me
arXiv:2608.11627v1 Announce Type: cross Abstract: The Relative Transfer Matrix (ReTM), recently introduced as a generalization of the relative transfer function for multiple receivers and sources, sho
arXiv:2608.11576v1 Announce Type: cross Abstract: Video-to-music generation has drawn growing interest for its role in conveying the emotion of visual media, including film. Progress in the field, how
arXiv:2608.11612v1 Announce Type: cross Abstract: The Muon optimizer incurs a significant overhead cost due to its cubic-time Newton-Schulz orthogonalization step. When weights are sharded, communicat
arXiv:2608.12043v1 Announce Type: cross Abstract: Acceleration for deterministic root-finding problems has been extensively studied in recent years; specifically, the anchor-based, or Halpern-type met
arXiv:2608.11513v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly integrated into software engineering workflows, helping developers write, debug, test, and maintain code
arXiv:2608.12125v1 Announce Type: cross Abstract: As LLM-based agents with user-instructed goals are becoming widely deployed, they increasingly encounter each other in strategic interactions, and fac
arXiv:2608.11441v1 Announce Type: new Abstract: Low-resource automatic speech recognition (ASR) commonly relies on cross-lingual transfer, where models are adapted from higher-resource donor languages
arXiv:2608.12064v1 Announce Type: new Abstract: We invert the typical formulation of sketch generation: instead of drawing strokes in order, we predict a 2D field that defines the order in which strok
arXiv:2608.11473v1 Announce Type: new Abstract: Inferring the ground-truth from noisy crowdsourced labels is an important theoretical and practical problem. Neural network-based methods offer an alter
arXiv:2608.12271v1 Announce Type: new Abstract: Global weather reanalyses and forecasts resolve the evolving atmospheric state on coarse grids, but site-specific applications require predictions at ar
arXiv:2608.11629v1 Announce Type: new Abstract: Audio transcription is a critical bottleneck in language documentation. While multilingual Automatic Speech Recognition (ASR) models like Whisper offer
arXiv:2508.08467v2 Announce Type: replace-cross Abstract: Despite their potential to enhance children's learning experiences, AI-enabled AR technologies are predominantly used in ways that position ch
arXiv:2602.24129v3 Announce Type: replace-cross Abstract: Large-scale homogeneous detectors with optical readouts are widely used in particle detection, with Cherenkov and scintillator neutrino detect
arXiv:2608.11275v1 Announce Type: cross Abstract: Systems often need to combine two numerical assessments of the same yes/no question. The appropriate formula depends on what the numbers represent and
arXiv:2608.11732v1 Announce Type: cross Abstract: Proprietary text-to-image diffusion models are increasingly distributed as hosted services and downloadable checkpoints, making their intellectual pro
arXiv:2602.09429v3 Announce Type: replace-cross Abstract: Dynamic models, particularly rate-dependent models, have proven effective in capturing the key phenomenological features of frictional process
arXiv:2608.11219v1 Announce Type: new Abstract: Automatic Prompt Optimization (APO) often rewrites prompts monolithically, which can improve one behavior while degrading others. We present SAPO, a seg
arXiv:2608.11675v1 Announce Type: new Abstract: Coupon campaigns seek to lift both conversion and revenue, but gross merchandise value (GMV) follows a deterministic funnel from conversion to condition
arXiv:2608.11472v1 Announce Type: new Abstract: Pancreatic cancer is among the most lethal malignancies; risk stratification of intraductal papillary mucinous neoplasms (IPMNs) offers a crucial opport
arXiv:2608.12209v1 Announce Type: new Abstract: While Multimodal Large Language Models (MLLMs) have achieved remarkable progress, visual understanding and generation are typically treated as divergent
arXiv:2608.11618v1 Announce Type: new Abstract: Diffusion-based generative video compression has emerged as a promising paradigm to improve perceptual quality, where latent frames are required to be e
arXiv:2608.12185v1 Announce Type: new Abstract: Deep learning models for neuroimaging have largely been developed for individual tasks, limiting knowledge transfer across applications. Here we introdu
arXiv:2608.11838v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have advanced image geolocalization mainly by improving how they reason about geographic cues. How that reasoni
arXiv:2608.12203v1 Announce Type: new Abstract: Generative models like Diffusion Models and Flow Matching have demonstrated remarkable capabilities in synthesizing high-fidelity driving videos, but ar
arXiv:2608.11237v1 Announce Type: new Abstract: Neural operators have shown strong potential for learning solution operators of partial differential equations (PDEs). However, long-horizon autoregress
arXiv:2608.11273v1 Announce Type: cross Abstract: Plenoptic point clouds (PPC) are novel data structures that represent the light from different viewing directions in order to provide a higher degree
arXiv:2608.12097v1 Announce Type: new Abstract: Rubric-based evaluators commonly treat rubrics as prompt context or flat criteria: they specify what to judge but leave criterion composition implicit,
arXiv:2608.11911v1 Announce Type: cross Abstract: A central promise of useful quantum advantage is the ability to compute ground states of Hamiltonian systems beyond the reach of classical simulation
arXiv:2608.12239v1 Announce Type: cross Abstract: Use this plain-text version for the arXiv abstract field: Learned image compression (LIC) models achieve strong rate-distortion performance but are hi
arXiv:2412.18081v3 Announce Type: replace-cross Abstract: We study Heterogeneous Transfer Learning (HTL) for high-dimensional regression with differing feature sets. Such feature mismatch arises when
arXiv:2603.00056v2 Announce Type: replace-cross Abstract: STEM Mental models can play a critical role in assessing students' conceptual understanding of a topic. They not only offer insights into what
arXiv:2608.12035v1 Announce Type: cross Abstract: Deploying unsupervised domain adaptation (UDA) in clinical practice requires choosing which algorithm to use and which of its trained models to ship.
When we set out to talk to kids about artificial intelligence, we thought we knew what we’d hear. We expected some to tell us they were using it to cheat a little, the way Millennials and Gen Xers ope
arXiv:2608.12187v1 Announce Type: cross Abstract: Transformer-based methods have achieved strong performance in monocular 3D human pose estimation, but most existing approaches organise spatial and te
arXiv:2608.11805v1 Announce Type: new Abstract: Gated attention is an effective approach to mitigate attention sinks and enhance the representational capacity of attention. To further extend its effec
arXiv:2608.11768v1 Announce Type: new Abstract: The adaptive neuro-fuzzy inference system (ANFIS) is an interpretable reasoning framework capable of generating explicit IF-THEN fuzzy rules, making it
arXiv:2608.11499v1 Announce Type: cross Abstract: Task vectors enable model merging without joint retraining. In practice, the subset of task vectors to be merged may vary, but many existing methods u
arXiv:2602.11650v2 Announce Type: replace Abstract: Although large language models (LLMs) show promise for generating educational feedback, it remains unclear how feedback should be designed (e.g., to
arXiv:2608.11831v1 Announce Type: new Abstract: Learning mappings between infinite-dimensional objects is a central challenge in scientific machine learning. We introduce a general kernel-based encode
arXiv:2608.11461v1 Announce Type: new Abstract: Rendering haptic feedback with nonlinear virtual environments (VEs) is important in many applications that require highly accurate force feedback. This
arXiv:2608.12283v1 Announce Type: cross Abstract: Large language models can extract richer signals from financial news than fixed sentiment lexicons, and recent work has explored feeding such signals
arXiv:2603.13891v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used for automated text annotation in tasks ranging from academic research to content moderation
arXiv:2608.12198v1 Announce Type: cross Abstract: Recent research in machine and deep learning has shown the potential of learningbased motion planning approaches to improve the driving behavior of au
arXiv:2608.11815v1 Announce Type: cross Abstract: Transfer-based adversarial attacks craft adversarial examples using surrogate models to mislead black-box victim models. Beyond perturbation generatio
arXiv:2608.11691v1 Announce Type: cross Abstract: Reinforcement-learning (RL) post-training equips multimodal large reasoning models (MLRMs) with exploratory chains of thought (CoT), substantially imp
arXiv:2602.13452v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly proposed for crisis preparedness and response, particularly for multilingual communication. Howe
arXiv:2603.20895v3 Announce Type: replace Abstract: Existing routers rely on semantic query features or handcrafted features, which often fail to capture model-specific failures or intrinsic task diff