Knowledge catalogue
arXiv:2608.02624v1 Announce Type: cross Abstract: Refinement operators generate many functions used in wavelet constructions, subdivision schemes, and geometric modeling. Their finite iterates can dev
arXiv:2608.03277v1 Announce Type: new Abstract: Differentially private zeroth-order optimization (DP-ZO) enables memory-efficient private fine-tuning of large language models using only forward evalua
arXiv:2608.03113v1 Announce Type: new Abstract: Adulteration of bovine milk using urea remains a major food quality and health concern, motivating the development of rapid and quantitative screening t
arXiv:2608.03812v1 Announce Type: new Abstract: Omni-modal large language models (Omni-LLMs) have achieved remarkable performance on audio-visual understanding tasks, but processing long and highly re
arXiv:2608.03233v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable potential for analogy making, a core cognitive capability that drives novelty and creativity.
arXiv:2608.02999v1 Announce Type: new Abstract: Statistical regularities are routinely offered as evidence that undeciphered sign systems encode language; the Indus script debate is the canonical exam
arXiv:2608.02671v1 Announce Type: cross Abstract: Malware detection using Hardware Performance Counters (HPC) has emerged as a promising solution to improve the security of computing systems as a comp
arXiv:2608.03225v1 Announce Type: new Abstract: Human-interpretable computer-aided diagnosis is crucial for clinical decision making. Concept-based models excel by providing transparent reasoning and
arXiv:2608.02691v1 Announce Type: cross Abstract: The key-value (KV) cache has become a major memory and bandwidth bottleneck in long-context large language model inference, making ultra-low-bit quant
arXiv:2608.03077v1 Announce Type: new Abstract: Multi-domain machine translation (MDMT) requires more than fluent generation: it demands domain-sensitive translation decisions such as domain disambigu
arXiv:2511.13300v1 Announce Type: cross Abstract: Generative models have shown remarkable performance in speech enhancement (SE), achieving superior perceptual quality over traditional discriminative
arXiv:2608.03055v1 Announce Type: cross Abstract: Automatic radiology report generation (RRG) aims to simulate the workflow of radiologists, assisting them in clinical diagnosis. However, existing met
arXiv:2511.10696v3 Announce Type: replace-cross Abstract: Sparse attention is crucial in long-context Transformers, which restricts each token to a limited neighborhood and thereby reduces the quadrat
arXiv:2608.02792v1 Announce Type: new Abstract: Self-supervised Vision Foundation Models (VFMs) have become essential backbones for downstream tasks due to their strong and transferable visual represe
arXiv:2608.03630v1 Announce Type: new Abstract: Deep learning has advanced time series forecasting, but periodicity drift, in which cycle timing and phase vary over time, remains a challenging problem
arXiv:2608.03323v1 Announce Type: new Abstract: Estimating room layouts from multi-view imagery is a core task for indoor scene understanding. Existing methods are typically limited either by poor gen
arXiv:2608.03709v1 Announce Type: new Abstract: Large hyperparameter sweeps for deep neural networks spend substantial compute on configurations that are effectively doomed from the first few epochs.
arXiv:2608.02704v1 Announce Type: new Abstract: Predictive processing theories portray the brain as a hierarchical prediction engine that minimizes prediction error, yet they lack operational definiti
arXiv:2512.12840v2 Announce Type: replace-cross Abstract: Vertical Federated Learning (VFL) enables collaborative model training across organizations that share common user samples but hold disjoint f
arXiv:2608.03439v1 Announce Type: cross Abstract: In recent work, Marcussen, Rubinfeld, and Sudan introduced the notion of quality control problems, which aim to capture the task of determining if a g
arXiv:2608.03304v1 Announce Type: new Abstract: Medical image classification often suffers from class imbalance due to the inherent disparities in disease incidence. Existing approaches, such as class
arXiv:2406.00238v3 Announce Type: replace-cross Abstract: Bounded bihramonic weights are a popular tool used to rig and deform characters for animation, to compute reduced-order simulations, and to de
arXiv:2608.03928v1 Announce Type: cross Abstract: Tensor cross-concentrated sampling (t-CCS) bridges entrywise sampling and t-CUR slice-wise sampling by observing entries only within selected horizont
arXiv:2608.03540v1 Announce Type: new Abstract: Digital pathology relies on high-resolution whole slide images for accurate diagnosis, yet limitations in imaging devices, storage, and transmission oft
arXiv:2608.02961v1 Announce Type: cross Abstract: We study a self-supervised generation task for single-cell gene expression vectors: given a set of vectors from a cell type, we aim to generate additi
arXiv:2608.02950v1 Announce Type: new Abstract: Checkpoint staffing requires accurate forecasts of when screening demand will occur, yet flight schedules record departure times rather than passenger a
arXiv:2608.02937v1 Announce Type: cross Abstract: Designing an effective electromagnetic inverse-scattering solver requires faithful enforcement of nonlinear full-wave physics together with an express
arXiv:2608.03120v1 Announce Type: new Abstract: Adapting CLIP for zero-shot sketch-based image retrieval (ZS-SBIR) via prompt learning faces a fundamental tension: the model must bridge the sketch-pho
arXiv:2608.02656v1 Announce Type: cross Abstract: The Secured data safe guard transaction with multi-tenant environments run on private-protected authenticate platforms runs by secured handed environm
arXiv:2608.02946v1 Announce Type: new Abstract: Accurate detection of sedentary behavior is important for studying health risks related to prolonged sitting, but posture-based classification remains c
arXiv:2608.03962v1 Announce Type: cross Abstract: Modern large language models - transformers and diffusion language models - are built around two canonical algorithmic tasks: prediction and generatio
arXiv:2608.03378v1 Announce Type: cross Abstract: The development and testing of advanced aerial robots require experiments in controlled environments with tailored airflow profiles. This paper presen
arXiv:2608.03250v1 Announce Type: cross Abstract: The rapid advancement of modern technology has led to a significant increase in the use of smart devices, such as smartphones and tablets, resulting i
arXiv:2608.03116v1 Announce Type: new Abstract: Motion retargeting approaches often prioritize kinematic similarity over whole-body dynamics, contact consistency, and actuation limits, yielding refere
arXiv:2608.03469v1 Announce Type: cross Abstract: We study score learning for reflected diffusion on bounded domains. Reflection keeps trajectories feasible but does not ensure that the learned score
arXiv:2608.03970v1 Announce Type: new Abstract: Human input reaches language models by typing or speaking, and each channel leaves a distinct signature: orthographic noise for keyboards; for voice, di
arXiv:2608.03117v1 Announce Type: new Abstract: The performance of generative diffusion models is determined by the choice of the reference diffusion process connecting the empirical and prior distrib
arXiv:2608.02356v2 Announce Type: replace Abstract: Large language model agents increasingly solve complex tasks by composing reusable skills from a library. To address this, the key challenge is not
arXiv:2608.03429v1 Announce Type: new Abstract: We introduce the Infinite SLAM Transformer (SLAMFormer-infty), the first geometric transformer capable of supporting both long-range frontend and backen
arXiv:2608.02995v1 Announce Type: cross Abstract: Modern large language models (LLMs) exhibit activation sparsity, wherein only a subset of their neurons is activated for given input tokens. Researche
arXiv:2508.05149v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have demonstrated potential in handling spoken inputs for high-resource languages, reaching state-of-the-art perf
arXiv:2608.03165v1 Announce Type: cross Abstract: Invisible image watermarks are increasingly used for deepfake detection and provenance tracking, where they must survive not only incidental distortio
arXiv:2608.03395v1 Announce Type: new Abstract: Deepfake technologies pose increasing threats to facial privacy and identity security, motivating proactive defenses that protect facial images before m
arXiv:2608.03023v1 Announce Type: cross Abstract: Remote sensing semantic segmentation is hindered by costly pixel-level annotations, motivating training-free open-vocabulary methods. Recently, the re
arXiv:2608.03425v1 Announce Type: new Abstract: Transformer-based architectures have dominated sequence modeling, largely due to the expressive power of attention mechanisms. However, for a class of d
arXiv:2407.11421v2 Announce Type: replace Abstract: Large Language Models (LLMs) exhibit emergent abilities that may reveal aspects of their internal mechanisms. We study one such capability: directly
arXiv:2608.03001v1 Announce Type: cross Abstract: Unit excitation (UE) is a common assumption in stochastic saddle avoidance: the stochastic error must have a uniformly positive component along every
arXiv:2502.08397v3 Announce Type: replace-cross Abstract: Clustering is a fundamental technique in data analysis and machine learning, used to group similar data points together. Among various cluster
arXiv:2603.09234v2 Announce Type: cross Abstract: Achieving high perceptual quality without hallucination remains a challenge in generative speech enhancement (SE). A representative approach, PASE, is
arXiv:2607.15196v2 Announce Type: replace-cross Abstract: We present a novel viewpoint for uncertainty quantification. Uncertainty measures are not primitives, in need of axioms and argumentation, but
arXiv:2603.01243v2 Announce Type: replace Abstract: Large language models (LLMs) are powerful tools that have found applications beyond human-machine interfaces and chatbots. Beside free-form generati
arXiv:2608.03158v1 Announce Type: new Abstract: Daily activities require humans to coordinate whole-body motion with the motion of surrounding objects. Despite recent progress in human-object interact
arXiv:2608.03172v1 Announce Type: new Abstract: Structure-preserving de-identification replaces protected health information (PHI) with realistic same-type surrogates -- 'Anna S.' becomes 'Maria S.',
arXiv:2608.02609v1 Announce Type: new Abstract: Half a million cuneiform clay tablets survive in museums worldwide, yet modern users can neither read nor write in the world's oldest writing system, le
arXiv:2608.03057v1 Announce Type: new Abstract: Static quantization assigns one weight precision to every denoising step. To preserve quality, that precision must accommodate the most quantization-sen
arXiv:2608.02985v1 Announce Type: cross Abstract: The standard check for contamination in LLM backtests is simple: compare scores before and after the training cutoff. We show this check is uninformat
arXiv:2608.04001v1 Announce Type: cross Abstract: Large language models can solve substantially harder reasoning problems with more inference-time compute. The term 'test-time scaling,' however, now c
The bottleneck for AI progress was never compute, it was always the verifier. Recursive self-improvement is limited by verification, not computation. Compute buys proposals - verifiers buy knowledge.
arXiv:2607.27933v2 Announce Type: replace Abstract: Flow matching (FM) has become a popular action head paradigm for modern embodied models. However, as a conditional generative model, it does not exp
arXiv:2608.03921v1 Announce Type: new Abstract: This paper offers a new interpretation of the Transformer during inference. Against the 'stochastic parrot' view that large language models merely repro