Knowledge catalogue
arXiv:2609.34581v1 Announce Type: new Abstract: Temporal video grounding is a key capability of advanced Multimodal Large Language Models (MLLMs) for the thorough understanding of video events, which
arXiv:2609.31659v1 Announce Type: new Abstract: Power magnetic materials are characterized on the sinusoidal and triangular waveforms that excitation hardware conveniently produces, whereas deployed c
arXiv:2609.34593v1 Announce Type: new Abstract: Off-policy evaluation is a foundational component of offline reinforcement learning, aiming to assess and optimize policy performance using pre-collecte
arXiv:2609.33836v1 Announce Type: cross Abstract: Construction environments evolve continuously, causing large geometric changes that degrade static mapping and registration performance. This necessit
arXiv:2510.04525v3 Announce Type: replace Abstract: Masked diffusion models have shown promising performance in generating high-quality samples in a wide range of domains, but accelerating their sampl
arXiv:2609.32534v1 Announce Type: new Abstract: Depth is a natural way to increase the computational capacity in Transformers, yet the contribution of deeper layers can diminish as depth grows larger.
arXiv:2609.35022v1 Announce Type: new Abstract: The integration of deep learning models into image preprocessing pipelines such as super-resolution introduces a largely unexplored attack vector for ad
arXiv:2609.31681v1 Announce Type: new Abstract: Devnagari is a one of the ancient language of the Indian subcontinent consisting of 36 vowels, 14 consonants and 10 numerals. The accurate recognition o
arXiv:2609.31885v1 Announce Type: new Abstract: Autonomous micro-mobility vehicles (MMVs) such as wheelchairs, scooters, and bicycles have the potential to improve mobility access and support safe low
arXiv:2511.15990v2 Announce Type: replace-cross Abstract: Digital agriculture is transforming the way we grow food by utilizing technology to make farming more efficient, sustainable, and productive.
arXiv:2609.35444v1 Announce Type: new Abstract: Robust skeleton-based action recognition requires representations that capture a wide spectrum of motions, from subtle to moderate and strong ones. Exis
arXiv:2510.17611v3 Announce Type: replace Abstract: Unsupervised anomaly detection (UAD) has evolved from specialized single-class models to unified multi-class models, yet existing multi-class models
arXiv:2609.33527v1 Announce Type: new Abstract: Parameter space symmetries are important for understanding neural networks' loss landscape, training dynamics, and generalization. However, systematical
arXiv:2609.35699v1 Announce Type: new Abstract: Distillation attacks copy the reasoning capabilities of closed-source large language models, allowing bad actors to replicate state-of-the-art performan
arXiv:2609.31653v1 Announce Type: cross Abstract: Sentiment analysis is a common tool for converting unstructured text into quantitative signals in finance and risk management. Yet most applications r
We did a study: does post training actually make LLMs funnier? We used open models that publish every stage of post training, so we could compare a base model with the future models it became: Tulu 3
arXiv:2609.34921v1 Announce Type: new Abstract: Predicting Drug-Target Interactions~(DTIs) is a central task in computational drug discovery, with direct applications in virtual screening, drug repurp
arXiv:2609.33530v1 Announce Type: cross Abstract: Natural Language Understanding (NLU) plays a crucial role in various applications, yet its performance suffers from weaknesses in handling the complex
arXiv:2412.16633v4 Announce Type: replace Abstract: LLM-based robots use Large Language Models (LLMs) as planners to translate natural language instructions into policies such as grasp(), move_to(), a
arXiv:2609.32176v1 Announce Type: new Abstract: The support recovery problem in mixture of linear classifiers intends to identify which features actually matter when data is generated by a mixture of
arXiv:2609.31638v1 Announce Type: new Abstract: Modern design optimization frameworks aim first and foremost for models with the most accurate predictions without balancing computational overhead. It
arXiv:2609.31648v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models are studied mainly in robotics, where visual observations and language instructions are mapped to physical actions.
arXiv:2609.33939v1 Announce Type: cross Abstract: Reliable 3D spatial understanding is essential for autonomous navigation, obstacle avoidance, and scene reconstruction. While state-of-the-art learned
arXiv:2609.35277v1 Announce Type: new Abstract: Tironian notes are generally regarded as the first Latin shorthand system and are notable for their large, fine-grained symbol inventory. Their high vis
arXiv:2609.32190v1 Announce Type: new Abstract: DNA methylation (DNAM) profiling has emerged as a powerful diagnostic tool for classifying brain and solid tumors. However, existing computational model
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:2308.09456v2 Announce Type: replace Abstract: Overtaking on two-lane roads is a safety-critical decision-making problem for autonomous vehicles, since oncoming traffic may force the ego vehicle
arXiv:2609.32177v1 Announce Type: new Abstract: Three-dimensional (3D) Gaussian splatting (3D-GS) has emerged as a promising technique for large-scale scene reconstruction due to its high rendering ef
arXiv:2609.32696v1 Announce Type: cross Abstract: Data shape determines how features are structured, how patterns are separated, and how distributions cover the underlying domain. Poor data shape can
arXiv:2609.32773v1 Announce Type: cross Abstract: Monetary-policy announcements and central-bank communications play a central role in foreign exchange markets, yet their qualitative, unstructured for
arXiv:2609.34994v1 Announce Type: cross Abstract: Most music-generation systems are still framed and evaluated primarily as producers of complete outputs, whereas composition often proceeds through su
arXiv:2609.31656v1 Announce Type: new Abstract: Data quality is a major bottleneck for the reliable deployment of graph neural networks (GNNs) in real-world graph mining tasks. Among various sources o
arXiv:2609.34371v1 Announce Type: new Abstract: On-policy distillation (OPD) specializes pretrained video diffusion models through teacher supervision along the student's own generation trajectory. Al
arXiv:2609.31749v1 Announce Type: new Abstract: We built an ordinary stacking ensemble for AI-generated image detection -- three open detectors producing five scores, fused by a gradient-boosted meta-
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:2609.34825v1 Announce Type: new Abstract: Gaussian neural networks (GaNNs) are proposed as a novel regularization mechanism for neural networks. From a Bayesian perspective standard regularizati
arXiv:2609.32941v1 Announce Type: new Abstract: Inferring quantities of interest (QoI) from data is a central task in Science and Engineering. In such contexts, we often have access to both quantitati
arXiv:2609.35341v1 Announce Type: new Abstract: Evaluating semantic similarity between videos is a fundamental challenge in computer vision, essential for tasks ranging from out-of-distribution (OOD)
arXiv:2609.34172v1 Announce Type: new Abstract: Active grounding of a frozen diffusion prior requires jointly determining where new measurements should be taken and how they should be used to refine t
arXiv:2609.35042v1 Announce Type: cross Abstract: Martian weather forecasting is important for future exploration, but atmospheric behaviour on Mars combines spatial, temporal, vertical, and dust-driv
arXiv:2609.35000v1 Announce Type: new Abstract: Robot-aided operations in space stations are essential for reducing the workload of astronauts and improving the efficiency of on-orbit activities. Mult
arXiv:2609.31636v1 Announce Type: cross Abstract: Vision-language models are increasingly used for driving-scene understanding, yet the semantic relations expressed in their outputs are often difficul
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:2609.33983v1 Announce Type: cross Abstract: Semantic Textual Similarity (STS) methods assume that a document's lexical content faithfully represents what it asserts. This assumption fails for di
arXiv:2609.33352v1 Announce Type: new Abstract: Set-valued classifiers, whether derived from precise probabilities and an adapted cost function, from convex sets with a robust inference mechanism, or
arXiv:2609.34490v1 Announce Type: new Abstract: Large collections of historical Persian manuscripts have been digitized, but searching them is still slow and mostly manual. Historians usually want to
arXiv:2609.32008v1 Announce Type: new Abstract: Recent advances in sensor technology have enabled more effective human activity recognition (HAR), particularly in real-time systems with limited comput
arXiv:2609.34584v1 Announce Type: cross Abstract: In-game toxic language has emerged as a critical concern in the gaming industry and community. While several frameworks and models for online game tox
arXiv:2609.31929v1 Announce Type: new Abstract: Newton methods efficiently find Generalized Nash Equilibria (GNE) in dynamic games by solving for the KKT necessary conditions. These methods are fast a
arXiv:2609.34976v1 Announce Type: cross Abstract: The integration of artificial intelligence into computer-aided design frameworks has sparked a shift in the design of analog integrated circuits (ICs)
arXiv:2609.33951v1 Announce Type: new Abstract: The increasing adoption of robotic systems in surgery, together with the expanding range of procedures they can support and the growing level of autonom
arXiv:2609.35260v1 Announce Type: new Abstract: Spiking neural networks are attractive for low-power speech command recognition, yet their latency has received far less attention than their energy eff
arXiv:2605.06050v2 Announce Type: replace Abstract: Graph-based learning from functional magnetic resonance imaging (fMRI) has shown strong potential for brain network analysis. However, existing meth
arXiv:2609.33028v1 Announce Type: new Abstract: A feed-forward neural network whose weights are integers and whose activation is the truncated identity implements, neuron by neuron, the connectives of
arXiv:2609.34378v1 Announce Type: new Abstract: Humans naturally possess the ability to work persistently toward long-term goals. Given a challenging task, humans can continuously work for months or e
arXiv:2609.31705v1 Announce Type: new Abstract: Byte-Pair Encoding (BPE) constructs subword vocabularies through greedy pair merging, but conventional BPE requires the number of merges or target vocab
arXiv:2609.34420v1 Announce Type: new Abstract: Graph Transformers (GTs) offer superior representation capabilities by overcoming the depth limitations and over-smoothing issues of traditional Graph N
arXiv:2609.35701v1 Announce Type: new Abstract: The success of large language models (LLMs) has been accompanied by continued growth in model size and pretraining costs. Muon offers high accuracy and
arXiv:2601.19284v3 Announce Type: replace-cross Abstract: Stabilizing a dynamical system is a fundamental problem that serves as a cornerstone for many complex tasks in the field of control systems. T
arXiv:2609.34451v1 Announce Type: new Abstract: Due to the gigapixel-scale nature of whole-slide images (WSIs), weakly supervised WSI analysis is commonly formulated as a multiple instance learning (M