Knowledge catalogue
arXiv:2511.10866v2 Announce Type: replace-cross Abstract: This paper proposes a Short-Window Sliding Learning framework for real-time violence detection in CCTV footages. Unlike conventional long-vide
arXiv:2609.03377v1 Announce Type: new Abstract: Proteins are fundamental to biological processes, with their function determined by the complex interplay between the amino acid sequence and the three-
arXiv:2609.02941v1 Announce Type: cross Abstract: Speech emotion recognition (SER) faces two fundamental challenges: scarcity of labeled data and inter-speaker variability, both of which hinder genera
arXiv:2510.01984v4 Announce Type: replace Abstract: Quadruped mammals coordinate sagittal spinal bending with axial extension and compression during dynamic locomotion. Yet most robotic quadrupeds use
arXiv:2609.03401v1 Announce Type: cross Abstract: We first prove spectral convergence of the random feature method (RFM) for multidimensional targets in Sobolev, Gevrey, ultra-analytic, and bandlimite
arXiv:2609.03376v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) has made dense retrieval over large document collections a standard building block. Organizations increasingly ou
arXiv:2609.03639v1 Announce Type: new Abstract: Training-free, camera-controlled novel view synthesis from a single image using pre-trained video diffusion models often becomes unstable under large ca
arXiv:2609.03447v1 Announce Type: new Abstract: Large-scale 3D surface reconstruction from aerial imagery is fundamental to geospatial mapping and urban modeling. Recent advances in 3D Gaussian Splatt
arXiv:2609.02981v1 Announce Type: new Abstract: Artificial intelligence is changing the form of applied English materials from fixed paper sequences to adaptive learning systems that can diagnose lear
arXiv:2609.02963v1 Announce Type: cross Abstract: Lead optimization in structure-based drug design aims to improve target binding while avoiding unintended interactions with off-target pockets. Howeve
arXiv:2609.03382v1 Announce Type: cross Abstract: Predicting storm surge induced by landfalling tropical cyclones is crucial for flood mitigation and coastal risk management. Traditionally, physics-ba
arXiv:2609.03697v1 Announce Type: cross Abstract: In the natural sciences, symmetries and cause-effect relationships are ubiquitous. Yet for complex machine-learning tasks, like world-modeling in rein
arXiv:2609.04203v1 Announce Type: new Abstract: We introduce S^3T (Self-Supervised Self-Distillation over Time), which, to the best of our knowledge, is the first fully self-contained framework for co
arXiv:2609.03302v1 Announce Type: new Abstract: We present a unified computational approach to tensor-based morphometry in detecting the brain surface shape differences between two clinical groups bas
arXiv:2609.03622v1 Announce Type: cross Abstract: Test-time adaptation (TTA) offers a promising direction for improving speech enhancement models under mismatched acoustic conditions, without requirin
arXiv:2609.03585v1 Announce Type: new Abstract: Thermal infrared imaging offers reliable perception in darkness and adverse weather, but thermal datasets remain scarce, motivating extensive work on tr
arXiv:2609.03231v1 Announce Type: new Abstract: The ambition of the 2025 PNPL competition (Landau et al., 2025) was to launch a multi-year curriculum for non-invasive speech decoding. Designed to prog
arXiv:2609.04009v1 Announce Type: cross Abstract: Noisy annotations pose a significant challenge for supervised deep learning, as neural networks rely on large-scale, high-quality labeled data whose c
arXiv:2609.04005v1 Announce Type: new Abstract: We present an alternative characterization of the occupancy measure of reinforcement learning, obtained by embedding the planning criterion into the dyn
arXiv:2609.03090v1 Announce Type: new Abstract: Continuous chain-of-thought models compress reasoning into latent tokens. Matrix-valued variants, which route each latent token through a d x d matrix b
arXiv:2609.04046v1 Announce Type: cross Abstract: What can a single layer of self-attention compute? We study head complexity: the minimum number of attention heads required to compute a function in a
arXiv:2609.03829v1 Announce Type: cross Abstract: Few-shot fine-grained image classification (FSFGIC) aims to classify similar images with limited labeled examples. This work highlights the critical y
arXiv:2609.03633v1 Announce Type: cross Abstract: Chain-of-thought (CoT) reasoning improves large reasoning models (LRMs) on complex tasks but often produces long, redundant traces. Recent training-fr
arXiv:2609.03358v1 Announce Type: new Abstract: Numerical simulation of dynamical systems is usually organized as a causal march through time: each state is computed from the previous one. We explore
arXiv:2609.03501v1 Announce Type: cross Abstract: Mixture-of-experts (MoE) architectures increase model capacity by combining a collection of expert predictors through input-dependent routing, while o
arXiv:2507.03887v1 Announce Type: cross Abstract: Recent advances in Text-To-Speech (TTS) technology have enabled synthetic speech to mimic human voices with remarkable realism, raising significant se
arXiv:2609.03641v1 Announce Type: new Abstract: Vector-quantization based image compression has achieved strong rate--distortion performance, yet most of them still produce a separate compressed repre
arXiv:2609.03534v1 Announce Type: cross Abstract: 3D Gaussian Splatting has become a de facto scene representation for novel view synthesis, yet robustly learning 3D Gaussian primitives from visual in
arXiv:2609.03775v1 Announce Type: new Abstract: Typological features are widely used in multilingual NLP, and the prediction of such features holds downstream utility. However, existing methods to pre
arXiv:2312.09454v3 Announce Type: replace-cross Abstract: Purpose: Uncertainty Quantification (UQ) has gained traction in an attempt to improve the interpretability and robustness of machine learning
arXiv:2609.03005v1 Announce Type: new Abstract: Many NLP tasks, such as summarization and extractive question answering, reduce to retrieving relevant content from documents under two constraints: cov
arXiv:2609.03824v1 Announce Type: new Abstract: 3D foundation models (3DFMs) excel at predicting camera poses and dense depth from multiple views of a scene, showcasing strong zero-shot generalization
arXiv:2609.03811v1 Announce Type: cross Abstract: AI-assisted computer-aided design (CAD) for industrial products involves two challenging phases. Part-level generation maps diverse forms of user inte
arXiv:2510.12528v2 Announce Type: replace Abstract: Object compliance perception enables the identification of soft materials, supporting tasks such as fruit detection and assisted medical palpation.
arXiv:2609.03203v1 Announce Type: cross Abstract: Expressive speech systems make a decision before any waveform is rendered: how an utterance is delivered. In dialogue agents, narration, and role-cond
We've shared research on fine-tuning forecasters, now you can try it for yourself: a cookbook recipe for training a model to predict event probabilities given reference information. Start with the pro
arXiv:2609.03246v1 Announce Type: cross Abstract: Smoothness of a function on the real line is reflected in the decay of its Fourier transform, which suggests that smoothness of a function in L^2(G) f
arXiv:2609.03515v1 Announce Type: new Abstract: Decoding-time KV cache compression research focuses heavily on designing better token scoring functions, while the temporal rule that aggregates scores
arXiv:2609.03429v1 Announce Type: new Abstract: Answering what-if queries about a scene with a VLM usually means injecting the assumption as text or repainting the scene with a generative model. We in
arXiv:2609.03816v1 Announce Type: cross Abstract: Graphs are a fundamental data structure underlying many problems in the natural and social sciences. Over the past decade, Graph Neural Networks (GNNs
arXiv:2609.03158v1 Announce Type: cross Abstract: Visual token pruning reduces the inference cost of vision-language models (VLMs), but most methods only ask which tokens to keep. This retained-token
arXiv:2609.03826v1 Announce Type: cross Abstract: Unsupervised anomaly detection scores each point of an unlabelled, contaminated sample in a single pass, and increasingly must also explain why a poin
arXiv:2607.17544v1 Announce Type: cross Abstract: Real-time speech-to-speech translation (S2ST) systems must balance translation quality, latency, speech naturalness, and speaker consistency. Publicly
arXiv:2609.02887v1 Announce Type: new Abstract: Speech brain-computer interfaces (speech BCIs) translate neural activity into language, offering a path towards restoring speech for people with paralys
arXiv:2609.02538v1 Announce Type: new Abstract: Graph construction is a critical but underexamined design choice in deep reinforcement learning for power grid control. We present a controlled experime
arXiv:2609.02382v1 Announce Type: cross Abstract: Gaussian graphical models (GGMs) are essential tools for interpretable structure learning. However, in high-dimensional, small-sample regimes, the ava
arXiv:2609.02153v1 Announce Type: new Abstract: Grammatical error annotation in Chinese learner writing requires labels that are both consistent and linguistically meaningful. This paper proposes a la
arXiv:2609.02143v1 Announce Type: cross Abstract: Most vector databases rely on graph-based indexes, notably HNSW and Vamana, for approximate nearest neighbor search. With embedding models widely adop
arXiv:2406.16893v3 Announce Type: replace-cross Abstract: The emergence of Transformer-based Large Language Models (LLMs) has substantially augmented the capabilities of Natural Language Processing (N
arXiv:2509.06554v2 Announce Type: replace-cross Abstract: In subjective image and video quality assessment, observers rate or compare selected stimuli. Before calculating mean opinion scores (MOSs), u
arXiv:2609.01963v1 Announce Type: new Abstract: Content-based image retrieval (CBIR) has advanced significantly with deep learning, yet effectively ranking similar images remains challenging, particul
arXiv:2609.02821v1 Announce Type: new Abstract: Researchers increasingly use artificial intelligence to construct measures of social, organizational, and occupational characteristics that are absent f
arXiv:2609.02210v1 Announce Type: new Abstract: Portable ultra-low-field (ULF) MRI can expand access to pediatric neuroimaging, but segmentation at 0.064 T remains challenging because anatomical bound
arXiv:2609.01921v1 Announce Type: new Abstract: Maize kernel traits such as row number, kernels per row, and kernel size vary largely for genetic reasons and are consistently associated with regions o
arXiv:2609.01828v1 Announce Type: new Abstract: Spoken dialogue state tracking recovers slot-value pairs from speech, where ASR errors concentrate in entity values and persist across turns, making it
arXiv:2606.21052v2 Announce Type: replace-cross Abstract: Speech Emotion Recognition (SER) systems increasingly leverage self-supervised acoustic representations, yet their vulnerability to training-t
arXiv:2609.01914v1 Announce Type: cross Abstract: Reliable attractor recall conventionally requires broad basins of attraction. However, in reservoir-computing based associative memory, temporal cues
Benchmarking AI systems is a continual process that co-evolves with the models. New benchmarks challenge AI capabilities with emerging questions to shape the directions and feedback signal of the rese
arXiv:2609.02129v1 Announce Type: new Abstract: Data-centric agents repeatedly perform a discovery step before planning or execution: identifying the data objects relevant to a task. Yet successful di
arXiv:2601.07468v2 Announce Type: replace Abstract: Memory enables Large Language Model (LLM) agents to perceive, store, and use information from past dialogues, which is essential for personalization