Knowledge catalogue
arXiv:2603.15203v1 Announce Type: cross Abstract: Nuclear mass prediction is one of the core issues in nuclear physics research, yet it faces the challenge of small-sample datasets with high complexit
arXiv:2609.09766v1 Announce Type: new Abstract: Churn models typically identify high-risk customers but do not specify which feasible retention action should be considered or why that action is approp
arXiv:2609.09659v1 Announce Type: new Abstract: Federated learning shares model updates rather than raw data, yet these updates can be inverted to reconstruct the clients' training data. Analytic reco
arXiv:2609.09255v1 Announce Type: cross Abstract: Graph-data workloads such as diffusion estimation, ranking, semi-supervised learning, and network optimization often solve many Laplacian or symmetric
arXiv:2602.08751v4 Announce Type: replace Abstract: Motivation: Interpretability is not optional in biology: understanding gene regulation requires models whose learned structure can be directly inter
arXiv:2609.10525v1 Announce Type: cross Abstract: Language generation in the limit asks for valid unseen elements from every exhaustive positive presentation of an unknown infinite language. We charac
arXiv:2609.07434v1 Announce Type: new Abstract: Natural-language Computer-Aided Design (CAD) code generation aims to turn design intent into executable and editable parametric programs. Large language
arXiv:2609.10484v1 Announce Type: new Abstract: Marine robotic simulation provides a safe and inexpensive method of developing and testing algorithms for unmanned underwater vehicle (UUV) and unmanned
arXiv:2604.25512v3 Announce Type: replace Abstract: In phishing detection, machine learning classifiers act as a first line of defense, but the false positives they produce are triaged by human analys
arXiv:2609.10441v1 Announce Type: cross Abstract: While Large Language Models (LLMs) have demonstrated impressive capabilities, they often struggle with extremely long contexts due to fixed context li
arXiv:2609.10230v1 Announce Type: new Abstract: Cylindrical watertight enclosures are widely used across various underwater systems, from unmanned underwater vehicles (UUVs), to remotely operated vehi
arXiv:2609.09244v1 Announce Type: cross Abstract: The edge-of-chaos condition preserves first-order input perturbations in wide randomly initialized networks, but physics-informed losses, score matchi
arXiv:2609.05439v1 Announce Type: new Abstract: Current evaluation methods for large language models are coarse-grained and decoupled from generation, producing generic explanations that fail to provi
arXiv:2609.10350v1 Announce Type: cross Abstract: The banking system now depends on a small set of shared artificial intelligence vendors for fraud screening, credit decisioning, anti-money-laundering
arXiv:2606.05957v2 Announce Type: replace Abstract: Singular learning theory and information geometry study the same spaces: the former in resolved coordinates, the latter in original coordinates unde
arXiv:2609.09885v1 Announce Type: cross Abstract: This paper studies unmanned aerial vehicle (UAV)-mouted reconfigurable intelligent surface (RIS)-assisted device-to-device (D2D) communication with st
arXiv:2609.09359v1 Announce Type: new Abstract: Polarimetric vision is gaining increasing attention because it provides physical cues about scene shape, material, and reflection that are difficult to
arXiv:2609.09217v1 Announce Type: new Abstract: Legged robots are versatile on land, but their use in underwater environments remains limited. Extending quadruped locomotion to water enables amphibiou
arXiv:2609.10334v1 Announce Type: new Abstract: This paper addresses the issue of supervised classification in the context of hyperspectral satellite images. It deals with two fundamental aspects: dim
arXiv:2609.10052v1 Announce Type: new Abstract: LLM agents for sequential decision tasks are often post-trained with trajectory-level outcome labels, but such labels provide little supervision for pre
arXiv:2609.07222v1 Announce Type: new Abstract: Transformer surrogates for 3D flow prediction compress an industrial mesh into a small set of tokens from which every prediction point reads. Two operat
arXiv:2609.09586v1 Announce Type: cross Abstract: Time series foundation models (TSFMs) are increasingly pre-trained on synthetically generated time series trajectories, where the data generating proc
arXiv:2609.09737v1 Announce Type: new Abstract: Test-Time Adaptation (TTA) enhances the robustness of models against distribution shifts but faces two critical challenges: error accumulation from nois
arXiv:2511.18031v1 Announce Type: cross Abstract: Few-shot object detection (FSOD) aims to detect novel instances with only a limited number of labeled training samples, presenting a challenge that is
arXiv:2609.07754v1 Announce Type: cross Abstract: AI coding assistants now select, install, and configure software, and attackers have exploited that position through invented package names, compromis
arXiv:2609.10506v1 Announce Type: cross Abstract: Action-conditioned latent world models predict future visual representations, enabling zero-shot goal-conditioned robot planning and control. However,
arXiv:2609.10028v1 Announce Type: cross Abstract: Multidimensional item response theory (MIRT) is a statistical test theory that precisely estimates multiple latent skills of learners from the respons
arXiv:2609.09708v1 Announce Type: cross Abstract: 6G wireless technology is poised to adopt higher and wider frequency bands, leveraging highly directional beamforming. However, the vast bandwidths am
arXiv:2609.09433v1 Announce Type: new Abstract: Leave-One-Subject-Out (LOSO) evaluation estimates generalisation performance for subject-based classification but makes Neural Architecture Search (NAS)
arXiv:2601.15295v2 Announce Type: replace-cross Abstract: Interactive narrative (IN) authors craft spaces of divergent narrative possibilities for players to explore, with the player's input determini
arXiv:2609.07139v1 Announce Type: new Abstract: A transformer can make an attribute linearly decodable in its residual stream at a depth where that attribute does not yet influence the output. This ga
arXiv:2609.08943v1 Announce Type: cross Abstract: Automatic fact-checking systems assess the veracity of claims given evidence from relevant documents. Large Language Models (LLMs) have demonstrated s
arXiv:2510.22115v2 Announce Type: cross Abstract: We introduce Ling 2.0, a series reasoning-oriented language foundation built upon the principle that every activation boosts reasoning capability. Des
arXiv:2609.08790v1 Announce Type: cross Abstract: Threat hunting increasingly depends on converting unstructured knowledge (e.g., Cyber Threat Intelligence reports) into actionable hunt leads: concise
arXiv:2609.09184v1 Announce Type: new Abstract: Vision-language model (VLM) confidence may change in aggregate when visual evidence is degraded while remaining structurally inconsistent within individ
arXiv:2608.27824v2 Announce Type: replace Abstract: Evidential argumentation extends Dung's abstract argumentation by requiring arguments and interactions to be backed by chains of evidence rooted in
arXiv:2609.08435v2 Announce Type: new Abstract: In persistent interactions, long contexts may encode an evolving process rather than a fixed record: later events can revise or revoke earlier informati
arXiv:2609.09860v1 Announce Type: new Abstract: Balanced k-shot sampling draws exactly k labeled examples per class. We show that it induces an exact, provable degeneracy in a family of small-sample d
arXiv:2609.09466v1 Announce Type: new Abstract: Online gradient descent is usually studied through external regret, where the learner competes with fixed alternatives. Recent work shows that first-ord
arXiv:2609.06155v1 Announce Type: cross Abstract: Neural models, including dense retrievers, have been widely adopted in Information Retrieval (IR), often delivering state-of-the-art performance. Desp
arXiv:2602.14536v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have seen remarkable advancements, achieving state-of-the-art results in diverse applications. Fine-tuning, an im
arXiv:2609.08310v1 Announce Type: cross Abstract: The success of generative retrieval has largely been attributed to the use of Semantic IDs, which improve over arbitrary item-level identifiers such a
arXiv:2609.05806v1 Announce Type: new Abstract: Emotion Recognition in Conversations (ERC) aims to identify speakers' emotions in multi-turn dialogue. Accurate emotion recognition can support a wide r
arXiv:2602.20773v2 Announce Type: replace Abstract: Purpose: Developing generalizable medical image segmentation models is challenging because imaging data are distributed across institutions and diff
arXiv:2609.10498v1 Announce Type: new Abstract: Recovering 3D human pose from monocular sports broadcasts remains challenging when players must be localized in a shared metric world coordinate system
arXiv:2606.23346v2 Announce Type: replace-cross Abstract: We perform a realistic KiDS-Legacy mock analysis with field-level neural compression and simulation-based inference using just 60 N-body simul
arXiv:2609.09883v1 Announce Type: new Abstract: Depth pruning reduces large language model (LLM) inference cost by removing complete Transformer blocks. Activation-based methods collect hidden states
arXiv:2609.07153v1 Announce Type: cross Abstract: Minimal-pair benchmarks such as BLiMP evaluate linguistic knowledge by testing whether language models (LMs) prefer acceptable sentences over minimall
arXiv:2609.10278v1 Announce Type: new Abstract: Recent progress in deep learning has significantly advanced facial landmark detection. However, most existing methods process features in a spatial-doma
arXiv:2609.10405v1 Announce Type: new Abstract: Robot actions are temporally correlated trajectories whose frequency components encode motion at different scales with highly non-uniform energy distrib
arXiv:2601.07268v2 Announce Type: replace Abstract: Data-driven landslide susceptibility mapping (LSM) typically relies on landslide conditioning factors (LCFs), whose availability, heterogeneity, and
arXiv:2609.09876v1 Announce Type: new Abstract: Dense change detection in remote sensing requires vision-language models (VLMs) to compare bi-temporal images and generate accurate pixel-level masks. E
arXiv:2511.16811v2 Announce Type: replace Abstract: Building on the third-wave Extended Mind (EM) theory and radical enactivism, this article suggests an alternative to representation-based models of
arXiv:2609.08899v2 Announce Type: replace-cross Abstract: Speech deepfakes can mimic a speaker's voice convincingly enough to deceive listeners and automated systems. This has driven strong progress i
arXiv:2609.09480v1 Announce Type: cross Abstract: We develop Gaussian approximation bounds in higher-order Wasserstein distance W_p, pgeq2, for sums of multivariate martingale differences generated by
arXiv:2609.06071v1 Announce Type: new Abstract: PDDL, the de-facto standard language in the AI Planning community, is designed to specify planning domains: sets of instances that share the same predic
arXiv:2609.09573v1 Announce Type: cross Abstract: Odor quality is commonly represented using high dimensional descriptor profiles, yet their low dimensional organization remains unclear. We investigat
arXiv:2609.10322v1 Announce Type: new Abstract: Transferring the rich priors of large 2D foundation models to sparse 3D LiDAR remains challenging, as training native 3D foundation models at comparable
arXiv:2609.09556v1 Announce Type: cross Abstract: Neural networks can leverage feature superposition to encode more concepts than dimensions, but cross-feature interference constrains the linear acces
arXiv:2512.04305v3 Announce Type: replace Abstract: Vision-language models (VLMs) such as CLIP are increasingly adapted across decentralized data silos, yet the reliability of their predictions under