Knowledge catalogue
arXiv:2609.10702v1 Announce Type: new Abstract: Learning from limited text requires models to use context, generalize to new inputs, and retain useful capabilities. Qiushi Engine conducted a long-hori
arXiv:2609.11917v1 Announce Type: cross Abstract: As the supply of human-written text is exhausted, it has become standard practice to repeat language model training data. Prior work has studied data
arXiv:2609.11376v1 Announce Type: cross Abstract: We introduce an amortized neural sampler that combines operator learning with flow methods for sampling. It maps SDE coefficient functions to pushforw
arXiv:2609.10830v1 Announce Type: new Abstract: When a language model finds a sentence unusually cheap to predict, it is tempting to conclude that the sentence was in its training data. Almost every p
arXiv:2609.11777v1 Announce Type: cross Abstract: Clinical electroencephalography (EEG) data are valuable for healthcare research and for developing artificial intelligence (AI)-based clinical decisio
arXiv:2609.11913v1 Announce Type: new Abstract: Out-of-distribution length generalization, namely to extrapolate a task from short to longer context, has been studied intensively for transformers. Her
arXiv:2609.09475v1 Announce Type: cross Abstract: Existing physical-layer authentication (PLA) schemes for non-terrestrial networks (NTNs) often rely on single-anchor verification, lack joint authenti
arXiv:2609.10993v1 Announce Type: new Abstract: Multilingual large language models (mLLMs) contain a small fraction of feed-forward neurons that are sensitive to particular languages, commonly termed
arXiv:2507.06560v5 Announce Type: replace Abstract: Recent success in contrastive learning has sparked growing interest in more effectively leveraging multiple augmented views of data. While prior met
arXiv:2609.11210v1 Announce Type: cross Abstract: The discovery of the EML operator, sufficient to evaluate the standard explicit purely transcendental elementary functions, has led to considerable in
arXiv:2404.13381v2 Announce Type: replace Abstract: The COVID19 pandemic had enormous economic and societal consequences. Contact tracing is an effective way to reduce infection rates by detecting pot
arXiv:2609.10893v1 Announce Type: new Abstract: Recent advances in large language models have transformed human-computer interaction. Despite their fluency, these models often produce texts that are g
arXiv:2609.10796v1 Announce Type: new Abstract: Pretrained diabetic retinopathy (DR) prediction models differ in their input fields, serialization formats, preprocessing requirements, and output seman
arXiv:2508.07769v3 Announce Type: replace Abstract: The synthesis of spatiotemporally coherent 4D content presents fundamental challenges in computer vision, requiring simultaneous modeling of high-fi
arXiv:2609.11334v1 Announce Type: new Abstract: Natural Language Inference processes pairs of sentences to extract their semantic relations. NLI has been a hot research topic, integrated as a main com
arXiv:2609.10952v1 Announce Type: cross Abstract: Data poisoning corrupts training data to degrade a model or to plant attacker-controlled behavior. This study evaluates two representative training-ti
arXiv:2609.11865v1 Announce Type: new Abstract: Truth and evidence-based communication provide important foundations for democratic governance, accountability, and collective decision-making. Prior wo
arXiv:2112.04169v3 Announce Type: replace-cross Abstract: We consider online resource allocation under a typical non-profit setting, where limited or even scarce resources are administered by a not-fo
arXiv:2609.11331v1 Announce Type: new Abstract: Cyber-Physical Systems (CPS) are commonly represented through multiple interconnected models. During development, CPS consistency requires that shared m
arXiv:2609.11872v1 Announce Type: cross Abstract: Continuous glucose monitoring (CGM) provides high-frequency measurements of glucose dynamics and enables short-term glucose forecasting for diabetes m
arXiv:2609.11920v1 Announce Type: new Abstract: Reliable relative localization between aerial and ground robots is a key requirement for tightly coordinated heterogeneous teams. This can be difficult
arXiv:2609.10551v1 Announce Type: cross Abstract: Peer-to-peer energy trading among electric vehicles (EVs) can improve charging flexibility under limited charging infrastructure, but effective EV--EV
arXiv:2609.11860v1 Announce Type: cross Abstract: Energy consumption forecasting relies on increasingly complex machine learning (ML) models, such as Genetic Programming-based symbolic regressors, who
arXiv:2609.09980v1 Announce Type: cross Abstract: High Performance Computing-Quantum Computing (HPCQC) platforms expose multiple Quantum Processing Units (QPUs) that may differ in size, topology, nati
arXiv:2609.11192v1 Announce Type: new Abstract: Soft context compression condenses a context into a few memory tokens that a frozen LLM consumes in place of the raw text, but existing compressors fix
arXiv:2609.10863v1 Announce Type: new Abstract: Continuous and discrete flow matching are usually treated as separate constructions. This paper identifies a duality between them: projecting continuous
arXiv:2609.11128v1 Announce Type: new Abstract: In disinformation datasets, narratives are often understood as recurring interpretive patterns that group texts under narrative labels. Recent work form
arXiv:2609.09625v1 Announce Type: new Abstract: As Digital Twin (DT) systems evolve beyond state synchronization toward task-oriented and knowledge-driven operation, Cognitive Digital Twins (CDTs) hav
Fugu Max pushes the Pareto Frontier for price-performance: • 2 input / 6 output per 1M tokens. • Performance within striking distance of elite models at 2x to 6x lower cost. • Best overall score on 6
arXiv:2609.11059v1 Announce Type: new Abstract: Passive mechanical interfaces offer a lightweight alternative to actuated manipulators for quadruped payload carrying, but their impedance directly coup
arXiv:2609.11918v1 Announce Type: new Abstract: Generalization under distribution shift remains a core challenge in modern machine learning, yet existing learning bound theory is limited to narrow, id
arXiv:2506.00849v2 Announce Type: replace Abstract: Despite the empirical success of Diffusion Models (DMs) and Variational Autoencoders (VAEs), their generalization performance remains theoretically
arXiv:2609.11915v1 Announce Type: cross Abstract: Generative artificial intelligence changes how firms reach customers, but standard marketing data do not record how often users see and notice a firm'
arXiv:2609.11689v1 Announce Type: cross Abstract: Area-based social risk indices summarize residents' socioeconomic conditions but incompletely capture physical features of place that may affect healt
arXiv:2609.11361v1 Announce Type: new Abstract: Reconfigurable robots can change their contact geometry when a fixed body cannot negotiate an obstacle. A variable-geometry truss (VGT) distributes this
arXiv:2609.09306v1 Announce Type: new Abstract: This paper investigates the hypothesis that the first-order structure of physical interactions, i.e. gradients or Jacobians, characterizes the structure
arXiv:2609.11312v1 Announce Type: new Abstract: Lung cancer causes more deaths than any other malignancy, and low-dose CT screening is the main pathway to early diagnosis. That pathway hinges on the s
arXiv:2609.11886v1 Announce Type: new Abstract: High-resolution digital surface models (DSMs) play an important role in urban analysis, 3D building reconstruction, and infrastructure monitoring, yet t
arXiv:2609.11173v1 Announce Type: new Abstract: Despite its ubiquity, clustering lacks a universally accepted definition of what is a cluster. Kleinberg's Impossibility Theorem formalizes this difficu
arXiv:2609.11132v1 Announce Type: new Abstract: Accurate posterior prediction need not require accurate approximation of Bayesian updates. We prove that an unbounded gap between the update maps can co
arXiv:2609.11606v1 Announce Type: cross Abstract: Identifiability of tensor decompositions is often established through linear-algebraic conditions on the factor families. For nonnegative decompositio
arXiv:2603.11298v3 Announce Type: replace Abstract: High dynamic range (HDR) novel view synthesis (NVS) aims to reconstruct HDR scenes from multi-exposure low dynamic range (LDR) images. Existing HDR
arXiv:2609.10298v1 Announce Type: cross Abstract: Cyberattacks against Operational Technology (OT) systems, which monitor and control industrial processes, pose an increasing threat to essential socie
arXiv:2609.11656v1 Announce Type: new Abstract: The transition from file storage to database management systems transformed stored data into managed resources. AI now faces an analogous transition fro
arXiv:2609.11216v1 Announce Type: new Abstract: A wrong answer does not show whether the model lacked the needed information or held it and failed to use it. On an entity-obligation binding task, a la
arXiv:2510.15685v2 Announce Type: replace Abstract: This paper investigates the use of an LLM to generate auxiliary background context for social media posts, and explores four methods to incorporate
arXiv:2609.10748v1 Announce Type: new Abstract: This paper develops an arbitrary-order kinematic construction that links serial propagation, parallel-mechanism closure, and rigid-platform point fields
arXiv:2609.11639v1 Announce Type: new Abstract: The energy transition is reshaping residential electricity consumption through the increasing adoption of distributed generation, electrified appliances
arXiv:2603.15814v2 Announce Type: replace Abstract: Longitudinal mammography screening has become an important source of information for improving future breast cancer risk prediction. However, the pe
arXiv:2609.10954v1 Announce Type: new Abstract: Continual world models must decide whether new data justify changing the model. Fixed replay schedules and prediction-error triggers specify when to upd
arXiv:2609.11041v1 Announce Type: new Abstract: No Free Lunch theorem implies that any performance gains achieved by a classifier on a particular image distribution are necessarily offset by a loss of
arXiv:2609.10749v1 Announce Type: new Abstract: Accurate plant growth estimation is essential for greenhouse monitoring, yet obtaining labeled data remains costly and time-consuming. To address this,
arXiv:2609.10743v1 Announce Type: new Abstract: Monocular 3D hand and body mesh recovery often suffers from severe occlusion and ambiguity. Traditional deterministic methods typically regress a single
arXiv:2609.11317v1 Announce Type: new Abstract: Iterative reference-conditioned image editing can introduce grid-like and granular textures, commonly described as digital ripple. We present Mi-Ripple,
arXiv:2508.05830v3 Announce Type: replace Abstract: Large Language Model (LLM) studies that use language responses elicited from depression assessments to predict scores on those same assessments ofte
arXiv:2609.11842v1 Announce Type: new Abstract: Diffusion and flow-matching schedules control the signal and noise coefficients that mix data and noise along affine probability paths. Minimizing a kin
arXiv:2609.11338v1 Announce Type: new Abstract: Closed kinematic chains complicate modular modeling by coupling active and passive coordinates through nonlinear closure constraints. This paper present
arXiv:2609.10664v1 Announce Type: cross Abstract: Language models leave a distinctive vocabulary in the prose they help write, and we measure how much of the astronomy literature now carries it. From
arXiv:2609.10545v1 Announce Type: cross Abstract: Federated Learning (FL) enables distributed model training without centralizing data but suffers from high communication overhead. To address this, we
arXiv:2609.11655v1 Announce Type: new Abstract: Muon has emerged as a highly effective optimizer for large language model training, often achieving superior convergence and performance compared with t