Knowledge catalogue
arXiv:2609.34670v1 Announce Type: cross Abstract: In this paper, we propose a representation of a digraph (directed graph) as a Hermitian matrix derived from its adjacency matrix. This representation
arXiv:2609.32789v1 Announce Type: new Abstract: Large-scale point cloud representations of complex geome tries incur prohibitive computational and memory costs, necessitating compressed implicit repre
arXiv:2609.33637v1 Announce Type: cross Abstract: In this paper, we propose a hierarchical multi-agent reinforcement learning framework for coordinating robot teams in warehouse environments under com
arXiv:2609.33607v1 Announce Type: new Abstract: Pretrained graph-text models align graph representations with textual semantics, enabling recognition of unseen classes and transfer across graph domain
arXiv:2609.31726v1 Announce Type: new Abstract: Collaborative medical AI platforms allow researchers to train models on sensitive imaging data while restricting data export. However, trained models ca
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:2609.32195v1 Announce Type: cross Abstract: Stochastic gradient descent (SGD) is widely used to train machine learning models, but subsampling the training data introduces noise into its updates
arXiv:2609.33570v1 Announce Type: new Abstract: Group-relative policy optimization (GRPO) makes learner-side activations a major memory-computation bottleneck: gradient checkpointing reduces activatio
arXiv:2609.32213v1 Announce Type: new Abstract: Agent performance depends on both the underlying model and the harness that manages its tool use and execution. Selecting a suitable pair requires accou
arXiv:2609.34674v1 Announce Type: new Abstract: Learning from demonstration (LfD) has enabled humanoid robots to acquire diverse whole-body skills, but extending this paradigm to human-object interact
arXiv:2609.32426v1 Announce Type: new Abstract: For graph node classification, calibrated class probabilities are needed when confidence scores, usually the maximum predicted class probability, are us
arXiv:2604.04204v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly embedded in educational, professional, and public infrastructure, yet widely used platforms expo
arXiv:2509.25637v2 Announce Type: replace Abstract: Preconditioning is widely used in machine learning to accelerate convergence on the empirical risk, yet its role on the expected risk remains undere
arXiv:2609.35457v1 Announce Type: new Abstract: Most modern multimodal large language models (MLLMs) build on a pretrained visual encoder that provides a strong visual prior. Encoder-free MLLMs instea
arXiv:2609.33093v1 Announce Type: new Abstract: Linear attention replaces the growing key--value (KV) cache of standard attention with a fixed-size recurrent state, substantially reducing memory growt
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:2609.32109v1 Announce Type: new Abstract: We study whether benchmarks reliably guide model selection as developers adapt to evaluation feedback across multiple criteria. We find that the worst-c
arXiv:2609.33936v1 Announce Type: new Abstract: Recent research argues that language models exhibit pronounced homogeneity in open-ended generation, framing such behavior as an Artificial Hivemind tha
arXiv:2609.33482v1 Announce Type: new Abstract: Conformal prediction provides distribution-free finite-sample marginal coverage, but post-hoc calibration data may be too scarce to learn how uncertaint
arXiv:2609.35751v1 Announce Type: new Abstract: Looped Transformers reuse one block of layers several times: by spending extra computation they push a model of fixed size further, and so use its param
arXiv:2609.34514v1 Announce Type: cross Abstract: As large language models (LLMs) become increasingly widespread, preventing unsafe responses to harmful prompts is essential for their safe deployment.
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:2603.27998v3 Announce Type: replace-cross Abstract: Individualized head-related impulse responses (HRIRs) enable binaural rendering, but dense per-listener measurements are costly. We address HR
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:2609.32346v1 Announce Type: new Abstract: This paper compares three human motion prediction methods - GMM-based clustering, TransFusion, and Graph-Mixer - evaluating their effectiveness in human
arXiv:2609.34430v1 Announce Type: new Abstract: Accurate retrieval of human motions is a crucial first step in text-guided human motion modeling and synthesis, as it selects semantically relevant sequ
arXiv:2609.31840v1 Announce Type: new Abstract: High-speed racket sports provide a demanding testbed for humanoid robots, requiring time-critical decisions, precise striking, and dynamic whole-body co
arXiv:2609.35709v1 Announce Type: new Abstract: Vision-language-action (VLA) models using discrete action tokens have proven effective for controling robotic arms on manipulation tasks. For a humanoid
arXiv:2609.33096v1 Announce Type: new Abstract: Rapid advances in humanoid robotics have motivated growing interest in the application of humanoids for healthcare and clinical tasks. However, it remai
arXiv:2607.09710v2 Announce Type: replace Abstract: Tabular prediction is central to a wide range of real-world applications. Tabular data typically contain heterogeneous features as well as rich and
arXiv:2609.34396v1 Announce Type: new Abstract: Hyperspectral video provides material cues that can disambiguate targets with similar false-color appearance, yet foundation-model trackers update memor
arXiv:2609.32276v1 Announce Type: new Abstract: Multi-label classification (MLC) requires predicting multiple relevant labels for each instance, where a central challenge is modeling complex label dep
arXiv:2609.33986v1 Announce Type: new Abstract: In some multi-agent systems, the quantity to be optimized is not an externally specified reward but the information acquired about unknown properties of
arXiv:2609.33812v1 Announce Type: cross Abstract: Repeated runs of the same coding agent are known to give different benchmark scores. We ask what that variation means for a team running an agent on i
arXiv:2609.31993v1 Announce Type: cross Abstract: We study how well gravitational-wave phase deviations can be identified using a neural network with classification and regression heads. The classific
arXiv:2506.18078v2 Announce Type: replace-cross Abstract: We propose a novel nonparametric regression method that models complex input-output relationships as the sum of convex and concave components.
arXiv:2609.35379v1 Announce Type: new Abstract: Electroencephalography (EEG) records mixtures of brain-source activity. Even with a known anatomical forward model, experiments that excite only part of
arXiv:2609.33183v1 Announce Type: cross Abstract: Large Language Models (LLMs) suffer from temporal misalignment, often due to the contradictory nature of their training corpora. While current mitigat
arXiv:2609.33372v1 Announce Type: cross Abstract: A special semimartingale admits a unique decomposition X=X_0+M+A into a local martingale M and a predictable finite-variation part A. We consider the
arXiv:2609.33131v1 Announce Type: new Abstract: Black-box Optimization (BO) is a powerful framework for optimizing expensive objective functions or unknown functions with a limited number of evaluatio
arXiv:2609.35726v1 Announce Type: new Abstract: Automated quality assessment of rehabilitation exercises relies heavily on accurate human pose estimation from video data. Although numerous RGB-based p
arXiv:2110.15075v2 Announce Type: replace Abstract: Estimating causal effects from observational data informs us about which factors are important in an autonomous system, and enables us to take bette
arXiv:2609.35512v1 Announce Type: new Abstract: Self-training generative models - the continued improvement of a model using its own outputs - is becoming increasingly important as high-quality traini
arXiv:2609.35748v1 Announce Type: cross Abstract: Looped transformers have demonstrated promising parameter efficiency by reusing layers for latent computation. Prior studies compare looped and non-lo
arXiv:2609.33038v1 Announce Type: cross Abstract: We propose DAST (Diversifying Arithmetic Sampling with TokenTour), a method that increases the diversity of LLM outputs without any change to the marg
arXiv:2609.32882v1 Announce Type: new Abstract: We present VSA2, a frontier trainable sparse attention for video DiTs. VSA2 includes a variety of new architectural features and training procedures tha
arXiv:2609.32540v1 Announce Type: new Abstract: Few-step autoregressive video diffusion generates a long video by splitting the video into temporal chunks and generating chunk-by-chunk, each through a
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:2609.33523v1 Announce Type: new Abstract: We present In-Token Learning, an image restoration framework that adapts a pretrained diffusion transformer using conditional rectified flow matching. C
arXiv:2609.35390v1 Announce Type: new Abstract: Verbal feedback can identify errors and prescribe corrections, providing rich supervision for language-model post-training even when reliable programmat
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:2609.34798v1 Announce Type: cross Abstract: Recent medical multimodal models have benefited from larger corpora, broader modality coverage, and stronger reasoning-oriented training, yet effectiv
arXiv:2609.35743v1 Announce Type: new Abstract: World-space hand motion estimation from egocentric video requires recovering 3D articulated hand geometry while tracking camera egomotion. Existing appr
arXiv:2609.33286v1 Announce Type: new Abstract: Multimodal foundation models edit natural photographs at production quality, yet the same models struggle with structured visual content such as infogra
arXiv:2609.31643v1 Announce Type: new Abstract: A principal who deploys a binary classifier with an abstention option must decide which queries the mechanism abstains on. The right choice depends on t
arXiv:2609.34731v1 Announce Type: cross Abstract: We develop an information-theoretic framework for generalization in next-token prediction under temporally dependent data. We consider independent tra
arXiv:2609.33288v1 Announce Type: new Abstract: Embodied Question Answering (EQA) requires agents to answer natural language questions about surrounding environments from visual observations. In this
arXiv:2609.33647v1 Announce Type: new Abstract: Many indoor environments in which robots operate, such as warehouses, offices, and hospitals, already have cameras installed. They observe parts of the
arXiv:2609.35516v1 Announce Type: new Abstract: Inspection planning seeks a minimum-length collision-free robot tour that observes a given set of points of interest (POIs). Sampling-based methods redu
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)