Knowledge catalogue
arXiv:2610.21852v1 Announce Type: new Abstract: Solving optimal control problems for high-dimensional mechanical systems subject to constraints remains challenging due to nonlinear dynamics and non-co
arXiv:2610.20843v1 Announce Type: new Abstract: Map generation tasks feature extensive non-structural vectorized data (e.g., points, polylines, and polygons) and thus pose significant challenges to co
arXiv:2610.21492v1 Announce Type: new Abstract: Language-model agents can accumulate interaction feedback, but reliably turning that feedback into reusable experience remains challenging. Existing met
arXiv:2607.18253v2 Announce Type: replace Abstract: Large language model (LLM) query routers typically assign each query to a suitable model that balances response accuracy and monetary cost. Subseque
arXiv:2610.21007v1 Announce Type: new Abstract: Data selection reallocates training support across visual concepts, including objects, attributes, color, texture, and backgrounds. Different allocation
arXiv:2610.22314v1 Announce Type: new Abstract: With people increasingly turning to AI chatbots for mental health support, the need to evaluate these tools is becoming more pressing. Recent benchmarks
arXiv:2610.22050v1 Announce Type: cross Abstract: Radio technosignature searches rely on machine learning detectors to shrink the candidate pool before a human looks at it. They are validated on held-
arXiv:2602.08901v2 Announce Type: replace Abstract: Large language models (LLMs) can memorize and reproduce training sequences verbatim, raising privacy and copyright concerns. Existing mitigation met
arXiv:2610.21703v1 Announce Type: new Abstract: Contrast-enhanced magnetic resonance imaging (CE-MRI) is crucial for accurately diagnosing and segmenting brain tumors. However, the acquisition of CE-M
arXiv:2610.21400v1 Announce Type: new Abstract: Rigid closed chains can admit indeterminate internal reactions even when their motion is fully specified. This paper develops a modular inverse-dynamics
arXiv:2610.20797v1 Announce Type: new Abstract: Reliable absolute heading estimation is critical for road-vehicle navigation, particularly when magnetic, inertial, or GNSS-based references are degrade
arXiv:2610.21679v1 Announce Type: new Abstract: Tokenization is a critical component of the language modeling pipeline, but it is poorly understood. Tokenizers can impact downstream performance, but t
arXiv:2607.08954v2 Announce Type: replace-cross Abstract: We study nonasymptotic convergence of primal-dual methods for a class of nonconvex constrained optimization problems under local multiplier re
arXiv:2610.22025v1 Announce Type: new Abstract: Semi-supervised learning (SSL) is particularly attractive in domains where labels are expensive to obtain, while unlabeled examples are readily availabl
arXiv:2610.22249v1 Announce Type: new Abstract: Recurring expressions in model-written text are often treated as signatures of machine authorship, yet most of them serve legitimate functions. Whether
arXiv:2610.21026v1 Announce Type: cross Abstract: In stochastic (strongly) convex optimization, establishing last-iterate convergence guarantees for first-order methods has recently garnered increasin
arXiv:2610.20674v1 Announce Type: cross Abstract: Flow maps are a recent method of probabilistic generation that amortize diffusion trajectories into one-step samplers, unlocking efficient generation.
arXiv:2610.21342v1 Announce Type: new Abstract: The task of constructing a predictor that is simultaneously accurate under multiple evaluation metrics has been popularized recently under the name omni
arXiv:2610.20782v1 Announce Type: new Abstract: The closed world asks AI for specialized competence and lets it manufacture the iid data its theory assumes: a fixed criterion licenses collecting examp
arXiv:2508.12834v2 Announce Type: replace-cross Abstract: Stochastic gradient descent (SGD), one of the most fundamental optimization algorithms in machine learning (ML), can be recast through a conti
arXiv:2610.20804v1 Announce Type: new Abstract: We study continual linear regression in a high-dimensional regime, where a learner observes a sequence of tasks and updates its parameters sequentially.
arXiv:2507.18903v2 Announce Type: replace-cross Abstract: Causal identification asks whether a causal target can be recovered from the observational distribution in an idealized infinite-data setting.
arXiv:2505.13142v3 Announce Type: replace Abstract: Layer Normalization (LN) is usually studied for its role in optimization, but it also acts as a multivariate nonlinearity between affine layers. We
arXiv:2610.22100v1 Announce Type: new Abstract: Byte-level language models such as the Byte Latent Transformer (BLT) run their large global model once per patch and start a patch where a small model's
arXiv:2504.21622v2 Announce Type: replace Abstract: This article proposes a new path planning method for addressing multi-level terrain situations. The proposed method includes innovations in three as
arXiv:2610.22358v1 Announce Type: new Abstract: Time series foundation models are becoming increasingly popular for general-purpose forecasting. However, most leading models predict marginal quantiles
arXiv:2610.22087v1 Announce Type: new Abstract: In pathology, pattern recognition relies on a frame of reference built through extensive training and experience, which may still fall short when encoun
arXiv:2610.22363v1 Announce Type: new Abstract: We introduce P^{3}-WAM, a unified world action model for egocentric dexterous manipulation that learns 3D perception, action-conditioned prediction, and
arXiv:2610.21295v1 Announce Type: new Abstract: Federated minimax problems, such as distributionally robust optimization and adversarial training, include an inner maximization variable built from eac
arXiv:2610.21451v1 Announce Type: new Abstract: We adapt the diffeomorphic coarse-to-fine LapIRN architecture for longitudinal whole-body PSMA PET/CT registration in the Learn2Reg 2026 PSMAReg challen
arXiv:2610.21493v1 Announce Type: new Abstract: Dense associative memory underlies modern attention mechanisms; however, Modern Hopfield Networks (MHNs) with linear kernels yield overlapping attractor
arXiv:2610.20973v1 Announce Type: new Abstract: Geo-foundation models (GFMs) are typically evaluated by downstream task predictive performance, revealing little about which physical features they are
arXiv:2610.20881v1 Announce Type: new Abstract: While foundation models and vision transformers (ViTs) have redefined visual representations, current active learning (AL) strategies for these backbone
arXiv:2610.22041v1 Announce Type: new Abstract: World-model planners evaluate candidate trajectories by predicting their future consequences, implicitly relying on the plan-to-motion relationship lear
arXiv:2610.21514v1 Announce Type: cross Abstract: When code-repair agents fail, final tests reveal that a patch is wrong but not which earlier commitment caused it or which software obligation failed.
arXiv:2610.21134v1 Announce Type: new Abstract: Predicting live birth before fresh embryo transfer could support shared decision-making in IVF/ICSI, but routine clinical data are small, heterogeneous,
arXiv:2610.22297v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) enhances the capabilities of large language models (LLMs) by querying external structured knowledge. However, it ca
arXiv:2602.02573v2 Announce Type: replace-cross Abstract: Modern neural architectures are largely developed as distinct empirical constructions, despite recurring principles across convolutional, atte
arXiv:2610.21870v1 Announce Type: cross Abstract: Risk-sensitive reinforcement learning (RL) addresses decision-making under uncertainty and adverse outcomes. Learning risk-sensitive policies from fix
arXiv:2610.20739v1 Announce Type: new Abstract: Quantile forecasts summarize uncertainty at each future time step, while diffusion forecasters represent it through sampled future trajectories. We stud
arXiv:2510.08667v3 Announce Type: replace-cross Abstract: Modern software teams frequently encounter delays in resolving recurring or related issues due to fragmented knowledge scattered across JIRA t
arXiv:2610.21336v1 Announce Type: cross Abstract: We study sparse and continual reinforcement learning fine-tuning of language models through attention-head ranking and partitioning. For sparse learni
arXiv:2610.20631v1 Announce Type: cross Abstract: Romance scam has become a serious problem via social media, and the improvement of artificial intelligence enable to fake identify easily. In the stud
arXiv:2610.22155v1 Announce Type: new Abstract: Real-time interaction requires agents to perceive evolving environments and act under temporal constraints, yet existing evaluations of real-time stream
arXiv:2606.09449v2 Announce Type: replace Abstract: Autoformalization (AF) requires both prover acceptance and fidelity to an informal source, but prover acceptance alone cannot establish fidelity, an
arXiv:2610.21557v1 Announce Type: cross Abstract: Preparing quantum states in bosonic qudits with SNAP--Displacement sequences requires both optimizing the gate parameters and deciding which Fock leve
arXiv:2610.20763v1 Announce Type: cross Abstract: Full-duplex spoken language models decide at every frame whether to take the floor, defining a conversational personality -- yet each set of weights e
arXiv:2610.21273v1 Announce Type: new Abstract: We study the problem of online learning of quantum states with logarithmic loss, which is a quantum generalization of online portfolio selection and has
arXiv:2610.21121v1 Announce Type: cross Abstract: Real-time mobile volumetric video enables immersive free-viewpoint experiences, but delivering high-quality 3D content under constrained bandwidth and
arXiv:2610.21287v1 Announce Type: new Abstract: Training-free object removal commonly redirects self-attention away from the target region, but on few-step distilled diffusion models this often leaves
arXiv:2602.05785v2 Announce Type: replace-cross Abstract: Generalizable image-based person re-identification (Re-ID) aims to recognize individuals across cameras in unseen domains without retraining.
arXiv:2610.22353v1 Announce Type: cross Abstract: Mixture-of-experts (MoE) models have become an important approach for scaling model parameters. Modern MoE architectures increasingly combine two desi
arXiv:2610.22051v1 Announce Type: cross Abstract: In the best-arm identification problem, an algorithm adaptively selects arms to find the arm of the largest mean reward. Successive Rejects (SR) is a
arXiv:2610.21042v1 Announce Type: cross Abstract: Systematic literature reviews follow the PRISMA process of screening scholarly works and extracting evidence from included studies. Deep research tool
arXiv:2610.20811v1 Announce Type: new Abstract: Generative gameplay requires more than immediate responses to player actions.Actions should have persistent consequences, while the generated world shou
arXiv:2610.20807v1 Announce Type: cross Abstract: Smartphones have become integral to modern life due to their diverse applications. However, the development of applications for these devices faces si
arXiv:2610.21709v1 Announce Type: cross Abstract: Robust covariance estimation under entry-wise contamination and missing data is challenging in various signal processing applications. Under the Stude
arXiv:2505.15737v3 Announce Type: replace Abstract: Reconstructing high-fidelity underwater scenes remains a challenging task due to light absorption, scattering, and limited visibility inherent in aq
arXiv:2610.21370v1 Announce Type: new Abstract: Neural PDE solvers commonly generate multi-step predictions through autoregressive rollout, recursively applying a learned evolution operator and feedin
arXiv:2610.21367v1 Announce Type: new Abstract: Test-time adaptation (TTA) mitigates the performance degradation of source-pretrained models on unlabeled test data under distribution shifts. Recent ba