Knowledge catalogue
arXiv:2609.11749v1 Announce Type: cross Abstract: We investigate mean-variance portfolio selection with an ell_0-penalty to promote sparsity in asset allocations. Uncertainty in the mean return vector
arXiv:2609.11799v1 Announce Type: cross Abstract: Large language models are often fine-tuned, shared, or downloaded from third parties, so a deployed model may carry a hidden backdoor that behaves nor
arXiv:2506.08949v2 Announce Type: replace Abstract: In the era of information explosion, efficiently leveraging large-scale unlabeled data while minimizing the reliance on high-quality pixel-level ann
arXiv:2606.08297v2 Announce Type: replace-cross Abstract: We provide a strategic foundation for information: in any given game with incomplete information we define strategic quotients as information
arXiv:2501.12299v3 Announce Type: replace-cross Abstract: Gaussian Mixture Models (GMMs) range among the most frequently used models in machine learning. However, training large, general GMMs becomes
arXiv:2609.11414v1 Announce Type: new Abstract: Large language models exhibit complementary strengths, motivating routing methods that dispatch each query to the most suitable model. Although existing
arXiv:2609.11081v1 Announce Type: new Abstract: Science-inspired vision models show that explicit propagation dynamics can provide structured and interpretable alternatives to conventional token mixin
arXiv:2609.09242v1 Announce Type: cross Abstract: Large Language Models (LLMs) often generate natural-language comments while writing code, and these comments become part of the context used to genera
arXiv:2609.10746v1 Announce Type: cross Abstract: The growing reliance on Low-Earth Orbit (LEO) satellite communication systems has increased the need for intelligent methods capable of detecting cybe
arXiv:2509.25741v3 Announce Type: replace-cross Abstract: Test-time training (TTT) enhances model performance by explicitly updating designated parameters prior to each prediction to adapt to the test
arXiv:2609.11247v1 Announce Type: new Abstract: Multimodal Sentiment Analysis (MSA) remains constrained by modality imbalance, yet the field continues to rely on optimization-based balancing methods t
arXiv:2609.11873v1 Announce Type: cross Abstract: Recursive self-improvement (RSI) enables AI systems to turn experience and feedback into persistent changes that improve both their capabilities and t
arXiv:2502.07891v3 Announce Type: replace-cross Abstract: For two causal structures with the same set of visible variables, one is said to observationally dominate the other if the set of distribution
arXiv:2609.11326v1 Announce Type: cross Abstract: The undecidability of a program's static semantic properties is governed by Rice's theorem. Self-modifying systems, however, require analysing not whe
arXiv:2609.09560v1 Announce Type: cross Abstract: This study evaluates Vibe Coding, an emerging AI-led conversational programming paradigm that enables developers to generate software through natural-
arXiv:2609.11770v1 Announce Type: new Abstract: Large language models are superseded every few quarters; clinical evidence takes years. We asked whether medical research is keeping pace with the syste
arXiv:2609.10981v1 Announce Type: new Abstract: Bakhtiari, Lattimore and Szepesvari (COLT 2025) proved that Thompson sampling (TS) has Bayesian regret ilde O(d^{5/2}sqrt n) for bandit convex optimisat
arXiv:2001.03346v4 Announce Type: replace-cross Abstract: We propose a novel framework for learning time-varying graphs from spatiotemporal measurements. Given an appropriate prior on the temporal beh
arXiv:2609.11014v1 Announce Type: new Abstract: Long-horizon goal-conditioned reinforcement learning delegates control to a high-level module that proposes subgoals, but existing subgoals are implicit
arXiv:2609.11040v1 Announce Type: new Abstract: The fusion of hyperspectral (HS) and Light Detection and Ranging (LiDAR) data plays a crucial role in enhancing land-cover classification by jointly exp
arXiv:2609.11223v1 Announce Type: new Abstract: Recovering clean 3D scenes from hazy multi-view images is challenging because haze attenuates scene radiance and introduces atmospheric scattering. Rece
arXiv:2609.11506v1 Announce Type: new Abstract: Three-dimensional ultrasound (US) is a safe, radiation-free complementary modality to CT and X-rays for longitudinal monitoring, yet its segmentation-de
arXiv:2609.11875v1 Announce Type: new Abstract: Recent advances in Vision-Language-Action (VLA) models have improved robotic manipulation, yet observation-to-action learning remains limited by a funda
arXiv:2609.10934v1 Announce Type: new Abstract: Turn-taking is a fundamental mechanism that governs when interlocutors speak and listen. Although Spoken Dialogue Systems (SDS) exploit a range of lingu
arXiv:2609.11638v1 Announce Type: new Abstract: We present Vidu S2, which comprises Vidu S2-Avatar, a real-time interactive digital-character model, and Vidu S2-Editing, a real-time video editing mode
arXiv:2602.17655v3 Announce Type: replace Abstract: Language Identification (LID) is an important component of many multilingual natural language processing pipelines, where it facilitates corpus cura
arXiv:2609.10726v1 Announce Type: new Abstract: Hazardous robotic exploration requires robots to map spatial risks, such as unsafe terrain, radiation, fire, mines, or structural damage, while operatin
arXiv:2609.11716v1 Announce Type: cross Abstract: Post-training quantization compresses large language models (LLMs) by storing their weights at reduced precision, and each quantized weight introduces
arXiv:2609.10643v1 Announce Type: new Abstract: Natural load-bearing patterns such as leaf venation, trabecular bone, and spider webs achieve high stiffness per unit mass, yet classical topology optim
arXiv:2609.11642v1 Announce Type: cross Abstract: Neural audio codecs are a fundamental component of modern speech generation systems. While recent codecs achieve increasingly low bitrates, reducing f
arXiv:2609.09457v1 Announce Type: cross Abstract: Quantum state tomography is a fundamental technique for estimating the state of a quantum system from measured data and plays a crucial role in evalua
arXiv:2603.15038v2 Announce Type: replace-cross Abstract: A novel convolutional autoencoder and neural ODE (CAE-NODE) framework is proposed for a reduced-order model (ROM) applied to transient 2D coun
arXiv:2609.10299v1 Announce Type: new Abstract: Sparse autoencoders (SAEs) are increasingly used to recover interpretable features from neural-network activations, yet systematic feature co-occurrence
arXiv:2609.07075v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) is commonly evaluated by whether the final answer is correct. That test is insufficient: an answer can match its re
arXiv:2505.23215v2 Announce Type: replace-cross Abstract: We propose a framework for generative modeling of continuous-time processes from irregularly and asynchronously recorded data. It is based on
A person with mid single digit years of industry experience and months at one company and negligible external footprint quits that job voluntarily and ends up on a press tour that would be the envy of
arXiv:2609.10529v1 Announce Type: new Abstract: We prove the gap-entropy conjecture for fixed-confidence best-arm identification with independent unit-variance Gaussian arms, means in [0,1], and a uni
arXiv:2609.09986v1 Announce Type: cross Abstract: Consistent submodular maximization studies the tradeoff between solution quality and stability when elements arrive over time. For a monotone submodul
arXiv:2609.06050v1 Announce Type: cross Abstract: For a compact set KsubsetC, let artheta(K) be the infimum of the planar areas of the unit lemniscates of all monic polynomials with zeros in K, allowi
arXiv:2609.09211v1 Announce Type: cross Abstract: The Davis-Kahan theorem is a fundamental tool in spectral analysis, providing quantitative control over the distance between the eigenspaces of a symm
arXiv:2609.06579v1 Announce Type: cross Abstract: Machine learning methods, and especially neural networks, are now routinely used for malware detection in network traffic. Though very effective, syst
arXiv:2609.07334v1 Announce Type: new Abstract: The Asset Administration Shell (AAS) is a cornerstone of Industry 4.0 and the Digital Product Passport, providing standardized digital representations o
arXiv:2609.09380v1 Announce Type: new Abstract: Unlocking the potential of tiny aerial robots requires order of magnitude improvements in the performance of embedded edge control. In particular, altho
arXiv:2609.09257v1 Announce Type: new Abstract: Sensor-based AI systems are rarely operated under the conditions under which they were trained: devices, personnel and recording epochs change, and each
arXiv:2609.09511v1 Announce Type: cross Abstract: Physical-layer authentication (PLA) in non-terrestrial networks (NTNs) is challenged by severe Doppler shifts, long delays, and fast channel variation
arXiv:2609.08166v1 Announce Type: cross Abstract: The twenty-first century's transformative technology, artificial intelligence, is increasingly constrained by the twentieth century's transformative t
arXiv:2609.10428v1 Announce Type: cross Abstract: Algorithmic stability refers to the property of an algorithm being insensitive to perturbations of the input data, where the type of perturbation may
arXiv:2609.10012v1 Announce Type: new Abstract: Blood-brain barrier (BBB) permeability is a critical determinant in the development of central nervous system therapeutics because it directly influence
arXiv:2609.07102v1 Announce Type: cross Abstract: Astronomical spectra encode rich physical information, but drawing scientific conclusions from spectral features typically requires expert interpretat
arXiv:2609.09356v1 Announce Type: new Abstract: At Noora Health, our nurses answer more than 50,000 medical queries per month on our WhatsApp-based service that provides caregivers with on-demand supp
arXiv:2609.06879v1 Announce Type: cross Abstract: Steering vectors have rapidly emerged as a popular and effective method for guiding the output of LLMs in very specific ways. But constructing accurat
arXiv:2603.04199v2 Announce Type: replace-cross Abstract: Theoretical and applied research into privacy encompasses an incredibly broad swathe of differing approaches, emphases and aims. This work int
arXiv:2609.10026v1 Announce Type: new Abstract: Heart rate is a critical biomarker of health, and remote photoplethysmography (rPPG) enables its contactless estimation from video data for telemedicine
arXiv:2609.07050v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) critically depends on retrieving the evidence necessary for effective reasoning. However, this remains particularly
arXiv:2511.13893v2 Announce Type: replace Abstract: In differentially private (DP) tabular data synthesis, the consensus is that statistical models are better than neural network (NN)-based methods. H
arXiv:2609.10008v1 Announce Type: new Abstract: Composed video retrieval (CoVR) searches a gallery for the target video that realizes a natural-language modification of a source clip. However, at gall
arXiv:2609.07379v1 Announce Type: cross Abstract: Large language models (LLMs) have recently shown promise for historical entity linking, but preference optimization for this task is often formulated
arXiv:2609.10371v1 Announce Type: new Abstract: High-resolution water mapping supports environmental monitoring and related applications, but accurate pixel-level labels are difficult and costly to pr
arXiv:2603.08599v2 Announce Type: replace Abstract: Intelligent agents must reason over both continuous dynamics and discrete representations to generate effective plans in complex environments. Previ
arXiv:2609.08566v1 Announce Type: new Abstract: KV cache is evolving from a serving optimization into an external memory substrate for long-term LLM agents. In a shared multi-user deployment, however,