Knowledge catalogue
arXiv:2609.30157v1 Announce Type: cross Abstract: Future quantum networks will make use of entanglement to perform numerous tasks, such as sending quantum information over long distances, distributed
arXiv:2609.29973v1 Announce Type: cross Abstract: Generative recommendation reformulates item retrieval as sequence generation, allowing a unified model to directly generate the next item from a user'
arXiv:2603.05370v2 Announce Type: replace-cross Abstract: Causal structure learning from observational data is central to many scientific and policy domains, but the time series setting common to many
arXiv:2609.29000v1 Announce Type: new Abstract: Robot policies deployed in real environments naturally accumulate mixed-quality experience, including successful executions, partial progress, and failu
arXiv:2609.28877v1 Announce Type: cross Abstract: New words are invented every day. A human listener can learn a new word by hearing it clearly once and inferring its usage from sentence context. This
arXiv:2609.28603v1 Announce Type: cross Abstract: Recently, Large Language Models (LLMs) have been increasingly able to solve advanced mathematical problems, including many that have been open for dec
arXiv:2609.29802v1 Announce Type: new Abstract: Scientific ideation is increasingly mediated by large language models, but current ideation systems are usually trained and evaluated on immediately jud
arXiv:2609.29121v1 Announce Type: cross Abstract: Audio-Visual Semantic Segmentation (AVSS) aims to identify, segment, and classify sound-emitting objects in video frames. Previous Transformer-based A
arXiv:2609.29988v1 Announce Type: new Abstract: Synthetic data can scale training supervision when real-world data are limited, but noise and distribution mismatch can reduce its value. Existing synth
arXiv:2609.29383v1 Announce Type: new Abstract: Climate data downscaling is the task of increasing the spatial resolution of climate data, typically by generating fine-resolution regional climate data
arXiv:2609.29785v1 Announce Type: new Abstract: Accurate brain tumor segmentation from Magnetic Resonance Imaging is essential for diagnosis, treatment planning, and surgical guidance. Although Convol
arXiv:2609.29390v1 Announce Type: new Abstract: LLM evaluation is commonly performed either by prompting models to produce answers or by scoring candidate outputs with likelihood-based metrics. In mul
arXiv:2609.29625v1 Announce Type: new Abstract: Across seven public educational prediction datasets, three passed all four pre-modeling reliability checks; the remaining four either failed group-aware
arXiv:2604.01754v2 Announce Type: replace-cross Abstract: Mathematical reasoning is a hallmark of human intelligence, and whether large language models (LLMs) can meaningfully perform it remains a cen
arXiv:2609.30266v1 Announce Type: cross Abstract: Asynchronous monitoring, incident investigations, and compliance audits primarily rely on agent traces to reconstruct what happened. These analyses as
arXiv:2609.07746v2 Announce Type: replace Abstract: Even though backdoors in LLMs have been a growing concern, their inner workings are still under heavy scrutiny. Trigger-based backdoors are easy to
arXiv:2602.04489v2 Announce Type: replace Abstract: Temporarily ambiguous garden-path sentences ('While the team trained the striker wondered... ') are known to cause processing difficulty, which can
arXiv:2609.29672v1 Announce Type: new Abstract: Artificial intelligence helps education most where an essential provision has been rationed by cost. For language learners that provision is a teacher's
arXiv:2609.29581v1 Announce Type: new Abstract: Although diffusion-based methods have substantially improved the controllability of multimodal face synthesis, their semantic alignment remains suboptim
arXiv:2609.28851v1 Announce Type: new Abstract: Vision-language models (VLMs) achieve strong visual reasoning performance, yet subtle changes from routine image capture and processing can alter their
arXiv:2609.30012v1 Announce Type: cross Abstract: Language models advise people, keep them company, and write software while they sleep. Measuring what they do is hard: behavior has to be sampled repe
arXiv:2609.28684v1 Announce Type: new Abstract: Encoding input coordinates with sinusoidal functions into multi-layer perceptrons (MLPs) has proven effective for implicit neural representations (INRs)
arXiv:2609.28836v1 Announce Type: cross Abstract: While various multimodal methods combining imaging and tabular data for Alzheimer's disease (AD) diagnosis were proposed, they are often limited in ge
arXiv:2609.30056v1 Announce Type: cross Abstract: Robotic novel view synthesis (NVS) must recover both visual appearance and metric 3D structure, yet most generative NVS methods rely only on images, o
arXiv:2609.29409v1 Announce Type: cross Abstract: In this paper, a method for achieving exact unlearning for Gibbs supervised learning algorithms is proposed using a variational formulation inspired b
arXiv:2609.29644v1 Announce Type: new Abstract: Future orbital infrastructures, such as deployable antennas, solar farms, and large orbital platforms will require autonomous inspection systems able to
arXiv:2603.06471v3 Announce Type: replace Abstract: Acquiring per-frame annotations for ultrasound videos is costly and requires clinical expertise, limiting learning-based analysis. We study cross-vi
arXiv:2609.28562v1 Announce Type: cross Abstract: Aggregation theory has traditionally focused on operators defined over vectors. However, many applications-including Multi-Criteria Decision Making, G
arXiv:2602.11323v2 Announce Type: replace Abstract: Traditional monocular Visual-Inertial Odometry (VIO) systems struggle in low-texture environments where sparse visual features are insufficient for
arXiv:2609.29156v1 Announce Type: cross Abstract: Long-tailed chest X-ray classification requires visual representations that capture both common abnormalities and subtle, infrequent findings. We prop
arXiv:2603.26797v2 Announce Type: replace Abstract: Large language models are increasingly used to generate financial alpha signals, but many have memorized the historical data in their training corpo
arXiv:2609.29015v1 Announce Type: new Abstract: Decentralized LLM-based multi-agent systems coordinate through local interactions, but an agent can remain responsive while its task-solving quality per
arXiv:2609.28857v1 Announce Type: new Abstract: Scene text spotting remains challenging for arbitrarily shaped text instances such as curved signs and dense multi-oriented characters in natural images
arXiv:2609.29941v1 Announce Type: new Abstract: Many real-world optimization problems rely on expensive simulations or experiments, making the efficient use of available data essential. Multi-fidelity
arXiv:2609.29913v1 Announce Type: new Abstract: Many-shot in-context learning (ICL) enables large language models (LLMs) to adapt to complex tasks by conditioning on thousands of demonstration example
arXiv:2609.29779v1 Announce Type: new Abstract: Vessel segmentation is commonly optimized as voxel-wise classification, but small local errors can strongly disrupt vascular connectivity while having l
arXiv:2609.29940v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) achieve strong performance on visual reasoning tasks, yet remain prone to hallucinations and over-reliance on
arXiv:2609.30218v1 Announce Type: cross Abstract: Pre-logit steering adapts a frozen language model to a test-time reward by adding vectors to its final hidden states. Unregularized reward optimizatio
arXiv:2510.27065v2 Announce Type: replace Abstract: We present MLPerf Automotive, the first standardized public performance benchmark for evaluating Machine Learning systems that are deployed for AI a
arXiv:2609.29112v1 Announce Type: new Abstract: Industrial robots are rarely used for machining tasks due to their limited path accuracy. This accuracy is mainly limited by inaccuracies in the drive t
arXiv:2609.29863v1 Announce Type: new Abstract: Event Vision Sensing (EVS) report threshold crossings of log-irradiance, so a static optical system imaging a static scene produces no output at all. Th
arXiv:2609.29573v1 Announce Type: new Abstract: Text-to-SQL systems are increasingly deployed on production databases, where queries that pass benchmark evaluation can still produce results that disto
arXiv:2609.28793v1 Announce Type: new Abstract: Mapping the distribution of traffic dynamics at high spatiotemporal resolution is a fundamental question in transportation research. Distributed acousti
arXiv:2603.04756v3 Announce Type: replace Abstract: MOOSEnger is a modeling and simulation AI agent framework for the Multiphysics Object-Oriented Simulation Environment (MOOSE) ecosystem, built aroun
arXiv:2609.29386v1 Announce Type: new Abstract: The partial label ranking problem is a supervised learning scenario that aims to fit a preference model that predicts a bucket order defined over a set
arXiv:2609.29908v1 Announce Type: cross Abstract: Neural models can learn to generate various solutions to the inverse kinematics problem from data, but are usually limited to a single robot. We prese
arXiv:2609.28660v1 Announce Type: new Abstract: Human hand-object interactions (HOIs) provide a rich source of demonstrations for dexterous manipulation, but learning directly from them presents chall
arXiv:2609.29603v1 Announce Type: cross Abstract: Social virtual reality (VR) creates a paradox. A user's body motion is a high-entropy biometric: head and hand trajectories alone re-identify users am
arXiv:2609.28956v1 Announce Type: new Abstract: Recent advances in video object segmentation with Multimodal Large Language Model (MLLM) reasoning have demonstrated the effectiveness of using a single
arXiv:2609.30104v1 Announce Type: cross Abstract: High Performance Computing (HPC) and Quantum Computing (QC) systems are increasingly converging towards unified High Performance Computing-Quantum Com
arXiv:2609.29044v1 Announce Type: cross Abstract: Pilot-aided channel estimation is a decisive block in orthogonal frequency-division multiplexing (OFDM) receivers for both 5G New Radio (5G-NR) and Lo
arXiv:2609.29958v1 Announce Type: cross Abstract: We study a matching mechanism where agents and objects are described by features rather than complete rankings. A single spectral projection reduces t
arXiv:2609.29855v1 Announce Type: cross Abstract: We address the challenge of syntactic parsing for Urdu, a morphologically rich language, and present state-of-the-art results for both constituency an
arXiv:2503.02781v3 Announce Type: replace-cross Abstract: Predicting clinical outcomes from preclinical data is essential for selecting safe and effective drug combinations and for reducing late-stage
arXiv:2609.28860v1 Announce Type: new Abstract: Textual descriptions can reduce ambiguity in medical image segmentation by specifying the finding and location to be delineated. Existing text-guided me
arXiv:2609.30130v1 Announce Type: cross Abstract: Current vision-language models (VLMs) excel at visual content understanding and text-based reasoning, yet their structure limits the advancement of in
arXiv:2603.10287v2 Announce Type: replace-cross Abstract: LLM-as-a-Judge is a flexible framework for text evaluation, which allows us to obtain scores for the quality of a given text from various pers
arXiv:2609.29682v1 Announce Type: new Abstract: Named Entity Recognition (NER) is a core NLP task, but transformer-based sentence-level models struggle with long documents because of fixed input-lengt
arXiv:2602.08329v2 Announce Type: replace-cross Abstract: A core bottleneck in large language model (LLM) inference is the cost of attending over the ever-growing key-value (KV) cache. Although near-o
arXiv:2609.29413v1 Announce Type: new Abstract: Sampling from Boltzmann distributions of molecular systems is an inference problem that has seen significant recent developments fuelled by advances in