Knowledge catalogue
arXiv:2604.08641v1 Announce Type: cross Abstract: Interpretation is essential to deciphering the language of art: audiences communicate with artists by recovering meaning from visual artifacts. Howeve
arXiv:2509.25214v3 Announce Type: replace-cross Abstract: As increasingly large pre-trained models are released, deploying them on edge devices for privacy-preserving applications requires effective c
arXiv:2602.01997v2 Announce Type: replace-cross Abstract: Recent work has shown that layer pruning can effectively compress large language models (LLMs) while retaining strong performance on classific
arXiv:2604.09430v1 Announce Type: cross Abstract: Text embeddings are central to modern information retrieval and Retrieval-Augmented Generation (RAG). While dense models derived from Large Language M
arXiv:2604.09202v1 Announce Type: cross Abstract: Cloud providers must assign heterogeneous compute resources to workflow DAGs while balancing competing objectives such as completion time, cost, and e
arXiv:2604.08579v1 Announce Type: cross Abstract: We study cross-modal alignment between independently pretrained vision (DINOv2) and language (all-MiniLM-L6-v2) encoders using the functional map fram
arXiv:2604.09156v1 Announce Type: new Abstract: Parallel kinematics machines (PKM) can exhibit kinematic as well as actuation redundancy. While the meaning of kinematic redundancy has been clarified a
arXiv:2604.08787v1 Announce Type: new Abstract: This paper proposes a common interface for real-time low-level motion planning of collaborative robotic arms, aimed at enabling broader applicability an
arXiv:2604.09303v1 Announce Type: cross Abstract: This paper presents an online intention prediction framework for estimating the goal state of autonomous systems in real time, even when intention is
arXiv:2604.08969v1 Announce Type: cross Abstract: This paper introduces a projected functional gradient descent algorithm (P-FGD) for training nonparametric additive quantile regression models in onli
arXiv:2604.09480v1 Announce Type: new Abstract: We present Online3R, a new sequential reconstruction framework that is capable of adapting to new scenes through online learning, effectively resolving
arXiv:2604.08601v1 Announce Type: new Abstract: The rise of autonomous AI agents exposes a fundamental flaw in API-centric architectures: probabilistic systems directly execute state mutations without
arXiv:2604.08759v1 Announce Type: cross Abstract: This paper considers the problem of multi-bit generative watermarking for large language models under a worst-case false-alarm constraint. Prior work
arXiv:2512.03730v2 Announce Type: replace-cross Abstract: Adversarial perturbations are a useful way to expose vulnerabilities in object detectors. Existing perturbation methods are frequently white-b
arXiv:2604.09072v1 Announce Type: new Abstract: Humans effortlessly navigate the physical world by predicting how objects behave under gravity and contact forces, yet how such judgments support sequen
arXiv:2602.04674v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used as proxies for human judgment in computational social science, yet their ability to reprodu
arXiv:2604.08801v1 Announce Type: cross Abstract: Prompt optimization improves language models without updating their weights by searching for a better system prompt, but its effectiveness varies wide
arXiv:2508.01312v4 Announce Type: replace Abstract: We revisit the classical Perspective-Three-Point (P3P) problem, which aims to recover the absolute pose of a calibrated camera from three 2D-3D corr
arXiv:2603.11178v3 Announce Type: replace Abstract: Standard LLM distillation treats all training problems equally -- wasting compute on problems the student has already mastered or cannot yet solve.
arXiv:2506.17310v3 Announce Type: replace-cross Abstract: While Large Language Models (LLMs) demonstrate strong performance across domains, their long-context capabilities are limited by transient neu
arXiv:2604.08707v1 Announce Type: new Abstract: Monadic second order logic (MSO2) plays an important role in parameterized complexity due to the Courcelle's theorem. This theorem states that the probl
arXiv:2604.09058v1 Announce Type: cross Abstract: Long-horizon spatiotemporal prediction remains a challenging problem due to cumulative errors, noise amplification, and the lack of physical consisten
arXiv:2604.08986v1 Announce Type: cross Abstract: Persona prompting has been widely adopted to steer large language models (LLMs) behavior and improve their instruction performance by assigning specif
arXiv:2604.09162v1 Announce Type: cross Abstract: Most affective computing research treats emotion as a static property of text, focusing on the writer's sentiment while overlooking the reader's persp
arXiv:2604.09100v1 Announce Type: new Abstract: We propose a multimodal, physically grounded approach for metric-scale amodal object reconstruction and pose estimation under severe hand occlusion. Unl
arXiv:2604.09434v1 Announce Type: cross Abstract: Turbulent boundary layers over aerodynamic surfaces are a major source of aircraft drag, yet their control remains challenging due to multiscale dynam
arXiv:2604.09499v1 Announce Type: new Abstract: Autonomous racing without prebuilt maps is a grand challenge for embedded robotics that requires kinodynamic planning from instantaneous sensor data at
arXiv:2604.09415v1 Announce Type: cross Abstract: We present PhysInOne, a large-scale synthetic dataset addressing the critical scarcity of physically-grounded training data for AI systems. Unlike exi
arXiv:2604.08987v1 Announce Type: new Abstract: As Large Language Models (LLMs) advance toward embodied AI agents operating in physical environments, a fundamental question emerges: can models trained
arXiv:2604.08991v1 Announce Type: cross Abstract: Small object-centric spatial understanding in indoor videos remains a significant challenge for multimodal large language models (MLLMs), despite its
arXiv:2604.09028v1 Announce Type: cross Abstract: Unmanned aerial vehicles serving as aerial base stations can rapidly restore connectivity after disasters, yet abrupt changes in user mobility and tra
arXiv:2604.08804v1 Announce Type: cross Abstract: Digital firms routinely run many online experiments on shared user populations. When product decisions are compositional, such as combinations of inte
arXiv:2508.05091v2 Announce Type: replace Abstract: Generating temporally coherent, long-duration videos with precise control over subject identity and movement remains a fundamental challenge for con
arXiv:2604.08828v1 Announce Type: cross Abstract: Classifier-free Guidance (CFG) lets practitioners trade-off fidelity against diversity in Diffusion Models (DMs). The practicality of CFG is however h
arXiv:2603.23055v2 Announce Type: replace-cross Abstract: Formal model evaluation methods typically certify that a model satisfies a prescribed target key performance indicator (KPI) level. However, i
arXiv:2604.08624v1 Announce Type: cross Abstract: Spiking Neural Networks (SNNs) are naturally suited for speech processing tasks due to their specific dynamics, which allows them to handle temporal d
arXiv:2511.20068v2 Announce Type: replace Abstract: Autoregressive (AR) image generation has recently emerged as a powerful paradigm for image synthesis. Leveraging the generation principle of large l
arXiv:2604.08649v1 Announce Type: cross Abstract: Modern financial systems generate vast quantities of transactional and event-level data that encode rich economic signals. This paper presents PRAGMA,
arXiv:2604.08881v1 Announce Type: new Abstract: In real-world deployments, Vision-Language Large Models (VLLMs) face critical challenges from multilingual and multimodal composite attacks: harmful ima
arXiv:2601.04884v2 Announce Type: replace Abstract: Executing a multi-agent plan can be challenging when an agent is delayed, because this typically creates conflicts with other agents. So, we need to
arXiv:2510.22293v4 Announce Type: replace Abstract: Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) affects 30-40% of US adults and is the most common chronic liver diseas
arXiv:2604.08941v1 Announce Type: new Abstract: Medical Vision Language Models VLMs suffer from two failure modes that threaten safe deployment mis calibrated confidence and sensitivity to question re
arXiv:2604.08598v1 Announce Type: cross Abstract: Text-based person search faces inherent limitations due to data scarcity, driven by stringent privacy constraints and the high cost of manual annotati
arXiv:2604.09482v1 Announce Type: new Abstract: Reasoning in knowledge-intensive domains remains challenging as intermediate steps are often not locally verifiable: unlike math or code, evaluating ste
arXiv:2604.09123v1 Announce Type: new Abstract: Aspect Sentiment Triplet Extraction (ASTE) aims to extract all sentiment triplets of aspect terms, opinion terms, and sentiment polarities from a senten
arXiv:2508.04853v2 Announce Type: replace-cross Abstract: Post-training quantization (PTQ) has become a crucial tool for reducing the memory and compute costs of modern deep neural networks, including
arXiv:2604.09111v1 Announce Type: cross Abstract: Recently, artificial intelligence-based dubbing technology has advanced, enabling automated dubbing (AD) to convert the source speech of a video into
arXiv:2604.08781v1 Announce Type: cross Abstract: Purpose: To develop and evaluate a deep learning (DL) method for free-breathing phase-sensitive inversion recovery (PSIR) late gadolinium enhancement
arXiv:2604.08277v2 Announce Type: replace-cross Abstract: We present a quantum-inspired ARIMA methodology that integrates quantum-assisted lag discovery with fixed-configuration variational quantum ci
arXiv:2604.08585v1 Announce Type: cross Abstract: Cache fusion accelerates generation process of LLMs equipped with RAG through KV caching and selective token recomputation, thereby reducing computati
arXiv:2604.08703v1 Announce Type: cross Abstract: QoS-QoE translation is a fundamental problem in multimedia systems because it characterizes how measurable system and network conditions affect user-p
arXiv:2604.08570v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used for code generation, yet quantum code generation is still evaluated mostly within single frameworks
arXiv:2604.08976v1 Announce Type: new Abstract: We report that model quantisation restructures domain-level metacognitive efficiency in LLMs rather than degrading it uniformly. Evaluating Llama-3-8B-I
arXiv:2604.08810v1 Announce Type: new Abstract: Graph neural networks (GNNs) are increasingly applied to physical design tasks such as congestion prediction and wirelength estimation, yet progress is
arXiv:2604.05060v2 Announce Type: replace Abstract: Accurate Point Cloud Registration (PCR) is an important task in 3D data processing, involving the estimation of a rigid transformation between two p
arXiv:2511.19704v2 Announce Type: replace Abstract: Open-vocabulary semantic segmentation (OVSS) underpins many vision and robotics tasks that require generalizable semantic understanding. Existing ap
arXiv:2603.19929v2 Announce Type: replace-cross Abstract: RAM incorporates a motion-aware semantic tracker with adaptive Kalman filtering to achieve robust identity association under severe occlusions
arXiv:2604.08685v1 Announce Type: new Abstract: Automated planning algorithms require an action model specifying the preconditions and effects of each action, but obtaining such a model is often hard.
arXiv:2604.08572v1 Announce Type: cross Abstract: State-of-the-art post-hoc out-of-distribution detection methods rely on intermediate layer activation editing. However, they exhibit inconsistent perf
arXiv:2604.08739v1 Announce Type: cross Abstract: Ransomware poses a serious and fast-acting threat to critical systems, often encrypting files within seconds of execution. Research indicates that ran