Knowledge catalogue
arXiv:2605.00647v1 Announce Type: new Abstract: Automated pediatric electrocardiogram (ECG) diagnosis remains challenging because models trained predominantly on adult data suffer from substantial cro
arXiv:2601.05833v2 Announce Type: replace Abstract: Pretokenization is a crucial, sequential pass in Byte-level BPE tokenizers, yet little work has been done to optimize it for edge-side inference. Ou
arXiv:2605.00814v1 Announce Type: new Abstract: While autoregressive Large Vision-Language Models (LVLMs) demonstrate remarkable proficiency in multimodal tasks, they face a 'Visual Signal Dilution' p
arXiv:2605.00227v1 Announce Type: new Abstract: There are growing concerns about the risks posed by AI companion applications designed for emotional engagement. Existing safety evaluations often rely
arXiv:2605.00264v1 Announce Type: new Abstract: Offline multi-agent reinforcement learning in general-sum settings is challenged by the distribution shift between logged datasets and target equilibriu
arXiv:2605.00707v1 Announce Type: new Abstract: Image editing instructions are heterogeneous: a color swap, an object insertion, and a physical-action edit all demand different spatial coverage and di
arXiv:2605.00412v1 Announce Type: cross Abstract: World models have recently re-emerged as a central paradigm for embodied intelligence, robotics, autonomous driving, and model-based reinforcement lea
arXiv:2605.00517v1 Announce Type: new Abstract: Despite substantial progress in text-driven 3D human motion synthesis, generating realistic multi-person interaction sequences remains challenging. Nota
arXiv:2605.00385v1 Announce Type: new Abstract: Physics-Informed Neural Networks have become a powerful mesh-free method for solving partial differential equations, but their performance is often limi
arXiv:2605.00265v1 Announce Type: new Abstract: Real-world knowledge is often organized as hierarchies such as product taxonomies, medical ontologies, and label trees, yet learning hierarchical repres
arXiv:2510.26020v2 Announce Type: replace Abstract: Multi-tool-integrated reasoning enables LLM-empowered tool-use agents to solve complex tasks by interleaving natural-language reasoning with calls t
arXiv:2605.00345v1 Announce Type: new Abstract: Generating pose-aligned 3D objects is challenging due to the spatial mismatches and transformation ambiguities inherent in decoupled canonical-then-rota
arXiv:2605.00742v1 Announce Type: cross Abstract: LLMs excel at predictive tasks and complex reasoning tasks, but many high-value deployments rely on decisions under uncertainty, for example, which to
arXiv:2605.00600v1 Announce Type: cross Abstract: Deep neural networks achieve impressive results across diverse applications, yet their overconfidence on unseen inputs necessitates reliable epistemic
arXiv:2605.00825v1 Announce Type: new Abstract: Flow matching (FM) trains a time-dependent vector field that transports samples from a simple prior to a complex data distribution. However, for high-di
arXiv:2411.02327v4 Announce Type: replace Abstract: In the past year, video-based large language models (Video LLMs) have achieved impressive progress, particularly in their ability to process long vi
arXiv:2605.00665v1 Announce Type: new Abstract: The systemic, metabolic, lifestyle factors have established associations with Alzheimer's Disease (AD) through epidemiologic and AD-specific biomarker s
arXiv:2605.00121v1 Announce Type: new Abstract: We have seen tremendous recent progress in our ability to build 'spatio-semantic' representations that enable robots to perform complex reasoning across
arXiv:2602.19423v3 Announce Type: replace Abstract: Domain adaptive segmentation (DAS) is a promising paradigm for delineating intracellular structures from various large-scale electron microscopy (EM
arXiv:2412.02125v2 Announce Type: replace-cross Abstract: Goal-conditioned policies enable decision-making models to execute diverse behaviors based on specified goals, yet their downstream performanc
arXiv:2605.00384v1 Announce Type: new Abstract: Preference-based reinforcement learning offers a scalable alternative to manual reward engineering by learning reward structures from comparative feedba
arXiv:2506.00158v2 Announce Type: replace Abstract: Zeroth-order optimization has emerged as a promising approach for fine-tuning large language models under differential privacy (DP) and memory const
arXiv:2601.06600v2 Announce Type: replace Abstract: Short-video platforms have become major channels for misinformation, where deceptive claims frequently leverage visual experiments and social cues.
arXiv:2605.00326v1 Announce Type: new Abstract: Single-prompt first-token probabilities from zero-shot vision-language model (VLM) safety classifiers are treated as decision scores, but we show they a
arXiv:2605.00099v1 Announce Type: cross Abstract: Despite rapid recent advances in quantum machine learning, the field is in many ways stuck. Existing approaches can exhibit serious limitations, and w
arXiv:2605.00022v1 Announce Type: new Abstract: The rapid proliferation of large audio models (LAMs) demands efficient approaches for model comparison, yet comprehensive benchmarks are costly. To fill
arXiv:2605.00744v1 Announce Type: new Abstract: Edge detection refers to identifying points in a digital image where intensity changes sharply, indicating object boundaries or structural features. Cor
arXiv:2605.00747v1 Announce Type: cross Abstract: Quantum machine learning is a promising field for efficiently learning features of a dataset to perform a specified task, such as classification. Inte
arXiv:2605.00421v1 Announce Type: new Abstract: Large language models (LLMs) show promise in radiology but their deployment is limited by computational requirements that preclude use in resource-const
arXiv:2605.00740v1 Announce Type: cross Abstract: Randomized-subspace methods reduce the cost of first-order optimization by using only low-dimensional projected-gradient information, a feature that i
arXiv:2602.18196v3 Announce Type: replace Abstract: Structured dilated attention has an appealing inference-time efficiency knob: it reduces the FLOPs of attention and the KV cache size by a factor of
arXiv:2605.00146v1 Announce Type: new Abstract: Real-time object detection on energy-constrained platforms is critical for applications such as UAV-based inspection, autonomous navigation, and mobile
arXiv:2605.00271v1 Announce Type: new Abstract: Event cameras provide several unique advantages over standard frame-based sensors, including high temporal resolution, low latency, and robustness to ex
arXiv:2508.21762v3 Announce Type: replace Abstract: AI researchers and practitioners increasingly apply large language models (LLMs) to what we call reasoning-intensive regression (RiR), i.e., deducin
arXiv:2605.00623v1 Announce Type: new Abstract: This paper introduces EnergyFlow, a framework that unifies generative action modeling with inverse reinforcement learning by parameterizing a scalar ene
arXiv:2407.16216v3 Announce Type: replace Abstract: Large language models (LLMs) trained via pretraining and supervised fine-tuning (SFT) can still produce harmful and misaligned outputs, or struggle
arXiv:2604.07669v2 Announce Type: replace Abstract: Lead optimization in drug discovery requires improving therapeutic properties while ensuring that molecular modifications correspond to feasible syn
arXiv:2605.00654v1 Announce Type: new Abstract: For a risk-averse finite-horizon Markov Decision Problem, we introduce a special class of Markov coherent risk measures, called mini-batch measures. We
arXiv:2605.00468v1 Announce Type: new Abstract: Plain Language Summaries (PLS) aim to make research accessible to lay readers, but they are typically written in a one-size-fits-all style that ignores
arXiv:2605.00256v1 Announce Type: new Abstract: SAM2 produces high-quality zero-shot segmentation on natural images, but applying it to large remote sensing scenes exposes two problems: (1) its mask g
arXiv:2501.00885v2 Announce Type: replace Abstract: The extraordinary success of recent Large Language Models (LLMs) on a diverse array of tasks has led to an explosion of scientific and philosophical
arXiv:2605.00380v1 Announce Type: cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) enhances reasoning of Large Language Models (LLMs) but usually exhibits limited generation diver
arXiv:2512.12744v3 Announce Type: replace Abstract: Activation sparsity offers a compelling route to accelerate large language model (LLM) inference by selectively suppressing hidden activations, yet
arXiv:2605.00419v1 Announce Type: cross Abstract: Model ensembling is a well-established technique for improving the performance of machine learning models. Conventionally, this involves averaging the
arXiv:2605.00062v1 Announce Type: cross Abstract: Rapid aerodynamic evaluation is crucial for modern vehicle design, yet existing neural operators struggle to capture intricate spatial correlations. W
arXiv:2605.00257v1 Announce Type: new Abstract: The inception of Large Language Models (LLMs) has catalyzed AI adoption in the finance sector, yet their reliability in complex, jurisdiction-specific t
arXiv:2605.00489v1 Announce Type: new Abstract: We study a graph bandit setting where the objective of the learner is to detect the most influential node of a graph by requesting as little information
arXiv:2601.07349v3 Announce Type: replace Abstract: Reinforcement Learning with Verifiable reward (RLVR) on preference data has become the mainstream approach for training Generative Reward Models (GR
arXiv:2602.07744v3 Announce Type: replace Abstract: Diffusion and flow models have become the dominant paradigm for generative modeling on Riemannian manifolds, with successful applications in protein
arXiv:2605.00595v1 Announce Type: new Abstract: Perception for automated driving is largely based on onboard environmental sensors, such as cameras and radar, which are cost-effective but limited by l
arXiv:2605.00032v1 Announce Type: cross Abstract: This work presents ROSA, a microring-based optical neural network architecture that improves robustness and energy efficiency using an optical shift-a
arXiv:2605.00180v1 Announce Type: cross Abstract: As the large language model (LLM) ecosystem expands, individual models exhibit varying capabilities across queries, benchmarks, and domains, motivatin
arXiv:2605.00199v1 Announce Type: new Abstract: When a language model answers a table question, users have no way to verify which cells informed which reasoning steps. We introduce RSAT, a method that
arXiv:2605.00392v1 Announce Type: new Abstract: DeepSeek-OCR leverages visual-text compression to reduce long-text processing costs and accelerate inference, yet visual tokens remain prone to redundan
arXiv:2605.00798v1 Announce Type: cross Abstract: Humans solve problems by executing targeted plans, yet large language models (LLMs) remain unreliable for structured workflow execution. We propose Ru
arXiv:2605.00528v1 Announce Type: cross Abstract: AI agents execute tens to hundreds of chained LLM calls per task, yet GPU schedulers treat each call as independent, discarding gigabytes of intermedi
arXiv:2605.00787v1 Announce Type: new Abstract: While representation and similarity learning have improved the sample efficiency of Reinforcement Learning (RL), they are rarely used to shape policy up
arXiv:2605.00620v1 Announce Type: new Abstract: Scientific literature is expanding at an unprecedented pace, making it increasingly challenging to efficiently organize and access domain knowledge. A h
arXiv:2605.00482v1 Announce Type: new Abstract: Mobile network operators must monitor thousands of heterogeneous network elements across the radio access network and the packet core, each exposing hig
arXiv:2605.00402v1 Announce Type: cross Abstract: Spiking Neural Networks (SNNs) provide a promising framework for energy-efficient and biologically grounded computation; however, scalable learning in