Knowledge catalogue
arXiv:2605.21861v1 Announce Type: new Abstract: Multi-modality medical vision (MV) foundation models (FM) are fundamentally challenged by pronounced Non-IID feature statistics across heterogeneous ima
arXiv:2503.00747v2 Announce Type: replace Abstract: Light field cameras capture multi-view observations within a single exposure. However, existing studies are typically tailored to specific LF repres
arXiv:2605.12321v2 Announce Type: replace Abstract: Large language models (LLMs) show strong potential for Intelligent Transportation Systems (ITS), particularly in tasks requiring situational reasoni
arXiv:2601.04537v3 Announce Type: replace-cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has driven significant performance gains in reasoning-oriented large language models (LL
arXiv:2507.01053v4 Announce Type: replace-cross Abstract: Large-scale clinical databases offer opportunities for medical research, but their complexity creates barriers to effective use. The Medical I
arXiv:2605.22177v1 Announce Type: cross Abstract: The proliferation of large language models (LLMs) and modular skills has endowed autonomous agents with increasingly powerful capabilities. Existing f
arXiv:2605.21804v1 Announce Type: cross Abstract: Field-scale crop maps support supply-chain forecasting and policy, yet statewide crop identification still often depends on retrospective surveys or r
arXiv:2602.02660v3 Announce Type: replace Abstract: A critical bottleneck in automating AI research is the execution of complex machine learning engineering (MLE) tasks. MLE differs from general softw
arXiv:2605.21917v1 Announce Type: new Abstract: Training Vision Language Models (VLMs) for video event reasoning requires high-quality structured annotations capturing not only what happened, but when
arXiv:2605.20442v1 Announce Type: cross Abstract: Generative AI systems are increasingly deployed as interactive agents in online environments, such as a social network called Moltbook. In Moltbook, l
arXiv:2605.22423v1 Announce Type: new Abstract: Motion degradation, manifested as blur in global shutter (GS) images or rolling shutter (RS) distortion in RS counterparts, remains a fundamental challe
arXiv:2605.22660v1 Announce Type: new Abstract: Moral language is subtle and culturally variable, making it difficult to translate faithfully across languages. Idiomatic expressions, slang, and cultur
arXiv:2605.22641v1 Announce Type: new Abstract: Detecting Schwartz values in political text is difficult because implicit cues often depend on surrounding arguments and fine-grained distinctions betwe
arXiv:2605.22550v1 Announce Type: new Abstract: Two-wheelers account for a disproportionately high share of road fatalities in the Global South. Research on two-wheeler rider behavior, however, lags f
arXiv:2506.19500v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) increasingly act as function-call agents that invoke external tools to tackle tasks beyond their static knowledge
arXiv:2605.20218v1 Announce Type: cross Abstract: Treating and preventing Human Immunodeficiency Virus (HIV) remains a critical global health challenge. While antiretroviral therapy provides a path to
arXiv:2605.21914v1 Announce Type: new Abstract: This paper presents a non-contact approach for vibration-based structural damage detection using an autonomous and customized cost-effective unmanned ae
arXiv:2605.22144v1 Announce Type: new Abstract: Existing approaches for digital short-drama production typically rely on one-shot LLM generated scripts and loosely coupled pipelines, which fail to sat
arXiv:2507.05660v3 Announce Type: replace-cross Abstract: Customizing Large Language Models (LLMs) on untrusted datasets poses severe risks of injecting toxic behaviors. In this work, we introduce Opt
arXiv:2605.22200v1 Announce Type: new Abstract: Achieving high levels of surgical skill through effective training is essential for optimal patient outcomes. Automated, data-driven skill assessment ho
arXiv:2605.20554v1 Announce Type: new Abstract: According to canonical negotiation theory, people's success in a negotiation depends on how well they balance competing demands--empathizing and asserti
arXiv:2605.21572v1 Announce Type: new Abstract: Simulation-ready physical 3D assets have emerged as a promising direction owing to their broad applicability in downstream tasks. However, most existing
arXiv:2605.22311v1 Announce Type: new Abstract: Identity-conditioned diffusion models enable high-quality and identity-consistent face generation, but they also raise severe privacy concerns, as model
arXiv:2605.22013v1 Announce Type: new Abstract: Understanding 3D point clouds through language remains a fundamental challenge in computer graphics and visual computing, due to the irregular structure
arXiv:2605.21726v1 Announce Type: new Abstract: The generative nature of Large Language Models (LLMs) is reflected in the conditional probabilities they compute to sample each response token given the
arXiv:2605.21776v1 Announce Type: new Abstract: Estimating mutual information from text usually requires training a task-specific critic, which limits its use in low-data settings. We ask whether larg
arXiv:2605.22140v1 Announce Type: new Abstract: In recent years, large language models have shown substantial potential in psychological support tasks. However, existing psychological counseling data
arXiv:2511.08093v2 Announce Type: replace-cross Abstract: Large speech recognition models like Whisper-small achieve high accuracy but are difficult to deploy on edge devices due to their high computa
arXiv:2605.22431v1 Announce Type: new Abstract: This paper develops a fast numerical dual control for exploration and exploitation (DCEE) method to address auto-optimization problems in unknown enviro
arXiv:2605.21781v1 Announce Type: new Abstract: Large language models (LLMs) have become increasingly capable of following instructions and complex reasoning, making prompting a flexible interface for
arXiv:2605.20326v1 Announce Type: cross Abstract: We develop a representability-aware and interpolable neural network (NN) framework for predicting two-particle reduced density matrices (2-RDMs). The
arXiv:2605.22591v1 Announce Type: new Abstract: Frozen Vision Foundation Models (VFMs) with lightweight classification heads are increasingly used in medical imaging because they offer efficient and r
arXiv:2605.22327v1 Announce Type: new Abstract: Purpose: To assess whether breast lesion segmentation can be learned directly from acquired MRI k-space, and whether doing so improves robustness when d
arXiv:2605.20368v1 Announce Type: cross Abstract: Organizations that scan documents for sensitive information face a practical problem. Cloud services require data to be sent to external infrastructur
arXiv:2605.22654v1 Announce Type: new Abstract: Previous detection studies have shown that LLMs cannot be effectively used as detectors, but these studies have not addressed modern Chinese poetry. Mor
arXiv:2605.22572v1 Announce Type: new Abstract: Accurate segmentation of brain tumour sub-regions from multi-parametric MRI is critical for treatment planning yet remains challenging due to morphologi
arXiv:2605.22809v1 Announce Type: new Abstract: Robust training and validation of Autonomous Driving Systems (ADS) require massive, diverse datasets. Proprietary data collected by Autonomous Vehicle (
arXiv:2605.22536v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have made rapid progress in spatial intelligence, yet existing spatial reasoning benchmarks largely assume pr
arXiv:2605.22283v1 Announce Type: new Abstract: We introduce SOMA, the Spatial Memory framework for Out-of-Vision Manipulation in Vision-Language-Action (VLA) models. Most existing VLAs implicitly ass
arXiv:2605.22206v1 Announce Type: cross Abstract: The Thousand Brains Theory (TBT) and its open-source Monty framework model object recognition through sensorimotor inference -- identifying objects by
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arXiv:2605.20190v1 Announce Type: new Abstract: Iterative industrial design-simulation optimization is bottlenecked by the CAD-CAE semantic gap: translating simulation feedback into valid geometric ed
arXiv:2506.23808v2 Announce Type: replace Abstract: A recent series of works has shown that initialization-free BA can be achieved using pseudo Object Space Error (pOSE) as a surrogate objective. The
arXiv:2603.28103v2 Announce Type: replace-cross Abstract: Parliamentary proceedings represent a rich yet challenging resource for computational analysis, particularly when preserved only as scanned hi
arXiv:2602.12952v2 Announce Type: replace-cross Abstract: Adapting large pre-trained models to downstream tasks often produces task-specific parameter updates that are expensive to relearn for every m
arXiv:2605.22769v1 Announce Type: new Abstract: Large language models (LLMs) are typically trained on shuffled corpora, yielding models whose knowledge is frozen at train time and whose temporal groun
arXiv:2605.06597v2 Announce Type: replace Abstract: Self-distillation (SD) offers a promising path for adapting large language models (LLMs) without relying on stronger external teachers. However, SD
arXiv:2605.21611v1 Announce Type: new Abstract: We introduce spatially grounded contextual image generation, a controllable image generation task that reframes the conditioning paradigm. Instead of su
arXiv:2605.20742v1 Announce Type: new Abstract: With the rapid proliferation of electric vehicles, the safety and reliability of lithium-ion batteries have become critical concerns. Effective anomaly
arXiv:2605.21977v1 Announce Type: new Abstract: AI-generated content (AIGC) is rapidly improving, creating an urgent need for detectors that generalize across data sources, deployment pipelines, and v
arXiv:2605.21924v1 Announce Type: new Abstract: On-policy knowledge distillation has proven effective for language models, yet its application to vision-language models (VLMs) remains underexplored. W
arXiv:2605.22651v1 Announce Type: new Abstract: CLIP-style contrastive pretraining typically curates web-scale image-text pairs using sample-level filtering signals, often based on pair-level alignmen
arXiv:2605.22823v1 Announce Type: new Abstract: Video Large Language Models (Video-LLMs) have made rapid progress on temporal video understanding, yet many fail at a basic perceptual primitive: signed
arXiv:2605.21058v1 Announce Type: new Abstract: Causal representation learning (CRL) and traditional representation learning have largely developed along different trajectories. Traditional representa
arXiv:2510.14444v3 Announce Type: replace Abstract: Post-training pruning can substantially reduce LLM inference costs, but it often degrades quality unless the remaining weights are adapted. Since gl
arXiv:2605.21461v1 Announce Type: new Abstract: Global Navigation Satellite Systems (GNSS) are widely used to provide position, velocity, and timing (PVT) information for various applications, includi
arXiv:2605.21371v1 Announce Type: new Abstract: Acquiring usable optical imagery in Antarctica is inherently challenging due to prolonged polar nights and frequent cloud cover. Landsat provides the lo
arXiv:2605.20666v1 Announce Type: new Abstract: This paper proposes a new birth model including semantic information derived from deep learning to create an occlusion-aware Gaussian Mixture Probabilit
arXiv:2605.20988v1 Announce Type: new Abstract: We study transformers' generalization behavior on boolean domains from the perspective of the Fourier Spectra of their target functions. In contrast to
arXiv:2605.21188v1 Announce Type: new Abstract: Kinodynamic planning for car-like vehicles on uneven terrain requires simultaneously optimizing competing objectives such as path efficiency and pose st