Knowledge catalogue
arXiv:2606.29113v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly mediate strategic interactions through natural language, making semantic control a critical element of commu
arXiv:2606.29879v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) provide powerful semantic understanding and commonsense reasoning for End-to-End Autonomous Driving (E2E-AD) planning. H
arXiv:2606.28419v1 Announce Type: cross Abstract: Limited data availability, class imbalance, and domain variability remain major barriers to reliable medical image classification. Conventional augmen
arXiv:2606.28377v1 Announce Type: cross Abstract: HAR using Inertial Measurement Unit (IMU) sensors is vital for healthcare monitoring and rehabilitation. Despite deep learning advancements, major cha
arXiv:2606.30575v1 Announce Type: new Abstract: The problem of autonomous navigation for UAV inspection remains challenging as it requires effectively navigating in close proximity to obstacles, while
arXiv:2606.28339v1 Announce Type: cross Abstract: Industrial 6G networks require ultra-reliable, low-latency, and energy-efficient connectivity in dynamic and blockage-prone environments, where conven
arXiv:2606.29165v1 Announce Type: new Abstract: Continuum robots offer key advantages in navigating unstructured environments, but their safe operation requires accurate estimation of the external con
arXiv:2606.28369v1 Announce Type: cross Abstract: Semantic search and recommendation of similar documents, such as news and reports about unusual environmental events (e.g., a dead whale washed ashore
arXiv:2606.29738v1 Announce Type: new Abstract: Real-time monocular Simultaneous Localization and Mapping (SLAM) fundamentally suffers from scale ambiguity and a lack of geometric self-correction. Whi
arXiv:2504.20383v3 Announce Type: replace Abstract: Disparity compensation represents the primary strategy in stereo video compression (SVC) for exploiting cross-view redundancy. These mechanisms can
arXiv:2606.30019v1 Announce Type: new Abstract: Music-driven dance video generation aims to synthesize expressive human motion that is temporally aligned with music while maintaining high visual fidel
arXiv:2606.28996v1 Announce Type: new Abstract: Short-fiber thermoplastic (SFT) composites are increasingly employed in lightweight aerospace and automotive structures owing to their favorable strengt
arXiv:2606.30077v1 Announce Type: cross Abstract: With Large Language Model (LLM) pre-training and fine-tuning shifting its focus from data volume to data quality, quality data selection has emerged a
arXiv:2606.28898v1 Announce Type: new Abstract: Knowledge updating in pre-trained Large Language Models (LLMs) remains an important challenge. While continual training provides a potential avenue for
arXiv:2510.19465v2 Announce Type: replace Abstract: Obtaining truly representative pore-scale images that match bulk formation properties remains a fundamental challenge in subsurface characterization
arXiv:2606.28845v1 Announce Type: new Abstract: Personalizing Multimodal Large Language Models (MLLMs) aims to recognize users' unique concepts from visual data and provide personalized responses. Alt
arXiv:2606.28630v1 Announce Type: new Abstract: Forecasting longitudinal brain lesion evolution is critical for disease monitoring and treatment planning. Existing approaches typically learn a direct
arXiv:2606.29673v1 Announce Type: new Abstract: Cooperative localization using range-based measurements is critical for multi-robot systems operating in GPS-denied and unstructured environments. Howev
arXiv:2505.19809v3 Announce Type: replace-cross Abstract: In many real-world applications of regression, conditional probability estimation, and uncertainty quantification, exploiting symmetries roote
arXiv:2606.29837v1 Announce Type: new Abstract: Dataset distillation (DD) condenses large corpora into compact, information-rich subsets for efficient training and reuse. However, under noisy supervis
arXiv:2606.30408v1 Announce Type: new Abstract: Homography estimation, as one of the fundamental problems in computer vision, remains challenged by scale variation scenarios where image pairs potentia
arXiv:2606.29887v1 Announce Type: new Abstract: In real-world applications, guardrails are often expected to identify unsafe user-model interactions according to application-specific safety policies,
arXiv:2512.02456v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) have achieved remarkable progress in integrating visual perception with language understanding. However, effecti
arXiv:2606.30244v1 Announce Type: new Abstract: Referring Remote Sensing Image Segmentation (RRSIS) seeks to localize and segment the target object or region specified by a natural language expression
arXiv:2510.12784v2 Announce Type: replace-cross Abstract: Recently, remarkable progress has been made in Unified Multimodal Models (UMMs), which integrate vision-language generation and understanding
arXiv:2510.14511v3 Announce Type: replace Abstract: This paper establishes analytical stability boundaries for robot-mediated human-human (dyadic) interaction systems, subject to haptic communication
arXiv:2512.01461v2 Announce Type: replace-cross Abstract: Model merging has emerged as a promising paradigm for enabling multi-task capabilities without additional training. However, traditional basic
arXiv:2602.12394v2 Announce Type: replace Abstract: Personalized prompting offers large opportunities for deploying large language models (LLMs) to diverse users, yet existing prompt optimization meth
arXiv:2606.29644v1 Announce Type: new Abstract: Earth system infrastructures relying on satellite-based technologies, such as Global Positioning System (GPS) communications, are affected by ionospheri
arXiv:2606.29832v1 Announce Type: new Abstract: Machine unlearning aims to eliminate the influence of specific data from trained models to safeguard privacy. However, this presents a significant chall
arXiv:2606.28327v1 Announce Type: cross Abstract: How do retrieval bounds compare between human episodic memory and Retrieval-Augmented Generation (RAG) systems under semantic interference? We present
arXiv:2606.29639v1 Announce Type: cross Abstract: Automatic prompt optimization is still underexplored for episodic few-shot relation extraction with smaller language models. We propose a two-stage fr
arXiv:2606.30020v1 Announce Type: new Abstract: Pathology foundation models (PFMs) offer generalizable representations for whole-slide image (WSI) analysis, yet their clinical adoption remains limited
arXiv:2503.16550v2 Announce Type: replace Abstract: Neural network language models (LMs) are confronted with significant challenges in generalization and robustness. Currently, many studies focus on i
arXiv:2606.30332v1 Announce Type: new Abstract: Recent advances in diffusion models have shown impressive performance in controllable image generation and dense prediction tasks. However, existing app
arXiv:2606.29714v1 Announce Type: new Abstract: Unified visual anomaly detection seeks to train a single detector that can be deployed across categories, domains, and application scenarios. In the few
arXiv:2606.29354v1 Announce Type: new Abstract: Chain-of-Thought (CoT) improves large language models (LLMs) on difficult reasoning tasks, but it often incurs long natural-language rationales that are
arXiv:2606.29489v1 Announce Type: new Abstract: When humans translate, not every word depends equally on the surrounding context. Some tokens, particularly function words like pronouns and auxiliaries
arXiv:2606.30587v1 Announce Type: cross Abstract: Researchers and practitioners increasingly apply Large Language Models (LLMs) for automated vulnerability detection. Recent work has shown that LLMs a
arXiv:2606.15708v2 Announce Type: replace Abstract: Welcome to the ninth edition of the AI Index report. As AI continues to advance rapidly, the question becomes whether the systems built around it ca
arXiv:2606.27997v1 Announce Type: new Abstract: Benchmarks of machine learning models often include many datasets, making evaluation expensive. For efficiency, it is preferable to perform evaluations
arXiv:2606.27547v1 Announce Type: new Abstract: Human motion generation models are fundamentally constrained by the limited diversity of motion capture datasets, which predominantly contain common, re
arXiv:2606.20493v2 Announce Type: replace-cross Abstract: When large language models serve as evaluators in multi-agent systems, their strategy preferences -- whether induced by explicit prompts or by
arXiv:2606.27935v1 Announce Type: new Abstract: Deep learning has demonstrated remarkable success in high-throughput histopathology image analysis. However, the performance of learning-based models cr
arXiv:2606.28179v1 Announce Type: cross Abstract: Identifying robust associations between cardiac imaging phenotypes and clinical diseases is fundamental to population-scale cardiovascular research an
arXiv:2601.14302v2 Announce Type: replace-cross Abstract: Image transmission and processing systems in resource-critical applications face significant challenges from adversarial perturbations that co
arXiv:2606.28323v1 Announce Type: cross Abstract: Dexterous manipulation policies can solve individual skills, but composing them to perform multiple tasks with a single hand remains challenging. Addi
arXiv:2606.27751v1 Announce Type: cross Abstract: This report investigates the extension of pretrained General-Purpose Audio Tagging (GP-AT) models toward spatially grounded Sound Event Localization a
arXiv:2606.27694v1 Announce Type: cross Abstract: Randomized Smoothing (RS) provides rigorous robustness guarantees for neural networks without architectural constraints, yet its adoption is limited b
arXiv:2603.06205v2 Announce Type: replace Abstract: Inertial measurement units (IMUs), which provide high-frequency linear acceleration and angular velocity measurements, serve as fundamental sensing
arXiv:2603.11638v2 Announce Type: replace Abstract: Autonomous Aerial Manipulators (AAMs) are inherently coupled, nonlinear systems that exhibit nonstationary and multiscale residual dynamics, particu
arXiv:2606.28128v1 Announce Type: cross Abstract: Video generation models have emerged as a promising paradigm for embodied world simulation. However, both general-domain video generators and robot-sp
arXiv:2606.27861v1 Announce Type: new Abstract: Reinforcement learning (RL) has emerged as a promising solution to accomplish complex robotic control tasks; however, most of the current work ignores t
arXiv:2606.27561v1 Announce Type: new Abstract: Generative models have achieved remarkable success in data synthesis, though recent advances driven by increasing model scale have introduced challenges
arXiv:2606.27922v1 Announce Type: cross Abstract: Current multimodal reflection mechanisms for long video understanding predominantly rely on closed-loop self-reflection within internal parameters. La
arXiv:2606.27701v1 Announce Type: cross Abstract: While voice control is rapidly becoming a ubiquitous vector of human-AI communication, the risks facing these systems remain poorly understood. This i
arXiv:2606.27581v1 Announce Type: cross Abstract: Current humanoid reinforcement-learning policies excel at free-space motions but struggle with contact-rich tasks, as pure kinematic tracking cannot r
arXiv:2606.27397v1 Announce Type: cross Abstract: Evaluating LLM agents requires dynamic environments that go beyond static reasoning and zero-sum games. Real-world economic interaction is often open-
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arXiv:2606.27777v1 Announce Type: new Abstract: Abdominal ultrasound is indispensable for rapid, noninvasive trauma triage. However, interpreting the subtle dynamic cues embedded in continuous scannin