Knowledge catalogue
arXiv:2604.24729v1 Announce Type: new Abstract: Specification-guided reinforcement learning (RL) provides a principled framework for encoding complex, temporally extended tasks using formal specificat
arXiv:2604.23432v1 Announce Type: cross Abstract: Reliable depth estimation from spherical images is crucial for 360{eg} vision in robotic navigation and immersive scene understanding. However, the on
arXiv:2512.07538v3 Announce Type: replace Abstract: Recognizing semantic differences across documents is crucial for text generation evaluation and content alignment, especially in cross-lingual setti
arXiv:2604.23001v1 Announce Type: cross Abstract: Despite remarkable progress in Vision--Language--Action (VLA) models, a central bottleneck remains underexamined: the data infrastructure that underli
arXiv:2604.22328v1 Announce Type: cross Abstract: Driven by the transition towards a climate-neutral energy system, accurate energy time series forecasting is critical for planning and operation. Yet,
arXiv:2112.02604v3 Announce Type: replace-cross Abstract: Accurately modeling pedestrian intention and understanding driver decision-making processes are critical for the development of safe and socia
arXiv:2604.22409v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have advanced static visual--spatial reasoning, yet they often fail to preserve long-horizon spatial coherence
arXiv:2604.21199v1 Announce Type: cross Abstract: Time series question-answering (TSQA), in which we ask natural language questions to infer and reason about properties of time series, is a promising
arXiv:2604.21461v1 Announce Type: new Abstract: Egocentric AI agents, such as smart glasses, rely on pointing gestures to resolve referential ambiguities in natural language commands. However, despite
arXiv:2604.21510v1 Announce Type: new Abstract: While Large Language Models (LLMs) demonstrate remarkable reasoning, complex optimization tasks remain challenging, requiring domain knowledge and robus
arXiv:2603.27112v2 Announce Type: replace Abstract: As Automatic Train Operation (ATO) advances toward GoA4 and beyond, it increasingly depends on efficient, reliable cab-view visual perception and de
arXiv:2604.21696v1 Announce Type: new Abstract: Tabular foundation models aim to learn universal representations of tabular data that transfer across tasks and domains, enabling applications such as t
arXiv:2510.21652v2 Announce Type: replace Abstract: AI agents hold the potential to revolutionize scientific productivity by automating literature reviews, replicating experiments, analyzing data, and
arXiv:2604.20460v1 Announce Type: new Abstract: Safety-critical traffic reasoning requires contrastive consistency: models must detect true hazards when an accident occurs, and reliably reject plausib
arXiv:2506.00979v5 Announce Type: replace-cross Abstract: The rapid development of Artificial Intelligence Generated Content (AIGC) techniques has enabled the creation of high-quality synthetic conten
arXiv:2604.20623v1 Announce Type: cross Abstract: Traditional change detection identifies where changes occur, but does not explain what changed in natural language. Existing remote sensing change cap
arXiv:2604.19133v1 Announce Type: new Abstract: Robust 3D reconstruction across varying environmental conditions remains a critical challenge for robotic perception, particularly when transitioning be
arXiv:2604.18837v1 Announce Type: cross Abstract: Quantum kernel methods have been proposed as a promising approach for leveraging near-term quantum computers for supervised learning, yet rigorous ben
arXiv:2601.04925v2 Announce Type: replace Abstract: Large Language Models (LLMs) can generate highly persuasive text, raising concerns about their misuse for propaganda, manipulation, and other harmfu
arXiv:2604.18873v1 Announce Type: new Abstract: Large language models (LLMs) are highly capable at language generation, but they remain unreliable when reasoning requires explicit symbolic structure,
arXiv:2604.19274v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as co-authors in collaborative writing, where users begin with rough drafts and rely on LLMs to compl
arXiv:2601.22737v2 Announce Type: replace Abstract: The robust safety of Vision-Language Large Models (VLLMs) against joint multilingual and multimodal threats remains severely underexplored. Current
arXiv:2604.18914v1 Announce Type: cross Abstract: While multilingual large language models (LLMs) perform well on high-level tasks like translation and question answering, their ability to handle gram
arXiv:2511.12606v3 Announce Type: replace Abstract: Group Activity Recognition (GAR) is well studied on the video modality for surveillance and indoor team sports (e.g., volleyball, basketball). Yet,
arXiv:2604.19001v1 Announce Type: new Abstract: Large reasoning models (LRMs) produce complex, multi-step reasoning traces, yet safety evaluation remains focused on final outputs, overlooking how harm
arXiv:2604.16984v1 Announce Type: new Abstract: This paper presents the report of the URVIS 2026 challenge on adverse-to-extreme panoptic segmentation. As the first challenge of its kind, it attracted
arXiv:2604.16582v1 Announce Type: new Abstract: Pedestrian detection is fundamental to autonomous driving, robotics, and surveillance. Despite progress in deep learning, reliable identification remain
arXiv:2604.16886v1 Announce Type: new Abstract: Generalist embodied agents must perform interactive, causally-dependent reasoning, continually interacting with the environment, acquiring information,
arXiv:2604.17761v1 Announce Type: cross Abstract: Interpretability tools are increasingly used to analyze failures of Large Language Models (LLMs), yet prior work largely focuses on short prompts or t
arXiv:2604.16341v1 Announce Type: cross Abstract: Virtual Reality (VR) applications require robust user identification systems to ensure secure access to equipment and protect worker identities. Motio
arXiv:2604.17969v1 Announce Type: new Abstract: Visual search in 3D environments requires embodied agents to actively explore their surroundings and acquire task-relevant evidence. However, existing v
arXiv:2604.18164v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have been increasingly used as automatic evaluators-a paradigm known as MLLM-as-a-Judge. However, their reliabi
arXiv:2510.22048v3 Announce Type: replace Abstract: Power flow (PF) calculations are the backbone of real-time grid operations, across workflows such as contingency analysis (where repeated PF evaluat
arXiv:2604.17260v1 Announce Type: new Abstract: Evaluating meeting effectiveness is crucial for improving organizational productivity. Current approaches rely on post-hoc surveys that yield a single c
arXiv:2604.17141v1 Announce Type: new Abstract: The rapid growth of scientific literature calls for automated methods to assess and predict research impact. Prior work has largely focused on citation-
arXiv:2604.17143v1 Announce Type: new Abstract: Despite their substantial successes, AI agents continue to face fundamental challenges in terms of trustworthiness. Consider deep research agents, taske
arXiv:2604.15561v1 Announce Type: cross Abstract: Reported chest CT segmentation performance can be strongly inflated when train and test partitions mix slices from the same study. We present CTSCAN,
arXiv:2406.14294v3 Announce Type: replace-cross Abstract: Discrete audio tokens have recently gained considerable attention for their potential to bridge audio and language processing, enabling multim
arXiv:2604.16270v1 Announce Type: cross Abstract: The complexity of Vietnam's legal texts presents a significant barrier to public access to justice. While Large Language Models offer a promising solu
arXiv:2604.15648v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs) consistently require new arenas to guide their expanding boundaries, yet their capabilities with hypergraphs remain
arXiv:2510.23536v2 Announce Type: replace Abstract: Intent identification serves as the foundation for generating appropriate responses in personalized question answering (PQA). However, existing benc
arXiv:2511.13131v2 Announce Type: replace Abstract: Large Language Models (LLMs) have emerged as powerful tools for automating complex reasoning and decision-making tasks. In telecommunications, they
arXiv:2604.16272v1 Announce Type: cross Abstract: As AI-assisted video creation becomes increasingly practical, instruction-guided video editing has become essential for refining generated or captured
arXiv:2604.13888v1 Announce Type: new Abstract: The integration of Large Language Models (LLMs) into Geographic Information Systems (GIS) marks a paradigm shift toward autonomous spatial analysis. How
arXiv:2604.15134v1 Announce Type: new Abstract: Reliable procedural monitoring in video requires exposure to naturally occurring human errors and the recoveries that follow. In egocentric recordings,
arXiv:2512.04578v3 Announce Type: replace Abstract: Legal general intelligence (GI) refers to artificial intelligence (AI) that encompasses legal understanding, reasoning, and decision-making, simulat
arXiv:2506.03610v3 Announce Type: replace Abstract: Large Language Model (LLM) agents are reshaping the game industry, by enabling more intelligent and human-preferable characters. Yet, current game b
arXiv:2604.14199v1 Announce Type: cross Abstract: Predicting real-world events from live market signals demands systems that fuse qualitative news with quantitative order-book dynamics under strict te
arXiv:2604.14934v1 Announce Type: new Abstract: Automatic evaluation metrics are essential for building multilingual translation systems. The common practice of evaluating these systems is averaging m
arXiv:2604.13060v1 Announce Type: new Abstract: Dental triage is a safety-critical clinical routing task that requires integrating multimodal clinical information (e.g., patient complaints and radiogr
arXiv:2604.12320v1 Announce Type: cross Abstract: While video large language models (Video-LLMs) excel in understanding slow-paced, real-world egocentric videos, their capabilities in high-velocity, i
arXiv:2506.08618v4 Announce Type: replace-cross Abstract: AI is transforming scientific research by revealing new ways to understand complex physical systems, but its impact remains constrained by the
arXiv:2604.12218v1 Announce Type: new Abstract: System log anomaly detection is critical for maintaining the reliability of large-scale software systems, yet traditional methods struggle with the hete
arXiv:2604.10456v1 Announce Type: new Abstract: The surging demand for adapting long-form cinematic content into short videos has motivated the need for versatile automatic video compilation systems.
arXiv:2604.11796v1 Announce Type: cross Abstract: Recently, large language models (LLMs) are capable of generating highly fluent textual content. While they offer significant convenience to humans, th
arXiv:2512.21877v3 Announce Type: replace-cross Abstract: Cricket is the second most popular sport worldwide, with billions of fans seeking advanced statistical insights unavailable through standard w
arXiv:2604.10425v1 Announce Type: new Abstract: Recent advancements in Vision-Language Models (VLMs) have revolutionized general visual understanding. However, their application in the food domain rem
arXiv:2604.09666v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) and its graph-based extensions (GraphRAG) are effective paradigms for improving large language model (LLM) reason
arXiv:2601.03926v2 Announce Type: replace Abstract: The deployment of Large Vision-Language Models (LVLMs) for real-world document question answering is often constrained by dynamic, user-defined poli
arXiv:2604.10200v1 Announce Type: new Abstract: As Vision-Language Models (VLMs) become integral to educational decision-making, ensuring their fairness is paramount. However, current text-centric eva