Knowledge catalogue
arXiv:2604.09234v1 Announce Type: cross Abstract: The King Wen sequence of the I-Ching (c. 1000 BC) orders 64 hexagrams -- states of a six-dimensional binary space -- in a pattern that has puzzled sch
arXiv:2509.25210v3 Announce Type: replace-cross Abstract: To gain finer regional forecasts, many works have explored the regional integration from the global atmosphere, e.g., by solving boundary equa
arXiv:2604.08597v1 Announce Type: cross Abstract: Extracting structured knowledge from unstructured data still faces practical limitations: entity and event extraction pipelines remain brittle, knowle
arXiv:2604.09361v1 Announce Type: new Abstract: In this paper, we propose a stochastic-dimension frozen sampled neural network (SD-FSNN) for solving a class of high-dimensional Gross-Pitaevskii equati
arXiv:2604.09502v1 Announce Type: new Abstract: AI agents increasingly operate in multi-agent environments where outcomes depend on coordination. We distinguish primary algorithmic monoculture -- base
arXiv:2512.21334v2 Announce Type: replace Abstract: We present Streamo, a real-time streaming video LLM that serves as a general-purpose interactive assistant. Unlike existing online video models that
arXiv:2604.09000v1 Announce Type: new Abstract: Vision agent memory has shown remarkable effectiveness in streaming video understanding. However, storing such memory for videos incurs substantial memo
arXiv:2604.09132v1 Announce Type: new Abstract: Recent advancements in autoregressive transformers have demonstrated remarkable potential for generating artist-quality meshes. However, the token order
arXiv:2604.08620v1 Announce Type: cross Abstract: Reinforcement learning is typically treated as a uniform, data-driven optimization process, where updates are guided by rewards and temporal-differenc
arXiv:2604.08809v1 Announce Type: new Abstract: Scalable Vector Graphics (SVG) represent visual content as structured, editable code. Each element (path, shape, or text node) can be individually inspe
arXiv:2604.09324v1 Announce Type: new Abstract: Reconstructing photorealistic and topology-aware human avatars from monocular videos remains a significant challenge in the fields of computer vision an
arXiv:2604.08578v1 Announce Type: cross Abstract: High-quality labeled data is critical for training reliable machine learning and deep learning models, yet manual annotation remains costly and error-
arXiv:2511.08798v2 Announce Type: replace-cross Abstract: LLM agents with tool-calling capabilities often fail when user instructions are ambiguous or incomplete, leading to incorrect invocations and
arXiv:2604.09158v1 Announce Type: cross Abstract: Supporting students in developing diagnostic reasoning is a key challenge across educational domains. Novices often face cognitive biases such as prem
arXiv:2602.17330v3 Announce Type: replace-cross Abstract: Comparative analysis of adaptive immune repertoires at population scale is hampered by two practical bottlenecks: the near-quadratic cost of p
arXiv:2604.08477v1 Announce Type: cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has significantly improved large language model (LLM) reasoning in formal domains such as mathem
arXiv:2604.08621v1 Announce Type: new Abstract: In consumer applications, Customer Relationship Management (CRM) has traditionally relied on the manual optimisation of static, rule-based messaging str
arXiv:2604.08555v1 Announce Type: new Abstract: Physician-physician discussions of patient cases represent a rich source of clinical knowledge and reasoning that could feed AI agents to enrich and eve
arXiv:2604.09411v1 Announce Type: new Abstract: Reliable 3D dynamic perception requires models that can anticipate motion beyond predefined categories, yet progress is hindered by the scarcity of dens
arXiv:2604.09091v1 Announce Type: new Abstract: The use of synthetic data in machine learning applications and research offers many benefits, including performance improvements through data augmentati
arXiv:2604.08921v1 Announce Type: new Abstract: Accurate 3D human keypoints localization is a critical technology enabling robots to achieve natural and safe physical interaction with users. Conventio
arXiv:2604.09115v1 Announce Type: cross Abstract: Wilderness Search and Rescue (WiSAR) represents a longstanding and critical societal challenge, demanding innovative and automatic technological solut
arXiv:2604.09547v1 Announce Type: new Abstract: Token pruning has emerged as a mainstream approach for developing efficient Video Large Language Models (Video LLMs). This work revisits and advances th
arXiv:2505.12318v2 Announce Type: replace Abstract: Federated Parameter-Efficient Fine-Tuning (Fed-PEFT) enables lightweight adaptation of large pre-trained models in federated learning settings by up
arXiv:2604.08726v1 Announce Type: new Abstract: Bimanual manipulation requires reasoning about where to interact with an object and which arm should perform each action, a joint affordance localizatio
arXiv:2604.09377v1 Announce Type: new Abstract: Large language models (LLMs) exhibit substantial variability in performance and computational cost across tasks and queries, motivating routing systems
arXiv:2311.14756v2 Announce Type: replace-cross Abstract: Data-Free Meta-Learning (DFML) aims to enable efficient learning of unseen few-shot tasks, by meta-learning from multiple pre-trained models w
arXiv:2509.24169v2 Announce Type: replace Abstract: Large Language Models (LLMs) can perform new tasks from in-context demonstrations, a phenomenon known as in-context learning (ICL). Recent work sugg
arXiv:2604.08948v1 Announce Type: new Abstract: While Large Language Models (LLMs) excel in various general domains, they exhibit notable gaps in the highly specialized, knowledge-intensive, and legal
arXiv:2604.08563v1 Announce Type: cross Abstract: Extended reasoning models represent a transformative shift in Large Language Model (LLM) capabilities by enabling explicit test-time computation for c
arXiv:2604.08870v1 Announce Type: cross Abstract: Student dropout is a persistent concern in Learning Analytics, yet comparative studies frequently evaluate predictive models under heterogeneous proto
arXiv:2604.09067v1 Announce Type: new Abstract: Data augmentation is a crucial technique for improving model generalization and robustness, particularly in deep learning models where training data is
arXiv:2312.09436v3 Announce Type: replace-cross Abstract: The recent development of connected and automated vehicle (CAV) technologies has spurred investigations to optimize dense urban traffic to max
arXiv:2604.09107v1 Announce Type: cross Abstract: Modern LLM reinforcement learning (RL) workloads require a highly efficient weight transfer system to scale training across heterogeneous computationa
arXiv:2604.08977v1 Announce Type: new Abstract: Active learning (AL) is a training paradigm for selecting unlabeled samples for annotation to improve model performance on a test set, which is useful w
arXiv:2604.09047v1 Announce Type: new Abstract: Dental implant abutments serve as the geometric and biomechanical interface between the implant fixture and the prosthetic crown, yet their design relie
arXiv:2604.09388v1 Announce Type: cross Abstract: AI coding tools are widely adopted, but most teams plateau at prompt-and-review without a framework for systematic progression. This paper presents th
arXiv:2604.09271v1 Announce Type: new Abstract: The transition to electric mobility hinges on maximising aggregate adoption while also facilitating equitable access. This study examines whether the 'c
arXiv:2604.09229v1 Announce Type: cross Abstract: Von Economo neurons (VENs) are large bipolar projection neurons found exclusively in the anterior cingulate cortex (ACC) and frontal insula of species
arXiv:2601.23045v2 Announce Type: replace Abstract: As AI becomes more capable, we entrust it with more general and consequential tasks. The risks from failure grow more severe with increasing task sc
arXiv:2512.17425v2 Announce Type: replace Abstract: Exoskeletons modulate human movement across diverse applications, from performance augmentation to daily-life assistance. These systems often enforc
arXiv:2604.09034v1 Announce Type: new Abstract: The rapid evolution of Large Language Models (LLMs) has significantly impacted the field of natural language processing, but their growing complexity ra
arXiv:2601.07220v3 Announce Type: replace Abstract: Multilingual language models (LMs) promise broader NLP access, yet current systems deliver uneven performance across the world's languages. This sur
arXiv:2601.01580v2 Announce Type: replace-cross Abstract: Self-reflection capabilities emerge in Large Language Models after RL post-training, with multi-turn RL achieving substantial gains over SFT c
arXiv:2511.09829v3 Announce Type: replace Abstract: Adversarial patches have emerged as a popular privacy-preserving approach for resisting AI-driven surveillance systems. However, their conspicuous a
arXiv:2604.09150v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) achieve strong performance on complex tasks by leveraging long Chain-of-Thought (CoT), but often suffer from overthinking,
arXiv:2604.09426v1 Announce Type: cross Abstract: Three-dimensional (3D) data visualizations, such as surface plots, are vital in STEM fields from biomedical imaging to spectroscopy, yet remain largel
arXiv:2604.09368v1 Announce Type: cross Abstract: Large language model (LLM) agents are increasingly deployed as scalable user simulators for recommender system evaluation. Yet existing simulators per
arXiv:2604.08602v1 Announce Type: cross Abstract: Background: Server-based screening tools impose subscription costs, while open-source alternatives require coding skills. Objectives: We developed a b
arXiv:2602.03342v2 Announce Type: replace-cross Abstract: Text-conditioned diffusion models have advanced image and video super-resolution by using prompts as semantic priors, and modern super-resolut
arXiv:2604.09220v1 Announce Type: new Abstract: Implicit neural video representations encode entire video sequences within the parameters of a neural network and enable constant time frame reconstruct
arXiv:2604.09439v1 Announce Type: cross Abstract: In this paper, we propose a sequential recommendation model that integrates Time-aware personalization, Multi-interest personalization, and Explanatio
arXiv:2603.01400v2 Announce Type: replace Abstract: Video Large Language Models (VLLMs) demonstrate strong video understanding but suffer from inefficiency due to redundant visual tokens. Existing pru
arXiv:2602.16821v2 Announce Type: replace Abstract: We propose TopoFlow (Topography-aware pollutant Flow learning), a physics-guided neural network for efficient, high-resolution air quality predictio
arXiv:2604.09057v1 Announce Type: new Abstract: Audio-video (AV) generation has recently made strong progress in perceptual quality and multimodal coherence, yet generating content with plausible moti
arXiv:2604.08945v1 Announce Type: new Abstract: Accurate object geometry estimation is essential for many downstream tasks, including robotic manipulation and physical interaction. Although vision is
arXiv:2604.08780v1 Announce Type: cross Abstract: World models promise a paradigm shift in robotics, where an agent learns the underlying physics of its environment once to enable efficient planning a
arXiv:2604.09519v1 Announce Type: new Abstract: World models have emerged as a unifying paradigm for learning latent dynamics, simulating counterfactual futures, and supporting planning under uncertai
arXiv:2603.27817v3 Announce Type: replace-cross Abstract: Street-level imagery contains personally identifiable information (PII), some of which is context-dependent. Existing anonymization methods ei
arXiv:2604.07720v2 Announce Type: replace Abstract: Deep Research (DR) requires LLM agents to autonomously perform multi-step information seeking, processing, and reasoning to generate comprehensive r