Knowledge catalogue
arXiv:2606.02310v1 Announce Type: new Abstract: Flooding is the most pervasive natural disaster worldwide. Timely and accurate flood inundation mapping are essential for informing disaster risk manage
arXiv:2606.01277v1 Announce Type: cross Abstract: Current end-to-end autonomous driving systems predominantly rely on frame-based sensors, which suffer from inherent perception latency and motion blur
arXiv:2606.00562v1 Announce Type: new Abstract: The emerging paradigm of 'thinking with images' embeds visual states into intermediate reasoning steps, defining a new frontier for Vision-Language Mode
arXiv:2606.01567v1 Announce Type: cross Abstract: Large language model (LLM) agents increasingly rely on reusable skills i.e. documents describing task-specific procedures. However, this introduces a
arXiv:2606.00076v1 Announce Type: new Abstract: The present work introduces DefocusTrackerAI, a generalized deep-learning framework for the automatic detection and position estimation of defocused par
arXiv:2606.00007v1 Announce Type: new Abstract: As AI agents transition from isolated tools to collaborative participants in shared knowledge ecosystems, governing collective knowledge curation become
arXiv:2603.13312v2 Announce Type: replace-cross Abstract: Interior design is a requirements-to-visual-plan generation process that must simultaneously satisfy verifiable spatial feasibility and compar
arXiv:2512.10236v2 Announce Type: replace-cross Abstract: Modern ML workloads demand distributing training and inference across multiple GPUs. However, these parallelization techniques often suffer fr
arXiv:2606.00203v1 Announce Type: new Abstract: Dominant approaches to Knowledge Base Question Answering (KBQA) fall into two categories. First is the generation of a formal query that suffers from br
arXiv:2606.02342v1 Announce Type: new Abstract: Dynamic aspects of handwriting are critical for assessing developmental disorders such as dysgraphia and are typically captured using digitizing tablets
arXiv:2606.00392v1 Announce Type: cross Abstract: AI-text detectors are vulnerable to paraphrasing and detector-guided paraphrasing attacks, but existing detector-evasion methods often lack precise co
arXiv:2606.02274v1 Announce Type: new Abstract: End-to-end manipulation policies, combined with web-scale pretrained Vision-Language Models (VLMs), show the promise for generalizable and dexterous rob
arXiv:2606.00686v1 Announce Type: new Abstract: The prevailing paradigm in large language model (LLM) alignment operates via erasure, filtering unsafe data or training models to strictly refuse harmfu
arXiv:2606.01109v1 Announce Type: cross Abstract: Citation extraction tools are designed for the structured end-of-document bibliographies of the natural sciences, but law and humanities scholarship c
arXiv:2606.00159v1 Announce Type: cross Abstract: Deep neural network (DNN)-based object detectors are widely used for analyzing aerial and satellite imagery in applications such as environmental moni
arXiv:2508.20072v4 Announce Type: replace Abstract: Vision-Language-Action (VLA) models adapt large vision-language backbones to map images and instructions into robot actions. However, prevailing VLA
arXiv:2603.10619v2 Announce Type: replace Abstract: The recent success of large pre-trained language models (PLMs) has motivated their integration into topic modeling. However, PLM-augmented topic mod
arXiv:2606.01824v1 Announce Type: new Abstract: We present DisFlow, a novel framework for online scene flow estimation from distance field that enables 6DoF dynamic object pose estimation, motion trac
arXiv:2509.25837v3 Announce Type: replace-cross Abstract: Large language models (LLMs) deliver remarkable performance but are costly to deploy, motivating knowledge distillation (KD) for efficient inf
arXiv:2606.01215v1 Announce Type: cross Abstract: Current 3D spatial reasoning methods face a fundamental trade-off: neuro-symbolic 3D (NS3D) concept learners achieve interpretable reasoning through c
arXiv:2606.02002v1 Announce Type: new Abstract: Blind image quality assessment (BIQA) aims to predict perceived image quality without access to a reference image. Classical natural scene statistics (N
arXiv:2606.00091v1 Announce Type: cross Abstract: Joint Embedding Predictive Architectures (JEPAs) have reshaped self-supervised representation learning in vision. The recent LLM-JEPA ported JEPA to a
arXiv:2606.02357v1 Announce Type: cross Abstract: Tool-augmented multimodal agents show strong benchmark gains, often taken as evidence that agents have learned to use tools. We argue that this interp
arXiv:2606.00477v1 Announce Type: new Abstract: Unified multimodal models (UMMs) have emerged as a promising paradigm for general-purpose multimodal intelligence. As they are deployed in real-world ap
arXiv:2606.02345v1 Announce Type: cross Abstract: Many machine learning problems, including similarity learning, ranking, and clustering, rely on empirical pairwise loss functions whose quadratic comp
arXiv:2606.01393v1 Announce Type: cross Abstract: Document parsing and recognition are fundamental capabilities for vision-language models (VLMs) and document processing systems. However, existing Opt
arXiv:2606.00012v1 Announce Type: cross Abstract: Multi-party dialogue discourse parsing aims to identify dependency structures and relation types between utterances in conversations. Previous studies
arXiv:2606.02521v1 Announce Type: cross Abstract: One-step text-to-image generators are attractive for deployment because they generate an image with a single forward pass, but preference finetuning t
arXiv:2606.00519v1 Announce Type: new Abstract: We present DriveAnchor, a three-stage framework for autonomous driving planning that achieves behavioral diversity, controllability, and safety in a com
arXiv:2606.00313v1 Announce Type: cross Abstract: Robust deployment of deep reinforcement learning (DRL) policies on real robots remains challenging due to discrepancies between simulation and real-wo
arXiv:2606.01434v1 Announce Type: new Abstract: Drug-information question answering is a high-stakes setting where hallucinated facts can mislead clinical decision-making and the provenance of each ci
arXiv:2511.04791v2 Announce Type: replace Abstract: Modern LLM serving systems must sustain high throughput while meeting strict latency SLOs across two distinct inference phases: compute-intensive pr
arXiv:2606.00804v1 Announce Type: cross Abstract: Enterprise multi-agent systems increasingly expose multiple coordination patterns, but deployments often lack evidence for when to use consensus, deba
arXiv:2606.01828v1 Announce Type: cross Abstract: Large language model-driven multi-agent systems enhance the reliability of complex reasoning tasks through multi-round deliberation, role specializati
arXiv:2606.01634v1 Announce Type: cross Abstract: Generating realistic time series is essential for scientific research and real-world applications. However, existing methods often emphasize overall d
arXiv:2602.03912v4 Announce Type: replace Abstract: This paper investigates the performance of Echo State Networks (ESNs) for univariate forecasting of monthly and quarterly time series from the M4 Fo
arXiv:2606.02136v1 Announce Type: new Abstract: Neural asymmetric routing models increasingly encode directionality through matrix representations and asymmetry-aware attention. The final routing acti
arXiv:2606.00552v1 Announce Type: cross Abstract: Multi-robot systems (MRS) increasingly offload compute-intensive perception tasks to edge nodes to meet strict time-sensitive Quality-of-Service (QoS)
arXiv:2606.02406v1 Announce Type: new Abstract: This paper presents a competitive solution to the S23DR Challenge 2026, which aims to reconstruct 3D house roof wireframe models from sparse SfM point c
arXiv:2606.02166v1 Announce Type: new Abstract: Epilepsy is one of the most common neurological disorders globally, characterized by recurring seizures and significantly impacting the quality of life.
arXiv:2606.01382v1 Announce Type: cross Abstract: Preference alignment is central to improving large language models, but standard reward-based formulations can be restrictive when human preferences a
arXiv:2502.04646v2 Announce Type: replace-cross Abstract: Weighted sampling -- sampling from a probability density function (PDF) proportional to the product of a base PDF and a weight function -- is
arXiv:2606.02246v1 Announce Type: new Abstract: To operate in the physical world, embodied agents must perceive their environment in an 'always-on' fashion, selectively accessing the most informative
arXiv:2606.00151v1 Announce Type: cross Abstract: In reinforcement learning (RL), agents benefit from exploration only because they repeatedly encounter similar states: trying different actions can im
arXiv:2606.01269v1 Announce Type: new Abstract: Transitive inference is the challenge of inferring that A < C from knowing only adjacent relations (A < B, B < C). It is solved by humans and animals no
arXiv:2606.00506v1 Announce Type: new Abstract: Energy consumption prediction is essential for efficient grid management, demand-side optimization, and sustainable energy planning. Although advanced m
arXiv:2411.12321v2 Announce Type: replace Abstract: Principal component analysis (PCA) and its sparse variants (sPCA) are widely used as a precursor to independent component analysis (ICA) for blind s
arXiv:2606.00869v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has become central to LLM reasoning, but its outcome-level rewards can make models more willing to
arXiv:2606.02339v1 Announce Type: cross Abstract: Entropy minimization (EM) is the dominant objective for test-time adaptation, yet its failure mode, model collapse, remains poorly understood. In this
arXiv:2606.00302v1 Announce Type: cross Abstract: Despite being ubiquitous in science, clustering remains a technique whose results are not quantitatively scrutinized via a framework. We present an an
arXiv:2603.03312v3 Announce Type: replace-cross Abstract: Decoding natural language from non-invasive EEG signals is a promising yet challenging task. However, current state-of-the-art models remain c
arXiv:2606.01602v1 Announce Type: cross Abstract: Pairwise dependence measures such as correlation and causality are fundamental to temporal data mining, yet there is still no principled and robust wa
arXiv:2606.01767v1 Announce Type: new Abstract: Electroencephalography (EEG) is the cornerstone of non-invasive brain-computer interfaces (BCIs), yet conventional decoding relies on fragmented, task-s
arXiv:2411.05196v3 Announce Type: replace Abstract: This study presents DhondtXAI as a SHAP-independent, D'Hondt-based attribution framework for tabular XAI. Instead of model-native feature importance
arXiv:2606.01271v1 Announce Type: new Abstract: The rapid growth of the satellite industry has driven a significant increase in geospatial data acquisition, highlighting a critical bottleneck: the sev
arXiv:2602.02763v3 Announce Type: replace Abstract: Interpretable time series deep learning systems are often assessed by checking temporal consistency on explanations, implicitly treating this as evi
arXiv:2606.01041v1 Announce Type: new Abstract: Experience learning has achieved promising results in enhancing LLM agent planning and reasoning by integrating past interactions as reusable knowledge.
arXiv:2606.02079v1 Announce Type: new Abstract: The main goal of active finetuning is to improve a pretrained model's performance on a specific task or domain by finetuning it with carefully selected
arXiv:2606.01719v1 Announce Type: cross Abstract: Machine learning models trained on sensitive data can inadvertently leak population-level information about their training distributions -- a threat k
arXiv:2512.08048v3 Announce Type: replace Abstract: Recent continual test-time adaptation (CTTA) methods adopt masked image modeling to stabilize learning under distribution shift, yet each treats its