Knowledge catalogue
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:2606.00543v1 Announce Type: new Abstract: In Vision-Language Models (VLMs), high-resolution images produce a large number of visual tokens, resulting in high computational costs and KV-cache ove
arXiv:2603.27223v2 Announce Type: replace-cross Abstract: We present EuraGovExam, a multilingual and multimodal benchmark sourced from real-world civil service examinations across five representative
arXiv:2503.05500v3 Announce Type: replace-cross Abstract: General-purpose multilingual vector representations, used in retrieval, regression and classification, are traditionally obtained from bidirec
arXiv:2512.16167v3 Announce Type: replace-cross Abstract: Decentralized LLM-based multi-agent service economies face three vulnerabilities that undermine traditional trust mechanisms: reduced cost of
arXiv:2606.01884v1 Announce Type: new Abstract: Practical non-invasive Brain-Computer Interface (BCI) systems require EEG decoders with strong cross-subject generalization and minimal calibration. How
arXiv:2508.02812v3 Announce Type: replace Abstract: Causal graphical models can encode large amounts structural knowledge, both from the background knowledge of domain experts and the structural knowl
arXiv:2606.00278v1 Announce Type: new Abstract: For many real-world systems, causal ground truth is difficult to obtain, making claims about causal effects hard to assess. We develop methods for evalu
arXiv:2606.00103v1 Announce Type: new Abstract: We introduce a multi-turn interactive framework for reasoning evaluation that treats reasoning as active evidence acquisition and belief updating. Where
arXiv:2606.02016v1 Announce Type: new Abstract: Algorithm Selection (AS) aims to automatically identify the most suitable optimization algorithm for a given problem instance by leveraging measurable p
arXiv:2602.22221v2 Announce Type: replace-cross Abstract: Search engines and AI-powered systems increasingly mediate access to factual information, yet their reliability remains difficult to evaluate
arXiv:2511.20615v2 Announce Type: replace-cross Abstract: This study aimed to explore the application of deep neural networks for whole-body human posture prediction during dynamic load-reaching activ
arXiv:2310.10322v3 Announce Type: replace Abstract: Large language models (LLMs) are prone to hallucinate unintended text due to false or outdated knowledge. Since retraining LLMs is resource intensiv
arXiv:2606.01840v1 Announce Type: new Abstract: Many difficult search problems cannot be solved by algorithms such as A* using only RAM. Search algorithms which use external memory such as SSDs and HD
arXiv:2606.01280v1 Announce Type: new Abstract: Earth Observation (EO) is undergoing a significant transformation driven by the deployment of novel sensing technologies. Traditional frame-based optica
arXiv:2606.01557v1 Announce Type: new Abstract: Everywhere learning is a new paradigm whereby Artificial Intelligence (AI) systems are trained to satisfy loss constraints with probability one over the
arXiv:2606.01730v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as heuristic advisors for black-box optimization, yet their suggestions and self-reported confidence
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:2606.01756v1 Announce Type: new Abstract: Large vision-language models (LVLMs) achieve strong performance on image and video understanding tasks, but their inference efficiency is constrained by
arXiv:2606.02418v1 Announce Type: cross Abstract: Quantum LDPC code discovery requires searching large algebraic design spaces while reliably certifying the parameters and equivalence classes of any c
arXiv:2606.00112v1 Announce Type: cross Abstract: Evolutionary systems have demonstrated remarkable results in creative domains, with recent applications in generative typography, design, and music. H
arXiv:2606.01617v1 Announce Type: cross Abstract: Large language models excel at general tasks but underperform smaller supervised models in specialized, high-stakes domains where training labels are
arXiv:2606.00018v1 Announce Type: cross Abstract: Ambient AI documentation systems generate clinical note drafts that clinicians frequently revise before signing off into electronic health records, ye
arXiv:2606.01072v1 Announce Type: cross Abstract: Imitation learning enables robots to learn how to execute tasks via observation. However, real-world environments like homes and offices are often sev
arXiv:2606.01160v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly used with formal interactive theorem provers such as Lean 4. Scaling these systems with reinforcement lear
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:2606.02049v1 Announce Type: new Abstract: The increasing integration of renewable energy sources into power systems, particularly in buildings equipped with photovoltaic (PV) panels and energy s
arXiv:2606.00949v1 Announce Type: cross Abstract: We propose a method combining Multi-Agent Deep Reinforcement Learning (MARL) and eXplainable Deep Learning (XDL) to reduce drag in wall-bounded turbul
arXiv:2606.02402v1 Announce Type: new Abstract: The rapid advancement of AI-driven video generation has transformed content creation, while simultaneously increasing the risk of misinformation through
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:2606.01608v1 Announce Type: new Abstract: Most existing extreme compression methods fail to achieve an optimal rate-distortion-perception trade-off, as they typically prioritize perceptual fidel
arXiv:2506.10677v3 Announce Type: replace-cross Abstract: We study A/B testing, the standard protocol for measuring the performance gain of a new decision system relative to a baseline. Traditional A/
arXiv:2606.00442v1 Announce Type: new Abstract: Many machine learning techniques rely on approximating a loss function's curvature, but this is notoriously hard to do at the scale of modern deep netwo
arXiv:2509.15234v2 Announce Type: replace Abstract: Multimodal learning from paired medical images and clinical text is a central challenge in medical data-driven informatics, where effective cross-mo
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:2606.02490v1 Announce Type: new Abstract: This work studies neural architectures for classifying symmetric positive-definite matrices, focusing on congruence-like layers, in which the input matr
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:2606.00795v1 Announce Type: cross Abstract: Metamodels for discrete-event simulations approximate the behavior of simulation models without running expensive simulations. Prior work introduced m
arXiv:2606.02011v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) rely on long reasoning traces, making inference expensive. While low-bit quantization reduces per-token decoding cost, we
arXiv:2606.01573v1 Announce Type: new Abstract: Gaussian splatting has shown strong potential for 3D reconstruction and novel view synthesis. However, most existing methods require accurate camera par
arXiv:2606.01964v1 Announce Type: new Abstract: The way our eyes move while reading provides valuable insights into both the reader's cognitive processes and the properties of the text. In particular,
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:2603.23485v2 Announce Type: replace-cross Abstract: Standard evaluation practices assume that large language model (LLM) outputs are stable when prompts are embedded in contextually equivalent d
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:2606.00355v1 Announce Type: new Abstract: Animal ecology data collection using drones represents a substantial investment of time, expertise, and financial resources. Yet most existing datasets
arXiv:2606.01159v1 Announce Type: new Abstract: We study two-player zero-sum games (TPZSGs) with bandit feedback under fairness constraints requiring every action to be played with probability at leas
arXiv:2606.01306v1 Announce Type: new Abstract: While Transformer-based architectures have established themselves as a dominant paradigm in Multivariate Time Series Forecasting (MTSF), their core self
arXiv:2606.00765v1 Announce Type: new Abstract: LLM-based agents increasingly solve complex tasks through long trajectories involving reasoning steps, tool calls, and inter-agent communication. Howeve
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
arXiv:2606.02068v1 Announce Type: cross Abstract: Recently, novel view synthesis has witnessed remarkable progress, with mainstream methods such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatt
arXiv:2606.01521v1 Announce Type: new Abstract: A central problem in machine learning is that models can achieve near-perfect training performance while generalizing substantially less well to unseen
arXiv:2602.05293v2 Announce Type: replace Abstract: SAM3D enables scalable, open-world 3D reconstruction from complex scenes, yet its deployment is hindered by prohibitive inference latency. In this w
arXiv:2606.02218v1 Announce Type: cross Abstract: Synchronous reinforcement learning methods such as Group Relative Policy Optimization (GRPO) provide stable and reproducible on-policy training, but t
arXiv:2601.06199v3 Announce Type: replace-cross Abstract: Scaling Multimodal Large Language Models (MLLMs) to long-form speech is bottlenecked by the explosive growth of input tokens. Unlike images or
arXiv:2606.02307v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models are increasingly used as generalist robot policies, yet their evaluation still relies largely on static benchmarks t
arXiv:2511.15645v3 Announce Type: replace Abstract: Pedestrian inertial odometry (PIO) estimates autonomous pedestrian motion using only acceleration and angular velocity measurements collected by an
arXiv:2606.01207v1 Announce Type: new Abstract: The choice between cross-attention and concatenation for multimodal fusion remains governed by practitioner intuition rather than principled understandi
arXiv:2606.01289v1 Announce Type: new Abstract: Zero-shot time series forecasting aims to predict future values for previously unseen series, requiring models to generalize temporal dynamics beyond th
arXiv:2509.22907v2 Announce Type: replace Abstract: Conformal Prediction (CP) is a widely used technique for quantifying uncertainty in machine learning models. In its standard form, CP offers probabi
arXiv:2303.04345v2 Announce Type: replace Abstract: Federated learning (FL) is a promising framework that models distributed machine learning while protecting the privacy of clients. However, FL suffe