Knowledge catalogue
arXiv:2608.08709v1 Announce Type: new Abstract: The reliability of AI generative models is typically measured by output correctness, yet in practice it depends on the effort required to verify those o
arXiv:2608.09775v1 Announce Type: new Abstract: Accurate air quality forecasting is essential for public health and urban environmental management, but remains challenging because pollutant channels d
arXiv:2604.02478v2 Announce Type: replace Abstract: Deep learning models excel at detecting anomaly patterns in normal data. However, they do not provide a direct solution for anomaly classification a
arXiv:2608.09214v1 Announce Type: cross Abstract: Recent Retrieval-Augmented Generation (RAG) systems increasingly combine vector retrieval with structured knowledge, such as Graph RAG and Filtered ve
arXiv:2608.09355v1 Announce Type: new Abstract: RGBA videos combine RGB appearance with an alpha channel, enabling animated assets to be applied across arbitrary backgrounds, which are heavily used in
arXiv:2312.12763v3 Announce Type: replace Abstract: Generating realistic human motion sequences from text descriptions is a challenging task that requires capturing the rich expressiveness of both nat
arXiv:2608.07651v1 Announce Type: new Abstract: Large language models (LLMs) show promise in medical image interpretation but suffer from hallucination, limited accuracy, and run-to-run inconsistency.
arXiv:2608.09142v1 Announce Type: new Abstract: Treatment planning in precision oncology requires synthesizing heterogeneous patient information with rapidly evolving clinical guidelines to ensure gui
arXiv:2608.07542v1 Announce Type: new Abstract: Autonomous research loops driven by large language models can run machine-learning experiments at scale but tend to drift toward local refinements of wh
arXiv:2607.15511v2 Announce Type: replace-cross Abstract: Serverless computing provides automatic resource management and pay-per-use execution, but effective autoscaling remains challenging because o
arXiv:2608.07537v1 Announce Type: cross Abstract: In this study, we propose a framework that incorporates subjective evaluations provided by a Vision-Language Model (VLM) into the fitness evaluation a
arXiv:2504.18587v2 Announce Type: replace-cross Abstract: Reinforcement learning has emerged as a powerful approach for improving the reasoning capabilities of large language models, as demonstrated b
arXiv:2608.09246v1 Announce Type: new Abstract: Industrial processes are complex systems composed of multiple interacting sensors that generate multivariate time series (MTS). Detecting anomalies in s
arXiv:2506.14194v2 Announce Type: replace Abstract: We present a theory for the construction of out-of-distribution (OOD) detection features for neural networks. We introduce random features for OOD t
arXiv:2509.03910v2 Announce Type: replace-cross Abstract: We formulate inverse problems in a Bayesian framework and aim to train an invertible generative model that is capable of simulation (i.e., sam
arXiv:2504.08811v3 Announce Type: replace Abstract: Modern learning systems often struggle with joint learning across diverse scenarios and immediate adaptation to new ones, because they rely heavily
arXiv:2506.19112v3 Announce Type: replace Abstract: Underactuated wheeled vehicles are commonly studied as nonholonomic systems with periodic actuation. Twistcar is a classical example inspired by a r
arXiv:2608.08401v1 Announce Type: new Abstract: T2-weighted (T2w) brain MRI provides fluid-sensitive soft-tissue contrast that is important for neuro-oncology and radiotherapy planning. However, T2w s
arXiv:2608.08701v1 Announce Type: new Abstract: Advanced automated driving presents significant potential to improve modern automotive safety systems, but it depends highly on the reliable activation
arXiv:2608.08732v1 Announce Type: cross Abstract: Multi-vector vision-language retrievers enable fine-grained Visual Document Retrieval (VDR) through late interaction, but storing and scoring hundreds
arXiv:2608.07775v1 Announce Type: new Abstract: Mobile agents have achieved promising results on clean online benchmarks such as AndroidWorld, yet their performance often degrades sharply in real-worl
arXiv:2603.05868v2 Announce Type: replace Abstract: Despite remarkable progress in Vision-Language-Action models (VLAs) for robot manipulation, these large pre-trained models require fine-tuning to be
arXiv:2608.08059v1 Announce Type: new Abstract: Post-Editing (PE) of Machine Translation (MT) output often involves repeating the same lexical and terminological corrections across many segments, espe
arXiv:2608.07471v1 Announce Type: cross Abstract: This study considers the task of applying artificial intelligence to recognize bank fraud. In recent years, due to the COVID19 pandemic, bank fraud ha
arXiv:2608.08872v1 Announce Type: new Abstract: In this paper we develop quantitative approximation results for shallow neural networks constructed using a dictionary based on metaplectic operators. F
arXiv:2506.17455v3 Announce Type: replace Abstract: Robust visual recognition in underwater environments remains a significant challenge due to complex distortions such as turbidity, low illumination,
arXiv:2608.08883v1 Announce Type: new Abstract: Recent advances in retrieval-augmented generation (RAG) and large language models (LLMs) enable researchers to integrate AI into scientific workflows. H
arXiv:2608.08256v1 Announce Type: new Abstract: Pretrained Transformer encoders such as AraBERT, MARBERT, and CAMeLBERT have become the standard backbone for Arabic natural language understanding, but
arXiv:2608.08424v1 Announce Type: cross Abstract: Conformal changepoint localization turns any score into a confidence set for the changepoint with finite-sample coverage. Coverage is universal; effic
arXiv:2608.09874v1 Announce Type: new Abstract: Agentic artificial intelligence has shown great promise in automating algorithm design, but scaling similar techniques to computer microarchitecture dis
arXiv:2608.08086v1 Announce Type: new Abstract: Diffusion language models (DLMs) iteratively refine a sequence, allowing earlier predictions to be revised as context evolves. This rollback capability
arXiv:2604.04902v2 Announce Type: replace Abstract: Latent reasoning models (LRMs) have attracted significant research interest due to their low inference cost (relative to explicit reasoning models)
arXiv:2406.14373v3 Announce Type: replace Abstract: The emergence of Large Language Models (LLMs) and advancements in Artificial Intelligence (AI) offer an opportunity for computational social science
arXiv:2608.09315v1 Announce Type: new Abstract: While mathematical models act as vital decision support systems for operational Air Traffic Flow and Capacity Management (ATFCM), existing approaches is
arXiv:2606.21597v2 Announce Type: replace-cross Abstract: The open-source ecosystem on GitHub lacks a systematic hierarchical taxonomy of software repositories. GitHub Topics, the dominant organizatio
arXiv:2509.08421v2 Announce Type: replace-cross Abstract: For multimedia spatial intelligence through time, multi-view multi-object tracking (MVMOT) suffers from persistent challenges in maintaining c
arXiv:2408.07490v4 Announce Type: replace Abstract: In unsupervised image anomaly detection, reconstruction-based methods learn normal patterns for data reconstruction, but often undesirably reconstru
arXiv:2603.00040v3 Announce Type: replace-cross Abstract: Achieving reliable 4-bit attention is a prerequisite for end-to-end FP4 computation on emerging FP4-capable GPUs, yet attention remains the ma
arXiv:2608.07895v1 Announce Type: cross Abstract: Robot demonstration datasets used to train vision-language-action policies can contain a subtle but harmful failure mode: trajectories that are behavi
arXiv:2608.07550v1 Announce Type: new Abstract: Vision-language models return structured chest-radiograph findings through interfaces exposing no confidence score, so a receiving institution cannot re
arXiv:2608.08822v1 Announce Type: new Abstract: Cognitive decision-making research depends on diverse scenarios with carefully controlled complexity, yet manual production is slow, inconsistent, and b
arXiv:2511.19517v3 Announce Type: replace-cross Abstract: Multi-turn conversational attacks, which leverage psychological principles like Foot-in-the-Door (FITD), where a small initial request paves t
arXiv:2606.20336v2 Announce Type: replace Abstract: Autonomous vehicles must plan trajectories that satisfy multiple requirements, such as safety, traffic-rule compliance, and passenger comfort. Howev
arXiv:2510.04465v3 Announce Type: replace-cross Abstract: LLM agents require personal information for personalization in order to effectively act on users' behalf, but this raises privacy concerns tha
arXiv:2601.22758v2 Announce Type: replace Abstract: Large language model agents repeatedly encounter related tasks, yet systems that learn from trajectories commit every lesson to one predefined artif
arXiv:2603.00077v3 Announce Type: replace-cross Abstract: Rubric-based LLM judges have become indispensable for evaluating and optimizing systems on non-verifiable tasks, where success cannot be reduc
arXiv:2608.09638v1 Announce Type: new Abstract: Theory of Mind (ToM) is essential for agent interactions, yet existing evaluations either rely on static scenarios that oversimplify mental-state reason
arXiv:2608.07873v1 Announce Type: new Abstract: We introduce the workbook time machine, a pipeline that automatically creates benchmarks evaluating the ability of language models to create derived obj
arXiv:2608.08674v1 Announce Type: new Abstract: In the operation of machine learning models, model update is a fundamental process that requires careful consideration of its impact on downstream decis
arXiv:2608.09231v1 Announce Type: new Abstract: Diffusion caching is a lightweight strategy that accelerates Diffusion Transformers (DiTs) by reusing intermediate features across denoising steps, but
arXiv:2608.08191v1 Announce Type: new Abstract: Multi-organ ultrasound segmentation remains challenging when anatomically adjacent structures must be delineated jointly, as localized boundary errors c
arXiv:2608.08027v1 Announce Type: cross Abstract: Prompt injection is a critical security threat in large language model (LLM) applications, where attackers hijack model behavior by embedding maliciou
arXiv:2608.09617v1 Announce Type: new Abstract: Symbolic regression is the problem of finding an algebraic expression describing a stochastic dependence of a target variable on a set of inputs. Unlike
arXiv:2608.09888v1 Announce Type: cross Abstract: We introduce BDH-CQ, a reasoning model that combines in-context learning with recurrent latent reasoning. Inputs presented at inference time continuou
arXiv:2511.22130v2 Announce Type: replace Abstract: To navigate ever-shifting real-world environments, agents must grapple with incomplete knowledge and adapt their strategies through experience. Howe
arXiv:2508.16481v3 Announce Type: replace Abstract: Ensuring the safe use of agentic systems requires a thorough understanding of the range of malicious behaviors these systems may exhibit. In this pa
arXiv:2608.08202v1 Announce Type: new Abstract: Data-centric curation pipelines frequently rely on model confidence scores to flag and filter noisy or mislabeled training instances. Evaluating this fi
arXiv:2608.09366v1 Announce Type: new Abstract: Large-scale learning systems often face the challenge of balancing multiple, potentially competing objectives, such as fairness, accuracy, and latency.
arXiv:2507.03005v3 Announce Type: replace Abstract: Computational phylogenetics has become an established tool in historical linguistics, with many language families now analyzed using likelihood-base
arXiv:2608.09140v1 Announce Type: cross Abstract: Recent work on protecting privacy during user-LLM interactions often focuses on direct, explicit identifiers: the personally-identifiable information