Search: “concepts”

arXiv:2605.26171v1 Announce Type: new Abstract: Many practical anomalies are not merely rare inputs, but violations of semantic constraints: objects co-occur in structured ways, actions imply precondi

arXiv:2605.25419v1 Announce Type: new Abstract: Effective learning support requires understanding not only what learners know but also how accurately they perceive their own understanding. This metaco

arXiv:2605.23933v1 Announce Type: cross Abstract: Educational Question Generation (EQG) aims to synthesize customized exercise questions that enhance student learning. An effective EQG system should i

arXiv:2605.24910v1 Announce Type: new Abstract: Financial Numerical Entity (FNE) understanding aims to recover the meaning of numerical mentions in financial reports. Existing studies primarily focus

arXiv:2605.23045v1 Announce Type: cross Abstract: Video representation learning has seen tremendous progress in recent years. This has been driven by many factors, including the scale of training and

arXiv:2605.22853v1 Announce Type: cross Abstract: Many modern datasets are large and carry complex structural relationships. Graph-based methods have traditionally been used to represent networked dat

arXiv:2605.20693v1 Announce Type: new Abstract: Interpretable text representations should expose coordinates that are not only predictive, but also meaningful enough for independent auditors to apply.

arXiv:2605.20537v1 Announce Type: new Abstract: Biomedical named entity recognition (NER) and entity linking (EL) strongly depend on annotated corpora, but the utility of these resources for benchmark

arXiv:2605.19735v1 Announce Type: cross Abstract: Graph-structured retrieval-augmented generation (RAG) systems can improve answer quality on multi-hop questions, but many current systems rely on larg

arXiv:2605.19111v1 Announce Type: cross Abstract: Existing text-to-image (T2I) evaluation metrics mainly assess whether generated images align with information explicitly stated in the prompt, but oft

arXiv:2506.18852v2 Announce Type: replace-cross Abstract: Mechanistic interpretability (MI) aims to explain how neural networks work by uncovering their underlying mechanisms. As the field grows in in

arXiv:2605.20158v1 Announce Type: cross Abstract: Large Vision Language Models (LVLMs) show promise in medical applications, but their inability to faithfully ground responses in visual evidence raise

arXiv:2510.08886v3 Announce Type: replace Abstract: Going beyond simple text processing, financial auditing requires detecting semantic, structural, and numerical inconsistencies across large-scale di

arXiv:2605.14403v1 Announce Type: new Abstract: Dermatological diagnosis requires integrating fine-grained visual perception with expert clinical knowledge. Although Multimodal Large Language Models (

arXiv:2605.13930v1 Announce Type: new Abstract: EEG foundation models achieve state-of-the-art clinical performance, yet the internal computations driving their predictions remain opaque: a barrier to

arXiv:2605.14940v1 Announce Type: cross Abstract: Semantic communication systems for goal-oriented transmission must protect task-relevant information not only through source compression but also via

arXiv:2605.13438v1 Announce Type: new Abstract: Existing agent memory remains predominantly reactive and retrieval-based, lacking the capacity to autonomously organize experience into persistent cogni

arXiv:2605.13773v1 Announce Type: cross Abstract: Large Language Models (LLMs) are being employed widely to automate tasks across the software development life-cycle. It is, however, unclear whether t

arXiv:2605.12509v1 Announce Type: cross Abstract: Many real-world phenomena are naturally modeled by graphs and networks. However, classical graph models are often limited to pairwise interactions and

arXiv:2605.11959v1 Announce Type: cross Abstract: Multimodal video summarization requires visual features that align semantically with language generation. Traditional approaches rely on CNN features

arXiv:2602.02280v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) face severe safety risks from jailbreak attacks, yet current safety testing largely relies on static datasets and

arXiv:2605.08188v1 Announce Type: cross Abstract: Human attention is the gateway to conscious perception, memory and decision-making. However, its role in modern transformer models remains largely une

arXiv:2605.07973v1 Announce Type: new Abstract: Text-to-image diffusion models can generate visually stunning images, yet, controlling what appears and how it appears, remains surprisingly difficult,

arXiv:2605.06610v2 Announce Type: replace-cross Abstract: Sparse Autoencoders (SAEs) have become an important tool in mechanistic interpretability, helping to analyze internal representations in both

arXiv:2602.00924v2 Announce Type: replace Abstract: Sparse auto-encoders (SAEs) have re-emerged as a prominent method for mechanistic interpretability, yet they face two significant challenges: the no

arXiv:2605.07922v1 Announce Type: new Abstract: Learning hierarchical features in Sparse Autoencoders (SAEs) is essential for capturing the structured nature of real-world data and mitigating issues l

arXiv:2602.07608v2 Announce Type: replace Abstract: Metastatic Progression remains the leading cause of cancer-related mortality, yet predicting whether a primary tumor will metastasize and where it w

arXiv:2603.22844v4 Announce Type: replace Abstract: Surgical smoke severely degrades intraoperative video quality, obscuring anatomical structures and limiting surgical perception. Existing learning-b

arXiv:2605.01442v1 Announce Type: new Abstract: There has been a growing interest in explaining entailments over description logic (DL) knowledge bases. The existing explanation formalisms focus on ju

arXiv:2605.03936v1 Announce Type: new Abstract: Conceptual analysis -- proposing definitions and refining them through counterexamples -- is central to philosophical methodology. We study whether lang

arXiv:2605.02580v1 Announce Type: new Abstract: Recognizing unknown objects is crucial for safety-critical applications such as autonomous driving and robotics. Open-Set Panoptic Segmentation (OPS) ai

arXiv:2605.02052v1 Announce Type: new Abstract: This discussion paper reflects on how quantitative approaches to historical linguistics interact with dataset properties. Drawing on two worked examples

arXiv:2605.00762v1 Announce Type: new Abstract: We propose a new framework for meritocratic fairness in budgeted combinatorial multi-armed bandits with full-bandit feedback (BCMAB-FBF). Unlike semi-ba

arXiv:2605.00297v1 Announce Type: cross Abstract: Traditionally, machine learning methods for PE malware detection have relied on static features like byte histograms, string information, and PE heade

arXiv:2509.05291v2 Announce Type: replace-cross Abstract: Large language models (LLMs) learn non-trivial abstractions during pretraining, such as detecting irregular plural noun subjects. However, bec

arXiv:2604.26976v1 Announce Type: cross Abstract: We study the problem of fitting a description logic (DL) ontology to a given set of positive and negative examples that take the form of an ABox and a

arXiv:2501.19143v2 Announce Type: replace Abstract: As the cornerstone of artificial intelligence, machine perception confronts a fundamental threat posed by adversarial illusions. These adversarial a

arXiv:2604.26964v1 Announce Type: cross Abstract: The growing sophistication of contemporary cyber threats necessitates a more effective and adaptive approach to cybersecurity training. Intuitive and

arXiv:2602.01433v2 Announce Type: replace Abstract: Multivariate time series in domains such as finance, climate science, and healthcare often exhibit long-term trends, seasonal patterns, and short-te

arXiv:2604.24877v1 Announce Type: new Abstract: Controlling illumination in images is essential for photography and visual content creation. While closed-source models have demonstrated impressive ill

arXiv:2502.02452v4 Announce Type: replace Abstract: Personalization of Large Vision-Language Models (LVLMs) involves customizing models to recognize specific users or object instances and to generate

arXiv:2604.00485v2 Announce Type: replace Abstract: Dimension reduction (DR) is inherently non-unique: multiple embeddings can preserve the structure of high-dimensional data equally well while differ

arXiv:2604.22794v1 Announce Type: cross Abstract: Reinforcement learning (RL) offers a promising approach for adaptive wind farm flow control, yet its practical deployment is hindered by slow training

arXiv:2404.10141v2 Announce Type: replace-cross Abstract: Text-to-image (T2I) models have achieved remarkable progress in high-quality image synthesis, yet most benchmarks rely on simple, self-contain

arXiv:2510.18346v2 Announce Type: replace Abstract: Audio-Visual Question Answering (AVQA) requires models to effectively utilize both visual and auditory modalities to answer complex and diverse ques

arXiv:2604.22851v1 Announce Type: cross Abstract: While Vision-Language Models (VLMs) have advanced highlevel reasoning in autonomous driving, their ability to ground this reasoning in the underlying