Search: “concepts”

arXiv:2608.06490v1 Announce Type: new Abstract: We present InsertFuse, a unified framework for multi-category reference-guided image insertion. Its key idea is to decouple category-specific expertise

arXiv:2509.16462v2 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly used in high-stakes decision-making systems, where biased predictions can reinforce social and economi

arXiv:2608.06351v1 Announce Type: new Abstract: This paper addresses the limitations of Explainable Artificial Intelligence (XAI) with respect to insufficient evaluation. They are illustrated through

arXiv:2510.17709v2 Announce Type: replace-cross Abstract: Training Reinforcement Learning (RL) policies using simulation models before deployment in real-world environments is a common strategy when r

arXiv:2505.19840v3 Announce Type: replace-cross Abstract: Deep Neural Networks (DNNs) have achieved widespread success yet remain prone to adversarial attacks. Typically, such attacks either involve f

arXiv:2608.02876v1 Announce Type: new Abstract: Tool-using agents do not merely consume observations: their actions determine what arrives next. In agentic text-to-SQL, a broad query can spend context

arXiv:2604.06005v2 Announce Type: replace Abstract: Interpreting the information encoded in language model weights remains a fundamental challenge in mechanistic interpretability. In this work, we int

arXiv:2608.02845v1 Announce Type: new Abstract: Tabular model performance degrades when feature distributions change over time or the relationship between features and outcome variables change over ti

arXiv:2608.03991v1 Announce Type: new Abstract: Training-free open-vocabulary semantic segmentation (OVSS) partitions an image into semantically distinct regions based on arbitrary text descriptions,

arXiv:2412.20206v4 Announce Type: replace Abstract: Visual Grounding, also known as Referring Expression Comprehension and Phrase Grounding, aims to ground the specific region(s) within the image(s) b

arXiv:2602.05220v4 Announce Type: replace Abstract: Current audio foundation models typically rely on rigid, task-specific supervision (e.g., speech recognition), addressing isolated factors of audio

arXiv:2608.01968v1 Announce Type: new Abstract: Transformer models are most often understood through what they do: their benchmark performance, generation quality, or behavior on downstream tasks. Yet

arXiv:2608.00086v1 Announce Type: new Abstract: Brain tumor diagnosis is a time-sensitive process in which patients may wait weeks for a finalized pathology report. This problem motivates automated sy

arXiv:2608.00279v1 Announce Type: cross Abstract: Recent radiology-adapted vision-language models have achieved strong performance on standard report generation benchmarks, yet their robustness and ge

arXiv:2608.00059v1 Announce Type: new Abstract: The success of connectome mapping now shifts the challenge of understanding the nervous system to the interpretation of neural circuits. Here, we devise

arXiv:2608.01176v1 Announce Type: new Abstract: Political polarization has become a defining feature of online discourse, yet its long-term evolution remains poorly understood. We present a longitudin

arXiv:2607.29252v1 Announce Type: cross Abstract: Reliable evaluation of open-ended LLM outputs requires fine-grained rubrics, yet expert curation is costly and difficult to scale. Existing automated

arXiv:2607.29553v1 Announce Type: new Abstract: Organizations increasingly define operational metrics in structured, machine-readable formats to monitor systems, processes, and compliance. These metri

arXiv:2607.27130v1 Announce Type: new Abstract: Ontology matching (OM) has traditionally been formulated as either equivalence discovery or subsumption matching. The existing OM systems identify only

arXiv:2607.28129v1 Announce Type: new Abstract: Face-and-voice association learning is one of the most challenging tasks in deep learning. In this paper, we propose a simple but powerful cross-modal f

arXiv:2607.27671v1 Announce Type: new Abstract: The majority of the recently-developed models for automated essay scoring (AES) are evaluated solely on the ASAP corpus. However, ASAP is not without it

arXiv:2607.27690v1 Announce Type: new Abstract: We introduce LabEvolver, a training-free framework that equips safe and grounded wet-lab agents with episodic memory from execution experience. LabEvolv

arXiv:2607.27537v1 Announce Type: new Abstract: Predicting future structural MRI of a brain is challenging because longitudinal changes are often subtle and confined to specific anatomical regions, wh

arXiv:2607.18785v2 Announce Type: replace Abstract: As large language model agents gain access to increasingly large skill libraries, retrieving the right skill becomes critical to reliable capability

arXiv:2607.27292v1 Announce Type: new Abstract: The rise of powerful, accessible image-editing models such as FLUX.2 has brought high-fidelity editing within broad reach. Their capabilities now extend

arXiv:2607.26170v1 Announce Type: new Abstract: This study developed a hybrid computer vision method to quantify exposed skin from images for dermal exposure assessment. Using 170 indoor-painting imag

arXiv:2607.26715v1 Announce Type: new Abstract: Existing supervised and unsupervised shadow removal methods often suffer from limited generalization due to the insufficient diversity of available trai

arXiv:2607.26381v1 Announce Type: new Abstract: Wi-Fi-based human activity recognition has advanced substantially, but most existing methods assume a closed set of activities and require labeled Wi-Fi

arXiv:2607.26023v1 Announce Type: new Abstract: Graph foundation models (GFMs) have emerged as a promising paradigm for transferring knowledge across graph domains and tasks. Real-world graphs associa

arXiv:2607.24861v1 Announce Type: cross Abstract: Question answering (QA) over complex documents requires models to retrieve and integrate evidence distributed across distant document regions and moda

arXiv:2510.16077v2 Announce Type: replace-cross Abstract: Domain Incremental Learning (DIL) is a sub-branch of continual learning that aims to address the never-ending arrival of new domains without c

arXiv:2607.24516v1 Announce Type: cross Abstract: While data curation for Vision Language Models (VLMs) is increasingly active, public practice for constructing pretraining mixtures remains largely he

arXiv:2607.23338v1 Announce Type: new Abstract: Graph compression reduces the computational cost of graph learning, but its effect on signal propagation remains largely underexplored. Existing work ev

arXiv:2607.22808v1 Announce Type: cross Abstract: The rapid advancement of text-to-image (T2I) models has necessitated robust Synthetic Image Source Attribution (SIA) methodologies. A critical challen

arXiv:2607.23488v1 Announce Type: cross Abstract: Text-to-image diffusion models expose many inference-time sampling parameters, including prompts, negative prompts, classifier-free guidance scales, a

arXiv:2607.22700v1 Announce Type: new Abstract: Post-click conversion rate (PCVR) prediction is central to industrial recommendation, but remains challenged by the structural mismatch between sparse,

arXiv:2607.22931v1 Announce Type: cross Abstract: Analytic Continual Learning (ACL) offers a computationally efficient alternative to gradient-based approaches. Recent ACL methods are based on Recursi

arXiv:2607.21998v1 Announce Type: new Abstract: This paper introduces a new benchmark test, Medical-Checklist, for assessing medical multimodal models. The recent advancements in multimodal models hav

arXiv:2607.20556v1 Announce Type: new Abstract: In large-scale text analysis tasks, pre-trained language models are often used to embed text corpora for downstream analysis. However, such models may s

arXiv:2607.19811v1 Announce Type: new Abstract: The Segment Anything Model 2 (SAM2) has advanced temporal promptable segmentation, yet its deployment remains hindered by heavy memory cross-attention o

arXiv:2607.20058v1 Announce Type: new Abstract: Large language models can answer scientific questions, yet a correct output does not reveal whether the model represents or uses the governing physics.

arXiv:2607.19986v1 Announce Type: new Abstract: Stereo matching is a fundamental task in 3D reconstruction. Despite remarkable advances, the prevailing paradigms formulate stereo matching as a determi

arXiv:2607.19901v1 Announce Type: new Abstract: Deep learning frameworks like nnU-Net achieve state-of-theart brain lesion segmentation performance but remain difficult to deploy in clinical research

arXiv:2607.13123v1 Announce Type: cross Abstract: Cybersecurity is the practice of protecting systems, networks, and data from digital attacks. Cyberpsychology (CPSY) is defined as the use of psycholo

arXiv:2607.13660v1 Announce Type: new Abstract: Contrastive Language-Image Pretraining (CLIP) representations form a semantic embedding space governed by cosine similarity, reflecting an intrinsic hyp

arXiv:2607.12634v1 Announce Type: new Abstract: This paper proposes a theoretical framework for understanding intelligence as a process of atomic compression and compositional reuse. We argue that cog