Knowledge catalogue
arXiv:2604.09644v1 Announce Type: cross Abstract: Corporate AI-washing-the strategic misrepresentation of AI capabilities via exaggerated or fabricated cross-channel disclosures-has emerged as a syste
arXiv:2604.11806v1 Announce Type: new Abstract: To identify safety violations, auditors often search over large sets of agent traces. This search is difficult because failures are often rare, complex,
arXiv:2603.18280v2 Announce Type: replace-cross Abstract: Current alignment evaluation mostly measures whether models encode dangerous concepts and whether they refuse harmful requests. Both miss the
arXiv:2604.11171v1 Announce Type: new Abstract: Computer-aided detection (CADe) of early neoplasia in Barrett's esophagus is a low-prevalence surveillance problem in which clinically relevant findings
arXiv:2604.09877v1 Announce Type: cross Abstract: In the intersection of computer vision and robotic perception, 4D reconstruction of dynamic scenes serve as the critical bridge connecting low-level g
arXiv:2601.01822v2 Announce Type: replace-cross Abstract: Since floorplan data is readily available, long-term persistent, and robust to changes in visual appearance, visual Floorplan Localization (FL
arXiv:2511.03015v2 Announce Type: replace Abstract: Generating graph-structured data is crucial in applications such as molecular generation, knowledge graphs, and network analysis. However, their dis
arXiv:2601.14346v2 Announce Type: replace-cross Abstract: Accurate prediction of drug response in precision medicine requires models that capture how specific chemical substructures interact with cell
arXiv:2604.11118v1 Announce Type: new Abstract: K-means clustering is a workhorse of unsupervised learning, but it is notoriously brittle to outliers, distribution shifts, and limited sample sizes. Vi
arXiv:2604.11170v1 Announce Type: new Abstract: Semantic segmentation requires dense pixel-level annotations, which are costly and time-consuming to acquire. To address this, we present SeSAM, a frame
arXiv:2604.10077v1 Announce Type: new Abstract: In Document Understanding, the challenge of reconstructing damaged, occluded, or incomplete text remains a critical yet unexplored problem. Subsequent d
arXiv:2512.00375v2 Announce Type: replace Abstract: Existing motion planning methods often struggle with rapid-motion obstacles due to an insufficient understanding of environmental changes. To addres
arXiv:2603.18806v2 Announce Type: replace Abstract: Diffusion Large Language Models (dLLMs) introduce a new paradigm for language generation, which in turn presents new challenges for aligning them wi
arXiv:2604.11094v1 Announce Type: cross Abstract: Contemporary microservice systems continue to grow in scale and complexity, leading to increasingly frequent and costly failures. While recent LLM-bas
arXiv:2604.11512v1 Announce Type: cross Abstract: The growing demand for deploying Small Language Models (SLMs) on edge devices, including laptops, smartphones, and embedded platforms, has exposed fun
arXiv:2603.05463v2 Announce Type: replace Abstract: Single-object tracking (SOT) on edge devices is a critical computer vision task, requiring accurate and continuous target localization across video
arXiv:2510.05837v2 Announce Type: replace Abstract: Balancing exploration and exploitation remains a central challenge in reinforcement learning with verifiable rewards (RLVR) for large language model
arXiv:2604.10660v1 Announce Type: cross Abstract: Recent research has devoted considerable effort to verifying the intermediate reasoning steps of chain-of-thought (CoT) trajectories using process rew
arXiv:2604.10662v1 Announce Type: new Abstract: Large-scale Internet of Things (IoT) networks enable intelligent services such as smart cities and autonomous driving, but often face resource constrain
arXiv:2601.04392v2 Announce Type: replace-cross Abstract: This paper introduces a fuzzy reinforcement learning framework, Enhanced-FQL(lambda), that integrates novel Fuzzified Eligibility Traces (FET)
arXiv:2604.10437v1 Announce Type: new Abstract: Vision--language models (VLMs) for radiology report generation (RRG) can produce long-form chest CT reports from volumetric scans and show strong potent
arXiv:2601.02956v3 Announce Type: replace Abstract: Multilingual Retrieval-Augmented Generation (mRAG) systems often exhibit a perceived preference for high-resource languages, particularly English, r
arXiv:2604.09698v1 Announce Type: cross Abstract: The growing ubiquity of Extended Reality (XR) is driving Conversational Recommendation Systems (CRS) toward visually immersive experiences. We formali
arXiv:2604.10535v1 Announce Type: cross Abstract: Incorporating large language models (LLMs) in medical question answering demands more than high average accuracy: a model that returns substantively d
arXiv:2604.10455v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have enabled promising progress in diagnosis prediction from electronic health records (EHRs). However,
arXiv:2604.10894v1 Announce Type: new Abstract: Referring Camouflaged Object Detection (Ref-COD) focuses on segmenting specific camouflaged targets in a query image using category-aligned references.
arXiv:2604.09568v1 Announce Type: cross Abstract: High-fidelity diagram creation requires the complex orchestration of semantic topology, visual styling, and spatial layout, posing a significant chall
arXiv:2604.09741v1 Announce Type: cross Abstract: For large language models deployed through black-box APIs, recurring inference costs often exceed one-time training costs. This motivates composed age
arXiv:2510.14543v4 Announce Type: replace Abstract: Aligning features from different modalities, is one of the most fundamental challenges for cross-modal tasks. Although pre-trained vision-language m
arXiv:2604.10189v1 Announce Type: new Abstract: Large Language Models (LLMs) can generate factually inaccurate content even if they have corresponding knowledge, which critically undermines their reli
arXiv:2604.10297v1 Announce Type: cross Abstract: Composed Image Retrieval (CIR) retrieves target images using a reference image paired with modification text. Despite rapid advances, all existing met
arXiv:2604.10275v1 Announce Type: new Abstract: Real-time image denoising is essential for modern mobile photography but remains challenging due to the strict latency and power constraints of edge dev
arXiv:2508.17431v3 Announce Type: replace-cross Abstract: Person re-identification (re-ID) is a fundamental task in intelligent surveillance and public safety. Federated learning (FL) provides a priva
arXiv:2604.10678v1 Announce Type: new Abstract: Social bot detection is critical to the stability and security of online social platforms. However, current state-of-the-art bot detection models are la
arXiv:2604.10391v1 Announce Type: cross Abstract: Vision foundation models (VFMs) and Bird's Eye View (BEV) representation have advanced visual perception substantially, yet their internal spatial rep
arXiv:2604.11083v1 Announce Type: cross Abstract: Text-to-motion generation is driven by learning motion representations for semantic alignment with language. Existing methods rely on either continuou
arXiv:2604.09989v1 Announce Type: cross Abstract: Recently, synthetic palmprints have been increasingly used as substitutes for real data to train recognition models. To be effective, such synthetic d
arXiv:2603.22911v2 Announce Type: replace-cross Abstract: Due to the great saving of computation and memory overhead, token compression has become a research hot-spot for MLLMs and achieved remarkable
arXiv:2604.10512v1 Announce Type: new Abstract: The development of generalizable Novel View Synthesis (NVS) models is critically limited by the scarcity of large-scale training data featuring diverse
arXiv:2604.10332v1 Announce Type: new Abstract: We present the progress of the GPT family from GPT-3 through GPT-3.5, GPT-4, GPT-4 Turbo, GPT-4o, GPT-4.1, and the GPT-5 family. Our work is comparative
arXiv:2603.23964v2 Announce Type: replace Abstract: The remarkable progress of reinforcement learning (RL) is intrinsically tied to the environments used to train and evaluate artificial agents. Movin
arXiv:2604.11041v1 Announce Type: new Abstract: Semiconductor supply chains face unprecedented resilience challenges amidst global geopolitical turbulence. Conventional Large Language Model (LLM) plan
arXiv:2604.11104v1 Announce Type: new Abstract: This paper presents an empirical study of a multi-model zero-shot pipeline for knowledge graph construction and exploitation, executed entirely through
arXiv:2509.20624v5 Announce Type: replace-cross Abstract: Autoregressive language models (ARMs) deliver strong likelihoods, but are inherently serial: they generate one token per forward pass, which l
arXiv:2601.08209v3 Announce Type: replace Abstract: In domains such as materials science, biomedicine, and finance, high-stakes deployment of large language models (LLMs) requires injecting private, d
arXiv:2604.11786v1 Announce Type: new Abstract: Modeling open-play soccer tactics is a formidable challenge due to the stochastic, multi-agent nature of the game. Existing computational approaches typ
arXiv:2602.09932v2 Announce Type: replace Abstract: Building height (BH) and footprint (BF) are fundamental urban morphological parameters required by climate modelling, disaster-risk assessment, and
arXiv:2604.11344v1 Announce Type: cross Abstract: Embedding-as-a-Service (EaaS) has become an important semantic infrastructure for natural language and multimedia applications, but it is highly vulne
arXiv:2604.10094v1 Announce Type: new Abstract: Anthropogenic methane (CH4) point sources drive near-term climate forcing, safety hazards, and system inefficiencies. Space-based imaging spectroscopy i
arXiv:2604.11404v1 Announce Type: cross Abstract: We present GlobalCY, a JAX-based framework for globally defined and symmetry-aware neural Kahler-potential models on projective hypersurface Calabi--Y
arXiv:2505.17022v2 Announce Type: replace-cross Abstract: Visual generation models have made remarkable progress in creating realistic images from text prompts, yet struggle with complex prompts that
arXiv:2604.10658v1 Announce Type: new Abstract: Institutional decisions -- regulatory compliance, clinical triage, prior authorization appeal -- require a different AI architecture than general-purpos
arXiv:2510.22329v2 Announce Type: replace Abstract: The Capacitated Vehicle Routing Problem with Time Windows (CVRPTW) is a fundamental NP-hard optimization problem in logistics. Solving large-scale i
arXiv:2604.10073v1 Announce Type: cross Abstract: Long-horizon Flexible Job-Shop Scheduling~(FJSP) presents a formidable combinatorial challenge due to complex, interdependent decisions spanning exten
arXiv:2604.11751v1 Announce Type: cross Abstract: In Model Predictive Control (MPC), world models predict the future outcomes of various action proposals, which are then scored to guide the selection
arXiv:2604.11218v1 Announce Type: new Abstract: Superpixels offer a compact image representation by grouping pixels into coherent regions. Recent methods have reached a plateau in terms of segmentatio
arXiv:2604.10665v1 Announce Type: new Abstract: HeceTokenizer is a syllable-based tokenizer for Turkish that exploits the deterministic six-pattern phonological structure of the language to construct
arXiv:2604.10560v1 Announce Type: new Abstract: Profiled Sparse Networks (PSN) replace uniform connectivity with deterministic, heterogeneous fan-in profiles defined by continuous, nonlinear functions
arXiv:2604.09679v1 Announce Type: cross Abstract: Multi-Agent Debate (MAD) is a collaborative framework in which multiple agents iteratively refine solutions through the generation of reasoning and al
arXiv:2604.10836v1 Announce Type: new Abstract: Generating realistic 3D hand-object interactions (HOI) is a fundamental challenge in computer vision and robotics, requiring both temporal coherence and