Knowledge catalogue
arXiv:2608.26970v1 Announce Type: cross Abstract: Place names provide persistent records of how landscapes have been described and organised, but their quantitative reuse requires explicit separation
arXiv:2608.27175v1 Announce Type: new Abstract: Many Earth Observation applications need land-use/land-cover maps that are both precise and frequently updated, yet the strongest Earth Observation foun
arXiv:2608.26821v1 Announce Type: new Abstract: Vision-language-action (VLA) models leverage pretrained vision-language representations for robot control, yet simply adding historical frames does not
arXiv:2505.11326v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) have shown remarkable progress in offline tasks such as image captioning and video question answering. However,
arXiv:2608.27221v1 Announce Type: new Abstract: Robots that can change their morphologies and behaviors for different tasks and environments hold great promise for adaptable, multifunctional systems.
arXiv:2608.26978v1 Announce Type: cross Abstract: Landscapes are named, but whether names retain measurable environmental information beyond broad geographic structure is rarely tested. We analysed 3,
arXiv:2608.26902v1 Announce Type: new Abstract: Streaming autoregressive video models generate long videos chunk by chunk, using historical memory to maintain consistency. Existing methods typically e
arXiv:2608.26624v1 Announce Type: new Abstract: Open-vocabulary semantic segmentation (OVSS) aims to segment image regions corresponding to arbitrary text queries. Although the Segment Anything Model
arXiv:2608.26134v1 Announce Type: new Abstract: Energy forecasting aims to maximize accuracy to ensure energy efficiency by reducing energy waste, an objective that applies equally to on-device foreca
arXiv:2608.26116v1 Announce Type: new Abstract: Existing methods for exploring cellular automata and other complex systems mostly operate in open loop: they set initial conditions, execute a full simu
arXiv:2411.15129v3 Announce Type: replace-cross Abstract: What can we learn about language from studying how it is used by ChatGPT and other large language model (LLM)-based chatbots? In this paper, w
arXiv:2606.07867v2 Announce Type: replace Abstract: Are tool-calling LLM agents equally safe throughout a conversation? We discover they are not: agents are most vulnerable at the very start of a sess
arXiv:2608.26423v1 Announce Type: cross Abstract: This paper proposes a framework for constructing a classifier as a safeguard layer, and for developing a complementary diagnostic that identifies whic
arXiv:2608.26235v1 Announce Type: new Abstract: Accuracy-only benchmarking of reasoning-capable large language models misses a central deployment question: when do extended thinking tokens earn their
arXiv:2606.22179v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed as black-box classifiers in pipelines that automate confident decisions and route uncertain o
arXiv:2608.26602v1 Announce Type: cross Abstract: Large software repositories are often beyond model context limits. Training repository knowledge into models is costly and quickly stale, while local
arXiv:2601.13029v4 Announce Type: replace Abstract: While Vision-Language Models (VLMs) excel at 2D visual understanding, they remain constrained by 2D-centric paradigm that severely limits genuine 3D
arXiv:2608.26809v1 Announce Type: new Abstract: While generative AI has significantly advanced video editing, existing methods primarily focus on single-shot or short video clips. Editing long videos
arXiv:2608.27147v1 Announce Type: new Abstract: The development of frontier models is commonly perceived to be the exclusive remit of a small number of heavily funded players, creating an information,
arXiv:2608.26647v1 Announce Type: new Abstract: Full-image objectives in brain magnetic resonance imaging (MRI) super-resolution (SR) can underweight tissue-transition regions affected by the partial-
arXiv:2608.27382v1 Announce Type: cross Abstract: Generative AI is transforming how people access information, challenging traditional advertising mechanisms built around predefined slots. Towards gen
arXiv:2512.06694v2 Announce Type: replace Abstract: Topic discovery is a fundamental technique for text mining that identifies abstract topics within large document collections. A recent approach to t
arXiv:2507.22893v3 Announce Type: replace-cross Abstract: Contemporary human-AI interaction research overlooks how AI systems fundamentally reshape human cognition pre-consciously, a critical blind sp
arXiv:2601.22300v4 Announce Type: replace-cross Abstract: We propose a deep photonic neuromorphic network (PNN) architecture based on phase-change material (PCM) synapses and local optical feedback fo
arXiv:2608.26451v1 Announce Type: new Abstract: Bike-sharing systems are an important component of low-carbon urban mobility, but continued expansion creates challenges in both cold-start prediction a
arXiv:2608.26417v1 Announce Type: cross Abstract: Metasurface design increasingly requires fast models that can operate across structurally distinct device families, rather than retraining a separate
arXiv:2608.26706v1 Announce Type: new Abstract: Expert-level financial question answering requires both grounded verification to catch numeric hallucinations and audit trails for regulatory compliance
arXiv:2608.26148v1 Announce Type: new Abstract: Depression affects millions worldwide, yet diagnosis relies on subjective self-reports that may miss authentic behavior. This paper presents an approach
arXiv:2608.27124v1 Announce Type: new Abstract: Reliable prediction of time-varying channel state information (CSI) is essential for efficient wireless communication. Each CSI frame is a matrix-valued
arXiv:2608.26219v1 Announce Type: cross Abstract: Reconstructing continuous physical fields from sparse measurements is central to scientific monitoring, inverse modeling, and digital-twin constructio
arXiv:2608.27182v1 Announce Type: new Abstract: LLM agents are increasingly applied to anomaly detection and root-cause analysis in time-series observations collected from real-world systems; however,
arXiv:2608.26125v1 Announce Type: cross Abstract: Online hate against Muslim communities often appears in culturally coded, multilingual forms that evade conventional AI moderation. Such systems, thou
arXiv:2603.25489v2 Announce Type: replace Abstract: Recent strategies for low-resource machine translation rely on LLMs to generate synthetic data based on text in higher-resource languages. We revisi
arXiv:2608.27127v1 Announce Type: new Abstract: Internet memes are a pervasive form of multimodal online communication; however, such communication often involves users from diverse linguistic and cul
arXiv:2608.26578v1 Announce Type: cross Abstract: This work introduces Configured Failure Trapping, a novel backdoor attack task against Vision-Language-Action (VLA) models, which aims to activate att
arXiv:2608.26112v1 Announce Type: new Abstract: Speculative decoding accelerates large language model inference through a draft-then-verify paradigm. Building on this, tree-structured methods improve
arXiv:2608.27448v1 Announce Type: new Abstract: Recent prominent post-training methods, such as Reinforcement Learning (RL) and On-Policy Self-Distillation (OPSD), have driven rapid progress in mathem
arXiv:2608.26184v1 Announce Type: new Abstract: AI programming tutors provide scalable support, yet lack the behavioral context human tutors rely on to adapt support to learners' needs. We present Tut
arXiv:2608.27128v1 Announce Type: new Abstract: Long-context inference is bottlenecked by the memory footprint of the key-value (KV) cache, especially for small models under tight resource budgets. Ex
arXiv:2511.22273v2 Announce Type: replace-cross Abstract: Selecting the best alternative from a finite set is the central objective of ranking and selection (R&S) and best arm identification (BAI). Tr
arXiv:2602.22712v2 Announce Type: replace Abstract: Small target detection in UAV imagery faces significant challenges such as scale variations, dense distribution, and the dominance of small targets.
arXiv:2608.27152v1 Announce Type: new Abstract: Applications in fields such as movement ecology, Internet of Things or robotics share the need for systems that localize devices that are too small and
arXiv:2608.27351v1 Announce Type: new Abstract: Evolution Strategies (ES) have recently emerged as a memory-efficient post-training paradigm for LLM reasoning. However, the optimization behavior of ES
arXiv:2608.27240v1 Announce Type: new Abstract: Prenatal ultrasound examination is crucial for assessing fetal limb development and detecting congenital anomalies. However, existing artificial intelli
arXiv:2608.27070v1 Announce Type: cross Abstract: To mitigate catastrophic forgetting in downstream continual learning (CL) for large language models (LLMs), existing methods typically constrain param
arXiv:2608.26722v1 Announce Type: new Abstract: Text-guided drone geo-localization aims to identify a target region in a large-scale image gallery from a natural-language description. Existing methods
arXiv:2608.27372v1 Announce Type: cross Abstract: We establish a sharp phase transition for fitting random vectors by an ellipsoid. The random vectors have independent subgaussian coordinates with mea
arXiv:2608.27190v1 Announce Type: new Abstract: Accurate 3D segmentation of cone-beam CT (CBCT) is critical for interventional and radiation therapy applications, yet it remains limited by two compoun
arXiv:2608.27456v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) can interpret a street view, but urban agency depends on whether such local evidence remains useful after the a
arXiv:2608.26165v1 Announce Type: cross Abstract: Automated creativity assessment has been a long standing challenge, with traditional methods often being resource intensive or lacking practical accur
arXiv:2510.25548v3 Announce Type: replace Abstract: In task and motion planning, high-level task planning is done over an abstraction of the world to enable efficient search in long-horizon robotics p
arXiv:2608.26146v1 Announce Type: new Abstract: We present Vagdhenu, a vrutta (meter) aware shloka-to-chant system for Sanskrit: a text-to-speech system that maps a metrical verse to its chanted paray
arXiv:2608.27311v1 Announce Type: new Abstract: Agent harnesses shape how language-model agents use instructions, tools, and runtime components, but adapting these harnesses requires costly verificati
arXiv:2608.26155v1 Announce Type: cross Abstract: Multimodal large language models have advanced rapidly, yet most remain English-centric, as scaling multilingual multimodal instruction tuning is limi
arXiv:2608.26495v1 Announce Type: new Abstract: Multimodal queries can require different types of reasoning. Some can be answered via perceptual reasoning, extracting information directly from the vis
arXiv:2608.27065v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) has recently emerged as an effective post-training paradigm that improves policy optimization through dense token-lev
arXiv:2608.26382v1 Announce Type: new Abstract: Pathology vision-language models are advancing rapidly, yet existing benchmarks remain focused on human tissue, particularly oncology, leaving non-human
arXiv:2608.26998v1 Announce Type: new Abstract: Intracranial electroencephalography (iEEG) provides temporally precise and spatially specific access to neural activity from focal and deep brain region
arXiv:2608.27199v1 Announce Type: new Abstract: Vision generative artificial intelligence (AI) has emerged as one of the most rapidly advancing areas of deep learning. The explosion of multimodal mode
arXiv:2608.26580v1 Announce Type: cross Abstract: Diffusion multimodal large language models (dMLLMs) have recently emerged as a new decoding paradigm for multimodal generation. Starting from a fully