Knowledge catalogue
arXiv:2606.25608v1 Announce Type: cross Abstract: This paper presents a novel approach to perform semi-automated BSI IT-Grundschutz certification using a MultiLarge Language Model system (MLS) with Hy
Anthropic accused Alibaba of a coordinated, industrial-scale operation to illicitly scrape data from Claude , using nearly 25,000 fraudulent accounts to generate more than 28.8 million exchanges betwe
arXiv:2606.25207v1 Announce Type: cross Abstract: Hyperparameter Optimization (HPO) is essential for maximizing machine learning model performance, and its core challenge is sample efficiency: finding
arXiv:2606.25253v1 Announce Type: new Abstract: Highlights provide a concise summary of the main contributions of an academic paper and help readers quickly understand its focus. However, many journal
arXiv:2606.25128v1 Announce Type: cross Abstract: Volume and quality of datasets are crucial for deep learning model training, yet they are often constrained by availability and data acquisition costs
arXiv:2606.25375v1 Announce Type: new Abstract: With the rapid adoption of generative AI, synthetic medical images pose growing risks, including diagnostic deception and insurance fraud. Although prio
arXiv:2606.25524v1 Announce Type: cross Abstract: Large language models (LLMs) reach high accuracy in mathematical reasoning, but individual traces on the same problem diverge; some arrive at the corr
arXiv:2603.25645v2 Announce Type: replace-cross Abstract: Early screening via colonoscopy is critical for colon cancer prevention, yet developing robust AI systems for this domain is hindered by the l
arXiv:2606.25769v1 Announce Type: new Abstract: In many prediction problems in medical applications, target labels exhibit an inherent ordinal structure, where class ordering reflects clinically meani
arXiv:2606.25454v1 Announce Type: new Abstract: In this study, the learning-based inertial odometry problem is investigated using raw IMU measurements obtained from the EuRoC MAV benchmark dataset. In
arXiv:2606.25546v1 Announce Type: new Abstract: Vision-language pre-training (VLP) holds great promise for general-purpose medical AI by leveraging radiology reports as rich textual supervision, yet e
arXiv:2606.24957v1 Announce Type: new Abstract: While speculative decoding improves inference throughput for multi-batch long-context Large Language Models (LLMs), its efficiency is often limited by a
arXiv:2606.25197v1 Announce Type: new Abstract: Learning effective policies for adaptive data acquisition remains challenging: posterior-based methods rely on surrogate models and posterior approximat
arXiv:2606.25732v1 Announce Type: new Abstract: Real-world single image dehazing is highly ill-posed due to spatially and spectrally varying scattering, while practical deployment demands lightweight
arXiv:2606.25177v1 Announce Type: new Abstract: Cognitive workload monitoring is important for adaptive rehabilitation and assistive interfaces, where task difficulty, pacing, and feedback should be a
LucidLink Corp., the maker of a cloud network-attached storage system based on object storage technology, today extended its distributed file system technology into agentic artificial intelligence wit
arXiv:2606.26037v1 Announce Type: cross Abstract: Federated learning has emerged as the foremost approach for decentralized model training with privacy preservation. The global class imbalance and cro
arXiv:2606.25000v1 Announce Type: new Abstract: To evaluate whether vision-language models can reason about geological histories, it is necessary to construct observations for which the underlying pro
arXiv:2606.25811v1 Announce Type: cross Abstract: Commodity futures can be represented hierarchically, with underlying assets at the upper level and individual futures contracts at the lower level. En
arXiv:2510.23842v2 Announce Type: replace Abstract: Most state-of-the-art sign language models are trained on interpreter or isolated vocabulary data, which overlooks the variability that characterize
arXiv:2606.25487v1 Announce Type: new Abstract: Almost every paper on LLM jailbreaks and prompt injection reports an attack-success rate (ASR), and that number is assigned not by people but by an auto
arXiv:2411.15490v2 Announce Type: replace Abstract: Acute ischemic stroke (AIS) requires time-critical decision-making, where inaccurate interpretation of neuroimaging findings can lead to irreversibl
arXiv:2606.25907v1 Announce Type: new Abstract: Diffusion models have shown promise in drag-style editing. Previous works mainly focus on point-based drag, which is inherently ambiguous. This paper fo
arXiv:2606.25169v1 Announce Type: cross Abstract: Sampling from an unnormalized target by reversing an Ornstein--Uhlenbeck diffusion requires the score of each noise-perturbed marginal. Tweedie's iden
arXiv:2512.10187v3 Announce Type: replace Abstract: LLMs excel at reasoning, but validating their steps remains challenging. Formal verification offers a solution through mechanically checkable proofs
arXiv:2606.25978v1 Announce Type: cross Abstract: Multi-agent goal recognition asks an observer to jointly infer which agents act together and what each team is trying to achieve, so the hypothesis sp
arXiv:2606.25008v1 Announce Type: cross Abstract: Neural scaling laws describe how pre-training loss decays as power laws with training time, model size, and compute. This position paper argues that t
arXiv:2606.26091v1 Announce Type: new Abstract: On-policy self-distillation achieves strong pass@1 accuracy by using a single model as both teacher and student, with the teacher conditioned on a corre
arXiv:2606.25462v1 Announce Type: new Abstract: Abstractive text summarization is the technique of generating a short and concise summary comprising the salient ideas of a source text without making a
Our new AI policy is that the White House decides ad hoc, for whatever reasons it likes, who does and does not get access to frontier intelligence. This seems rather maximally terrible. The US Governm
arXiv:2606.25784v1 Announce Type: new Abstract: Data augmentation is known to improve generalization of deep visual models. Recent methods favor mixup strategies that generate interpolated samples to
arXiv:2510.07094v2 Announce Type: replace Abstract: This work focuses on sampling strategies of configuration variations for generating robust universal locomotion policies for quadrupedal robots. We
arXiv:2606.25533v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) has emerged as a dominant paradigm for enhancing large language models with external knowledge. By coupling retri
arXiv:2606.18837v2 Announce Type: replace-cross Abstract: Large Language Model (LLM)-based automatic Multi-Agent Systems (MAS) generation has become a crucial frontier for tackling complex tasks. Howe
arXiv:2606.25224v1 Announce Type: new Abstract: Learning-based automated coaching systems for complex motor tasks such as high-performance driving remain limited in the ability to be adaptive by their
arXiv:2606.25754v1 Announce Type: new Abstract: Polishing is a critical finishing process in high-end manufacturing fields such as aerospace, where surface quality directly affects the service perform
arXiv:2606.25478v1 Announce Type: new Abstract: Adapting CLIP for open-vocabulary video recognition necessitates a delicate balance between newly acquired video knowledge and the pretrained generaliza
arXiv:2606.25975v1 Announce Type: cross Abstract: Common first-order optimizers, such as Adam, implicitly treat each parameter block as an unstructured vector, which disregards the multilinear weight
arXiv:2606.19626v2 Announce Type: replace-cross Abstract: AI pipelines that reason quantitatively over technical text depend on input where physical quantities, numbers, units, and symbolic expression
arXiv:2606.24955v1 Announce Type: new Abstract: Power forecasting models deployed in real-world energy markets must operate under nonstationary conditions, where data distributions continually evolve
arXiv:2606.25600v1 Announce Type: cross Abstract: In the first part of this work, we construct the first type of two-dimensional (2D) hyperbolic neural quantum state (NQS) in the form of the Lorentz 2
arXiv:2602.06020v3 Announce Type: replace Abstract: How do protein structure prediction models fold proteins? We investigate this question through causal interventions on the folding trunks of ESMFold
arXiv:2606.25880v1 Announce Type: new Abstract: Embodied Visual Tracking (EVT) requires an agent to continuously follow a specified target while actively moving through dynamic environments. However,
arXiv:2606.25987v1 Announce Type: new Abstract: Large language models (LLMs) attain remarkable surface fluency on code, yet they neither formally guarantee the syntactic validity of their output nor l
arXiv:2606.24967v1 Announce Type: new Abstract: In ill-posed inverse problems, the recovered solution depends as much on the prior as on the data, yet much of the engineering knowledge that could serv
arXiv:2606.26027v1 Announce Type: new Abstract: Tool use enables large language models (LLMs) to perform complex tasks, and recent agentic reinforcement learning (RL) methods show promise for enhancin
arXiv:2606.23719v1 Announce Type: cross Abstract: Laser powder bed fusion (LPBF) is a promising additive manufacturing technique that suffers from quality assurance concerns. Predicting melt pools fro
arXiv:2606.24172v1 Announce Type: cross Abstract: More than a billion people communicate in Indic languages, yet the natural language processing infrastructure serving them remains fragmented and unde
arXiv:2606.24140v1 Announce Type: new Abstract: Discrete flow matching (DFM) provides a principled framework for generative modeling on discrete state spaces via continuous-time Markov chain dynamics.
arXiv:2606.12824v2 Announce Type: replace-cross Abstract: AI governance for medical imaging is formalizing: the 2026 ACR-SIIM Practice Parameter recommends local acceptance testing and ongoing drift m
arXiv:2606.24484v1 Announce Type: new Abstract: WordArt (artistic text) features highly customized fonts, textures, and layouts, making WordArt-oriented scene TExt Recognition (WATER) substantially mo
arXiv:2606.24286v1 Announce Type: new Abstract: Multimodal Large Language Models have achieved remarkable progress in short-form audio-video understanding, yet long-form audio-video comprehension rema
arXiv:2509.03647v2 Announce Type: replace-cross Abstract: Large language models (LLMs) increasingly serve as automated evaluators, yet they suffer from 'self-preference bias': a tendency to favor thei
arXiv:2606.24767v1 Announce Type: new Abstract: Indoor visual relocalization plays a critical role in emerging spatial and embodied AI applications. However, prior research was predominantly devoted t
arXiv:2606.23725v1 Announce Type: cross Abstract: Machine-learning screens for battery materials are trained and judged almost entirely against computed reference voltages, and those references carry
arXiv:2409.11363v2 Announce Type: replace-cross Abstract: AI agents have the potential to aid users on a variety of consequential tasks, including conducting scientific research. To spur the developme
arXiv:2606.24786v1 Announce Type: new Abstract: Counting individual trees is a fundamental task for environmental monitoring, yet remains largely unexplored with satellite imagery. At these resolution
arXiv:2606.18478v2 Announce Type: replace Abstract: Recent progress has shown promise in distilling multi-step video diffusion models into efficient few-step students. Among them, Distribution Matchin
Decisions about how to use AI in your organization are increasingly organizational design and strategy decisions, not IT choices: How do you integrate agents into your firm? What intelligence will you
arXiv:2606.24153v1 Announce Type: new Abstract: While Deep Unfolding Networks (DUNs) dominate video Snapshot Compressive Imaging (SCI), they remain constrained by a uniform design philosophy. Existing