Knowledge catalogue
arXiv:2608.04307v1 Announce Type: cross Abstract: Text summarization is deceptively difficult. While condensing information seems straightforward, real-world enterprise summarization of support ticket
arXiv:2608.04023v1 Announce Type: cross Abstract: Sri Lanka's fisheries sector is important for jobs and food supply. Between 2019 and 2025, it faced several major problems at the same time, and how t
arXiv:2601.18056v2 Announce Type: replace Abstract: We discuss the benefits of incorporating oscillatory neural mechanisms into the study of bilingual production errors and their traditionally documen
arXiv:2608.05145v1 Announce Type: new Abstract: While modern 3D reconstruction excels at modeling object geometry and appearance, it largely ignores the rich acoustic cues revealed through physical in
arXiv:2608.04113v1 Announce Type: cross Abstract: Black-box optimization is a ubiquitous problem in science and engineering, often dealing with expensive objective functions with cheaper lower-fidelit
arXiv:2608.04593v1 Announce Type: cross Abstract: Echo State Networks (ESNs) offer an efficient framework for temporal prediction, but their randomly initialized reservoirs are often over-parameterize
arXiv:2608.04752v1 Announce Type: new Abstract: X-ray computed tomography (CT) reconstructs volumetric representations of objects from projection images obtained by transmitting X-rays through a targe
arXiv:2608.04246v1 Announce Type: cross Abstract: Vision-language-action policies often fail under deployment-time distribution shifts such as clutter, distractor objects, lighting changes, novel obje
arXiv:2603.11532v2 Announce Type: replace-cross Abstract: We study the problem of estimating multiple discrete unimodal distributions, motivated by search behavior analysis on a real-world platform. T
arXiv:2608.04432v1 Announce Type: cross Abstract: On two-sided content platforms, symmetric two-sided isolation (assigning matched fractions of creators and viewers to isolated treatment and control s
This definitely seems like something worth noting, and illustrates the gap between Fable/Astra class models and the previous frontier that was 'merely' good at hacking under human instructions. Initia
Together Serverless Inference gives developers a managed production path for bringing FLUX 3 into creative products and automated media workflows. Start building: https://www.together.ai/models/flux-3
arXiv:2608.04429v1 Announce Type: new Abstract: Backlit and low-light images often suffer from severe exposure imbalance or global underexposure, presenting significant challenges for both visual perc
arXiv:2608.04170v1 Announce Type: cross Abstract: AI co-scientists can generate fluent materials-science hypotheses, but fluency does not show that an answer preserves a scientifically meaningful mech
Also I think AISI is a great model of a government agency tasked with AI security. They have open benchmarks, very fast testing, and clear communication about incidents that is neither hyped up nor hi
arXiv:2608.03715v1 Announce Type: new Abstract: Mean-reverting dynamics are pervasive in finance, and the Cox--Ingersoll--Ross (CIR) process is a standard model for the time series they produce, from
arXiv:2608.03653v1 Announce Type: new Abstract: Network dismantling is fundamental to analyzing the robustness and vulnerability of complex systems, yet practical heuristics must balance effectiveness
arXiv:2608.03340v1 Announce Type: new Abstract: Predicting LLM's capabilities on real-world tasks is essential, yet the extent to which performance on commonsense benchmarks predicts downstream perfor
arXiv:2608.03129v1 Announce Type: new Abstract: Large Language Model-assisted Evolutionary Search (LES) has emerged as a powerful paradigm for automated algorithm design. However, existing LES methods
arXiv:2608.03772v1 Announce Type: new Abstract: Explaining the predictions of neural networks is a central challenge in trustworthy AI. Existing explanation methods, such as those based on feature att
arXiv:2608.03360v1 Announce Type: cross Abstract: Non-intrusive reduced-order models (NIROMs) have become a standard tool for approximating parametric partial differential equations from computer desi
arXiv:2608.03628v1 Announce Type: new Abstract: Adaptive conformal prediction can recover clinically important heartbeat classes missed by a point classifier, but delayed feedback makes its decisions
arXiv:2608.02869v1 Announce Type: new Abstract: Ptychography neural networks suffer from scaling inconsistencies when generalizing out of distribution, limiting their real world viability. We address
arXiv:2608.02694v1 Announce Type: new Abstract: Long-horizon video editing agents receive final-product feedback only after many interdependent decisions. Yet editing quality is subjective, admits mul
arXiv:2601.20771v2 Announce Type: replace-cross Abstract: Accurate forecasting of infectious disease incidence is critical for public health planning and timely intervention. While most data-driven fo
arXiv:2608.03893v1 Announce Type: new Abstract: Production deployments often swap between different-sized models in a family for cost-quality cascading, mid-conversation switching, and routing, and ea
arXiv:2608.02833v1 Announce Type: cross Abstract: Chart question answering (CQA) requires multimodal large language models (MLLMs) to integrate visual comprehension with logical reasoning, yet current
arXiv:2608.03413v1 Announce Type: new Abstract: As artificial intelligence (AI) continues to evolve and mature, recent AI practices have moved beyond large language models (LLMs) and text or image gen
arXiv:2608.02699v1 Announce Type: new Abstract: When algorithms make or influence consequential decisions---about loan eligibility, hiring, or healthcare---EU law grants affected individuals a Right t
arXiv:2608.03923v1 Announce Type: new Abstract: Codebook-based blind face restoration (BFR) often suffers from ambiguous conditioning features and a fragile prediction mechanism under severe degradati
arXiv:2608.02604v1 Announce Type: new Abstract: LLM-based agents are increasingly being deployed for data-related tasks, including data sense-making, exploration, and retrieval. However, their perform
arXiv:2608.03600v1 Announce Type: new Abstract: Partial differential equations (PDEs) become actionable in science and engineering not as isolated formulae, but as executable workflows that connect mo
arXiv:2608.03489v1 Announce Type: cross Abstract: Combining Bayesian learning and quantitative verification is a powerful toolset for analysing key quantitative properties of software systems, like re
arXiv:2603.24929v2 Announce Type: replace Abstract: Understanding and quantifying uncertainty in large language model (LLM) outputs is critical for reliable deployment. However, traditional evaluation
arXiv:2502.06606v3 Announce Type: replace Abstract: Manipulating the material appearance of objects in images is critical for applications like augmented reality, virtual prototyping, and digital cont
arXiv:2608.02627v1 Announce Type: cross Abstract: Zero Trust Architecture (ZTA) principles need rigorous network segmentation and ongoing verification to reduce implicit trust and lateral threat propa
arXiv:2608.02892v1 Announce Type: new Abstract: Scene Graph Generation (SGG) is fundamental to structured visual understanding, yet existing benchmarks focus mainly on daily life images and overlook s
arXiv:2608.03017v1 Announce Type: new Abstract: There has been significant interest in using machine learning algorithms to predict kidney transplant outcomes, such as the number of years until a graf
arXiv:2608.02692v1 Announce Type: new Abstract: Access to holistic, multimodal data improves the performance of Artificial Intelligence (AI) in medical classification tasks compared to utilizing singl
arXiv:2608.03296v1 Announce Type: cross Abstract: Accurate extrinsic rotation calibration between sensors is fundamental to the performance of robotic perception systems. However, most existing calibr
arXiv:2608.03926v1 Announce Type: cross Abstract: Time series anomaly detection (TSAD) underpins applications in predictive maintenance, finance, and cloud computing, however performance remains sensi
arXiv:2608.03006v1 Announce Type: new Abstract: Prerequisite relation learning is central to adaptive instruction, yet existing methods often formulate it as conventional link prediction, limiting the
arXiv:2207.12877v3 Announce Type: replace Abstract: Motivated by the successes of deep learning, we propose a class of neural network-based discrete choice models, called RUMnets, inspired by the rand
arXiv:2603.16548v2 Announce Type: replace-cross Abstract: In light of globalized hardware supply chains, the assurance of hardware components has gained significant interest, particularly in cryptogra
arXiv:2608.02780v1 Announce Type: new Abstract: In robot teleoperation, haptic feedback can be used to help human operators accomplish dexterous manipulation tasks. However, existing haptic feedback m
arXiv:2608.03432v1 Announce Type: cross Abstract: Refurbishment-based noisy-label learning mixes an observed label with a model-derived pseudo target, typically using one sample-wise cleanliness score
arXiv:2608.02695v1 Announce Type: cross Abstract: Software supply-chain attacks increasingly exploit an identity gap where compromised maintainer accounts authorize malicious changes. This work evalua
arXiv:2608.03087v1 Announce Type: cross Abstract: Fine-grained energy consumption data are essential for applications such as demand forecasting, demand response planning, and grid reliability assessm
arXiv:2511.21095v2 Announce Type: replace Abstract: Early Stage Ranking (ESR) in large-scale recommendation systems is dominated by ''user--item decoupling'' Two Tower architectures, which scale effic
arXiv:2507.19308v2 Announce Type: replace-cross Abstract: In this paper, we present our studies and experiments carried out for the task 1 of the Challenge and Workshop on Multilingual Conversational
arXiv:2608.03706v1 Announce Type: new Abstract: Statistical learning is a fascinating field that has long been the mainstream of machine learning/artificial intelligence. A large number of results hav
arXiv:2608.02975v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated impressive performance in MQM-based translation quality (TQ) evaluation, and recent advances in large r
arXiv:2608.03123v1 Announce Type: cross Abstract: Machine unlearning aims to eliminate the influence of sensitive data on a model. In the real world, unlearning requests arrive continually, which give
arXiv:2608.03811v1 Announce Type: new Abstract: We introduce the Unified Neural Variational Measurement of Proficiency (UNVaMP) architecture, a knowledge tracing method that integrates observed studen
arXiv:2601.09445v2 Announce Type: replace-cross Abstract: In language models (LMs), intra-memory knowledge conflict arises when inconsistent information about the same subject is encoded within the mo
arXiv:2608.03666v1 Announce Type: new Abstract: Self-supervised monocular depth estimation has emerged as an appealing solution to design lightweight and effective models for deployment on computation
arXiv:2608.00369v1 Announce Type: new Abstract: This letter presents an automated robotic manufacturing system for soft capacitors which operate as actuators and sensors. Emphasis is placed on the two
arXiv:2608.02073v1 Announce Type: cross Abstract: We investigate Optimization under Input Uncertainty (OIU), in which the input to the objective function, rather than the objective function itself, is
arXiv:2608.02471v1 Announce Type: new Abstract: Computational attention models could help surgeons manage the visual demands of laparoscopy, but they require dense spatial labels that are difficult to
Advanced Micro Devices Inc. delivered another strong quarter Tuesday, beating Wall Street expectations as its artificial intelligence infrastructure business continued its rapid expansion. The company