Knowledge catalogue
arXiv:2608.13835v1 Announce Type: new Abstract: Dynamic topic models capture evolving word distributions, but traditional coherence metrics may fail when vocabulary changes while semantic meaning pers
arXiv:2601.08151v2 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable progress in vision-language understanding, yet how they internally integrate visua
arXiv:2606.17035v2 Announce Type: replace Abstract: Prior research suggests that differential privacy (DP) inherently enhances the robustness of federated learning (FL) against backdoor attacks. In th
arXiv:2608.13621v1 Announce Type: new Abstract: A hidden Markov model (HMM) combines three roles: inference of a hidden-state belief from observations, propagation through a Markov transition, and emi
The article describes an end‑to‑end DIY project that builds an AI text detector from scratch, including dataset construction, model training, local deployment, and a reinforcement learning verifier (R
8/19, join us for the #ACMTechTalk, 'From Conventional LLMs to Reasoning Models to Agents,' w/AI & LLM Research Engineer @rasbt. ACM Practitioner Board Co-Chaior @marlene_zw (@Microsoft) will moderate
arXiv:2608.12745v1 Announce Type: new Abstract: Medical AI has demonstrated specialist-level diagnostic accuracy, yet these capabilities remain largely inaccessible in resource-constrained rural setti
arXiv:2608.13183v1 Announce Type: new Abstract: Visual foundation models are a cornerstone of image and video understanding but typically require large amounts of data and computation. The current sca
arXiv:2608.12398v1 Announce Type: cross Abstract: We propose IM-LEPP (Integrated Multimodal Latent Energy-based Predictive Processing), a hierarchical, energy-based model of multimodal cognition that
arXiv:2506.04291v2 Announce Type: replace Abstract: With the proliferation of Internet of Things (IoT) devices, the demand for addressing complex optimization challenges has intensified. The Lyapunov
arXiv:2608.13073v1 Announce Type: new Abstract: Significant health risks are associated with the illegal, yet commonly practiced use of industrial-grade Calcium Carbide (CaC2) for ripening climacteric
arXiv:2608.13456v1 Announce Type: new Abstract: World Models (WM) are increasingly seen as a foundation for intelligent agents that can predict, plan, and act beyond their training distribution. In th
arXiv:2505.12599v3 Announce Type: replace-cross Abstract: We propose a class of discrete state sampling algorithms based on Nesterov's accelerated gradient method, which extends the classical Metropol
arXiv:2608.12903v1 Announce Type: new Abstract: The k-Nearest Neighbor~(KNN) algorithm is widely used across various tasks. The selection of the k value is a key issue because it significantly impacts
arXiv:2608.12863v1 Announce Type: new Abstract: As AI systems proliferate in consumer facing applications, questions about liability for AI related harms remain unresolved. This working paper examines
arXiv:2608.12835v1 Announce Type: new Abstract: Unmanned Aerial Vehicle Vision-Language Navigation (UAV-VLN) requires agents to follow language instructions, infer spatial structure from sparse multi-
arXiv:2608.13492v1 Announce Type: new Abstract: This report presents an improved version of AlayaWorld. While the backbone architecture, chunk-wise autoregressive generation scheme, and training data
arXiv:2608.13433v1 Announce Type: cross Abstract: Transformer-based language models are known to sometimes generalize to sequences longer than seen during training, but we lack a precise characterizat
arXiv:2608.12548v1 Announce Type: cross Abstract: Dance imitation integrates motor planning, sensorimotor integration, and social cognition, offering a sensitive framework to characterize motor behavi
arXiv:2608.12329v1 Announce Type: cross Abstract: Progress on AI for psychosis-risk assessment is limited by a data-access bottleneck. Real clinical interviews are difficult to share because of privac
arXiv:2608.13430v1 Announce Type: cross Abstract: Instruction-tuned language models achieve strong performance across a range of generation tasks, but have also recently been shown to exhibit verbaliz
arXiv:2608.12374v1 Announce Type: cross Abstract: Language models (LMs) struggle with logical tasks like reasoning on syllogisms. It has been shown that Knowledge Representation (KR) plays a crucial r
arXiv:2608.12840v1 Announce Type: new Abstract: Visual-inertial navigation systems estimate six-degree-of-freedom motion by fusing visual and inertial data. Modern discrete-time methods with IMU prein
arXiv:2508.18829v3 Announce Type: replace Abstract: National Forest Inventory (NFI) serves as the primary source of forest information, however, maintaining these inventories requires labor-intensive
arXiv:2608.12351v1 Announce Type: cross Abstract: Generative artificial intelligence (GenAI) has challenged the validity of unsupervised online assessment, especially in technical subjects where plaus
arXiv:2608.12570v1 Announce Type: new Abstract: Fashion retrieval often requires satisfying multiple attributes at once, such as category, color, pattern, and demographic. Monolithic embeddings mix th
arXiv:2603.16946v2 Announce Type: replace-cross Abstract: Currently, an excessive amount of event data is being obtained in four-dimensional inelastic neutron-scattering experiments. A method for auto
arXiv:2608.12936v1 Announce Type: cross Abstract: As quantum computing progresses from proof-of-principle demonstrations toward practical utility, a significant impediment is the need to augment algor
arXiv:2608.13514v1 Announce Type: cross Abstract: We revisit the problem of learning predictors robust to adversarial examples at test-time. We prove that VC classes are adversarially robustly learnab
arXiv:2608.12679v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed in discovery domains such as math and science. The usual approach is to present the problem to th
arXiv:2608.12403v1 Announce Type: cross Abstract: Pre-trained black-box predictive functions encode knowledge distilled from massive datasets and extensive computation. However, when the available inp
Brilliant new paper from Meta. LLM judges get validated on accuracy against golden data. That says nothing about whether the verdict survives when questioned. The Wiggle Framework stress-tests 9 front
arXiv:2506.17337v5 Announce Type: replace-cross Abstract: Vision Language Models (VLMs) have shown promise in automating image diagnosis and interpretation in clinical settings. However, developing sp
arXiv:2608.12332v1 Announce Type: new Abstract: In recent years, low-rank adaptation (LoRA) has emerged as a significant paradigm that freezes pre-trained weights and introduces small, learnable adapt
arXiv:2608.13459v1 Announce Type: cross Abstract: We address the use of large language models (LLMs) to help discover Isabelle proofs. An Isabelle build establishes that the submitted theory is accept
arXiv:2608.13396v1 Announce Type: new Abstract: Shape and force sensing have long been critical bottlenecks in the development of compact capstan-driven continuum surgical robots, primarily due to the
arXiv:2608.13101v1 Announce Type: new Abstract: Research on automatic speaking assessment (ASA) has increasingly adopted multimodal speech large language models to assess learners' speaking performanc
arXiv:2510.22282v2 Announce Type: replace-cross Abstract: Urban socio-economic sensing plays a vital role in advancing global sustainable development goals. With the advent of Large Vision-Language Mo
arXiv:2608.12974v1 Announce Type: cross Abstract: McCoy & Griffiths (2025, henceforth M&G) suggest that a Bayesian prior can be distilled into Artificial Neural Networks (ANNs) through Model-Agnostic
arXiv:2606.21202v2 Announce Type: replace-cross Abstract: Reaching global agreement from purely local interactions is a defining problem of collective intelligence, and most models of it assume that a
arXiv:2608.13465v1 Announce Type: cross Abstract: Concept drift refers to changes over time in the statistical properties of data, as compared to the data that was used to train a learning model. Mach
arXiv:2509.23176v2 Announce Type: replace Abstract: The Segment Anything Model (SAM) exhibits strong zero-shot performance on natural images but suffers from domain shift and overconfidence when appli
arXiv:2608.13226v1 Announce Type: cross Abstract: While 3D Vision-Language Models (3D VLMs) have demonstrated remarkable spatial reasoning capabilities, they suffer from massive visual token counts th
arXiv:2608.13524v1 Announce Type: new Abstract: Speculative decoding losslessly accelerates autoregressive language models by verifying multiple draft tokens in parallel. Diffusion-based drafters furt
arXiv:2608.12599v1 Announce Type: new Abstract: Multi-turn dialogues let users revoke constraints as easily as impose them, but revocation does not reliably take effect: models keep enacting withdrawn
arXiv:2608.12935v1 Announce Type: new Abstract: Perturbation methods explain model decisions by measuring prediction changes under altered inputs, but response magnitude tells us only how much a model
arXiv:2608.13554v1 Announce Type: new Abstract: We study online probabilistic forecasting of binary outcomes chosen by an adaptive adversary. Given an online learning algorithm for a weak hypothesis c
arXiv:2608.12680v1 Announce Type: cross Abstract: Item demand forecasting is an integral component of store assortment optimization. Existing literature focuses on learning a suitable customer choice
arXiv:2608.12670v1 Announce Type: new Abstract: IT service management (ITSM) systems accumulate large volumes of heterogeneous ticket data that are difficult for sales and executive stakeholders to co
arXiv:2608.12757v1 Announce Type: cross Abstract: Laplacian-regularized minimization is fundamental in signal processing and machine learning, but is limited by the dense and ill-conditioned nature of
arXiv:2608.12864v1 Announce Type: cross Abstract: Public blockchain data enables large-scale DeFi-related analysis, but many existing approaches are application-specific, difficult to scale, or hard t
arXiv:2608.12828v1 Announce Type: cross Abstract: Distribution steering seeks feedback laws that drive the state law of a dynamical system between prescribed initial and terminal distributions. Optima
arXiv:2608.13048v1 Announce Type: new Abstract: In this work, we introduce DMDIntel which uses dynamic mode decomposition (DMD) to make the predictions made by LLMs in a classification task interpreta
arXiv:2608.13489v1 Announce Type: new Abstract: We present extbf{DreamX-Phi 1.0}, an action-conditioned video world model for robotic manipulation that, given an observed frame, a language instruction
arXiv:2608.12385v1 Announce Type: new Abstract: As large language models serve more requests, cumulative inference cost is becoming increasingly important relative to one-time training cost. The two i
arXiv:2608.12587v1 Announce Type: cross Abstract: This article presents with DYSANOS the first generative market model for smooth SANOS option surfaces for all strikes and expiries which are free of s
arXiv:2608.13072v1 Announce Type: new Abstract: Electroencephalography (EEG) decoding models often generalize poorly across datasets and subjects due to domain shifts in acquisition protocols and indi
arXiv:2608.12704v1 Announce Type: cross Abstract: Multi-objective bilevel optimization has wide applications in the AI area such as automated learning and multi-task meta-learning. Although recently s
arXiv:2505.16733v3 Announce Type: replace Abstract: This paper proposes to perform image restoration through a state-dependent mean-reverting forward diffusion (FoD) process. In contrast to traditiona
arXiv:2411.16818v2 Announce Type: replace-cross Abstract: To evaluate a multi-representational framework in which large language model (LLM)-generated expert summaries of intensive care unit (ICU) not