Knowledge catalogue
arXiv:2607.23513v1 Announce Type: cross Abstract: Diagrams are widely used to support logical reasoning, and prior studies suggest that representations such as Euler diagrams can improve human reasoni
arXiv:2607.22653v1 Announce Type: new Abstract: Large language models are increasingly used in recursive refinement workflows, where an initial draft is repeatedly revised by the same model. Despite t
arXiv:2607.23496v1 Announce Type: new Abstract: Safety-aligned large language models are trained to refuse harmful requests, yet embedding the same requests in particular scenarios can bypass their sa
arXiv:2607.22644v1 Announce Type: new Abstract: Real-world document classification pipelines typically apply the same sequence of models to every incoming document, regardless of its complexity or typ
arXiv:2607.23197v1 Announce Type: new Abstract: Anomaly detection on multivariate sensor time series is critical for industrial monitoring of cyber-physical systems (CPS), where even subtle deviations
arXiv:2607.22679v1 Announce Type: new Abstract: In visually-rich documents, information is encoded not only in individual page objects such as tables, headers, and text blocks, but also in the structu
arXiv:2607.24434v1 Announce Type: cross Abstract: Large Mixture-of-Experts (MoE) language models are attractive for end-device deployment because only a small subset of experts is active per token, bu
arXiv:2603.05607v2 Announce Type: replace-cross Abstract: Computer-Aided Design (CAD) relies on structured and editable geometric representations, yet existing generative methods are constrained by sm
arXiv:2607.23822v1 Announce Type: new Abstract: Driving style captures stable, driver-specific patterns in how a vehicle is driven. In naturalistic data, however, this signal is hard to isolate becaus
arXiv:2607.23614v1 Announce Type: cross Abstract: We present DualityCert, a symbolic verifier for candidate Seiberg-duality claims in four-dimensional N=1 quiver gauge theories. The verifier evaluates
arXiv:2607.23924v1 Announce Type: cross Abstract: Vision foundation models have enabled strong training-free anomaly detection (AD). However, most existing approaches rely primarily on independent loc
arXiv:2607.22801v1 Announce Type: cross Abstract: Underwater image enhancement is challenged by spatially non-uniform, wavelength-dependent attenuation. Propagation distance and wavelength govern this
arXiv:2607.24331v1 Announce Type: new Abstract: As the inference phase of Large Language Models (LLMs) requires handling long context windows, the Key-Value (KV) cache initially appears to address thi
arXiv:2607.23722v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed as agents that interact with stateful environments over multiple steps: gathering hidden informat
arXiv:2607.23813v1 Announce Type: cross Abstract: We introduce Earnings25, a finance-domain benchmark for evaluating automatic speech recognition (ASR) on English-language earnings calls under realist
arXiv:2607.22705v1 Announce Type: new Abstract: Object-centric learning aims to represent scenes as objects whose properties can be reused in new combinations. Existing evaluations usually score segme
arXiv:2607.22889v1 Announce Type: new Abstract: Learning the natural parameters z in R^n of discrete distributions mu_z from independent samples constrained to a subset S subseteq {0,1}^n is a foundat
arXiv:2607.24714v1 Announce Type: cross Abstract: Trapped-ion quantum computers rely on shuttling compilers, which cast an input algorithm into a sequence of ion-qubit movements within a given archite
arXiv:2607.24560v1 Announce Type: cross Abstract: We introduce EgoPlay, an event-triggered video-to-video editor for egocentric streams, obtained by fine-tuning a pretrained V2V diffusion transformer
arXiv:2607.23899v1 Announce Type: cross Abstract: This exploratory study examines whether a large multimodal language model, GPT-5.1, can serve as the high-level controller of a physical mobile robot
arXiv:2607.24707v1 Announce Type: new Abstract: Entity-Relationship Diagrams (ERDs) are central to conceptual database design, yet they are typically available only as rendered images rather than mach
arXiv:2607.23326v1 Announce Type: new Abstract: The rapid rise of large language models (LLMs) has driven transformative adoption across enterprises. However, deploying these models in real-world sett
arXiv:2607.24577v1 Announce Type: new Abstract: Reinforcement Learning (RL) agents are increasingly deployed in safety-critical domains such as robotics, autonomous driving, and drone control, where u
arXiv:2607.20712v1 Announce Type: cross Abstract: Security protocol verification relies on formal tools such as ProVerif and OFMC. This study evaluates whether Large Language Models (LLMs) can perform
arXiv:2607.22609v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly used for decision-making and reasoning tasks, yet their potential as controllers for physical systems rema
arXiv:2607.24449v1 Announce Type: cross Abstract: International recruitment in France requires navigating a layered legal framework absent from existing legal AI benchmarks. We present a publicly avai
arXiv:2406.13128v2 Announce Type: replace Abstract: Due to the intricate structure of vascular trees, minor segmentation errors can significantly alter connectivity patterns and increase variability i
arXiv:2607.22633v1 Announce Type: new Abstract: Table Question Answering (TableQA) aims to reason over tables to answer user queries. Existing research treats all questions uniformly and evaluates sol
Secure artificial intelligence infrastructure startup Dymium Inc. today introduced GhostAI, a gateway designed to apply security and governance policies across the models, data, context and tools used
arXiv:2607.24177v1 Announce Type: cross Abstract: Due to the lack of systematic evaluations, we are not yet able to determine which AI-based Windows malware detector to deploy in production, since exi
arXiv:2607.24672v1 Announce Type: new Abstract: In safety-critical sectors such as robotics and automotive engineering, the deployment of Deep Reinforcement Learning (DRL) is often hindered by the bla
arXiv:2607.23466v1 Announce Type: new Abstract: Parameterized and coupled partial differential equations (PDEs) are central to modeling phenomena in science and engineering, yet neural operator method
arXiv:2607.23682v1 Announce Type: new Abstract: Early warning of extreme market volatility is central to financial risk management, but actionable events are rare, nonstationary, and often triggered b
arXiv:2407.14766v4 Announce Type: replace-cross Abstract: This paper presents a philosophical and experimental study of fairness interventions in AI classification, centered on the explainability and
arXiv:2607.23147v1 Announce Type: cross Abstract: Large language models now power autonomous agents capable of complex, multi-step tasks in different environments. Accurate and reliable execution of t
arXiv:2607.22637v1 Announce Type: new Abstract: Deep learning has shown strong potential for massive multiple-input multiple-output (Massive MIMO) physical-layer tasks, including channel state informa
arXiv:2607.23245v1 Announce Type: cross Abstract: Multimodal Federated Learning is often challenged by arbitrary modality missingness and Non-IID data distributions, which lead to severe representatio
arXiv:2607.24241v1 Announce Type: cross Abstract: Progress in video generation keeps narrowing the visual gap between AI-generated and professionally produced footage, yet most benchmarks still draw p
arXiv:2607.23877v1 Announce Type: new Abstract: We present Flash-CNNCap, a CNN-based capacitance extractor that reformulates full-matrix capacitance prediction as image-to-image regression over spatia
arXiv:2607.24207v1 Announce Type: new Abstract: Mobile manipulation requires robots to identify Floor Affordance (FloAff) that maximizes downstream manipulation success rather than merely ensuring nav
arXiv:2607.22698v1 Announce Type: new Abstract: Perception under adverse weather remains a critical bottleneck for reliable autonomous driving, yet existing benchmarks lack the systematic multi-modal
arXiv:2607.24585v1 Announce Type: new Abstract: We present ELMOD - Efficient Language Model for On-Device Deployment - a compact (2.7B) German language model designed for efficient inference on resour
arXiv:2607.23802v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has driven recent progress in reasoning-oriented large language models (LLMs) by enabling large-sc
arXiv:2607.24542v1 Announce Type: new Abstract: Automatic Text Recognition (ATR) now supplies digital humanities with large volumes of unstructured, heterogeneous, and often noisy text in ancient lang
arXiv:2607.22785v1 Announce Type: cross Abstract: Apple-Silicon SoCs share CPU, GPU, and Neural Engine over one unified memory system, raising the question of whether transformer inference can be acce
arXiv:2607.23542v1 Announce Type: new Abstract: Efficient border control is becoming a significant global challenge, mainly due to severe congestion and extended passenger waiting times. To mitigate t
arXiv:2607.22719v1 Announce Type: new Abstract: Multiplicative Gamma noise is a signal-dependent degradation in coherent imaging; synthetic aperture radar (SAR) despeckling is its most prominent real-
arXiv:2607.22847v1 Announce Type: new Abstract: Human gaze does more than point to visual targets; it serves as a subtle indicator of social intent within static images, whereas standard models typica
arXiv:2607.23621v1 Announce Type: new Abstract: This paper presents GEMCo, a releasable, human-written proxy for inaccessible counselling data: 86 complete German e-mail counselling conversations (728
arXiv:2607.23913v1 Announce Type: new Abstract: Modern vision-language models (VLMs) increasingly rely on dynamic or high-resolution visual encoding, producing thousands of visual tokens that substant
arXiv:2607.23657v1 Announce Type: new Abstract: Articulated portrait mesh estimation is fundamental to 3D understanding, avatar generation, and immersive interaction. Existing approaches primarily rel
arXiv:2607.24471v1 Announce Type: new Abstract: A central question in the in-context learning literature is whether transformers can organize episode-level adaptation around different inductive-bias f
arXiv:2607.24339v1 Announce Type: new Abstract: Large language model (LLM) agents inherit reactive failure modes: escalation under provocation, sycophantic drift under flattery, perseveration when stu
arXiv:2607.23538v1 Announce Type: new Abstract: Despite recent advances in large language models (LLMs), their ability to generate empathetic mental health counseling responses in low-resource languag
arXiv:2607.23361v1 Announce Type: cross Abstract: Language generation in the limit is an elegant model introduced by Kleinberg and Mullainathan [KM24] to formally study language generation by an algor
arXiv:2607.23412v1 Announce Type: new Abstract: Electrocardiograms (ECGs) are widely used for cardiovascular risk prediction, yet models often fail to transfer across hospitals because of protocol, po
Artificial intelligence startup HeyDonto AI Technology today announced that it has established DFT Labs, a research subsidiary dedicated to pursuing a physics-based framework for machine learning. The
arXiv:2607.22927v1 Announce Type: new Abstract: Weights and biases are normally optimized as separate parameter tensors, yet they do not represent separate functions when the input to an affine layer
arXiv:2607.24562v1 Announce Type: new Abstract: Large language models serve heterogeneous populations structured by domain, topic difficulty, and linguistic style. Conformal risk control (CRC) gives r
arXiv:2107.08183v2 Announce Type: replace Abstract: High-dimensional state and action spaces combined with sparse reward structures in reinforcement learning (RL) environments typically require advanc