Knowledge catalogue
arXiv:2608.07530v1 Announce Type: new Abstract: SHACL is a core technology for validating the conformance of RDF knowledge graphs (KGs). Yet, authoring SHACL shapes requires technical expertise that m
arXiv:2608.08489v1 Announce Type: new Abstract: Neural network classifiers trained by cross-entropy minimization are highly sensitive to label noise and adversarial contamination. While robust alterna
arXiv:2608.09602v1 Announce Type: new Abstract: Strictures caused by esophageal cancer can narrow down the esophageal lumen, leading to dysphagia. Palliation of dysphagia has driven the development of
arXiv:2608.08607v1 Announce Type: new Abstract: Mental health disorders are a leading cause of disability worldwide, yet Natural Language Processing (NLP) research for mental healthcare has remained c
arXiv:2604.02979v2 Announce Type: replace Abstract: Autoregressive (AR) video diffusion models enable long-form video generation but remain expensive due to repeated multi-step denoising. Existing tra
arXiv:2608.09176v1 Announce Type: cross Abstract: Visual token compression for vision--language models (VLMs) has largely relied on criteria such as attention, redundancy, and uncertainty to maximize
arXiv:2608.08939v1 Announce Type: new Abstract: The rise of autonomous AI agents represents a major paradigm shift in how users interact with mobile devices. Frameworks such as MobileRun and Mobile-Us
arXiv:2608.08389v1 Announce Type: new Abstract: Long-horizon research agents solve open-ended tasks through iterative retrieval, aggregation, and synthesis, but context grows rapidly while the margina
arXiv:2608.09782v1 Announce Type: new Abstract: This paper presents a review of the NTIRE 2026 Low-light Enhancement: Twilight Cowboy Challenge. The objective of the competition was to merge a set of
arXiv:2601.19040v2 Announce Type: replace Abstract: Time Series Foundation Models (TSFMs) are a powerful paradigm for time series analysis and are often enhanced by synthetic data augmentation to impr
arXiv:2608.08264v1 Announce Type: new Abstract: Large language model agents are becoming operational interfaces to files, memories, registries, and external tools. This deployment shift creates a new
arXiv:2608.08696v1 Announce Type: new Abstract: 3D occupancy prediction is fundamental to scene understanding, yet existing 3D semantic occupancy methods are typically specialized to fixed scene types
arXiv:2602.00488v3 Announce Type: replace Abstract: Solving large-scale capacitated vehicle routing problems (CVRP) is hindered by the high complexity of classical heuristics and the limited generaliz
arXiv:2608.09150v1 Announce Type: new Abstract: Unmanned aerial vehicle (UAV) infrared image super-resolution aims to recover weak thermal structures for deployment on resource-constrained platforms;
arXiv:2608.09227v1 Announce Type: new Abstract: Omnimodal language models (OLMs) enable unified audio-visual understanding, but processing long joint token sequences makes inference computationally pr
arXiv:2608.08775v1 Announce Type: new Abstract: Agentic benchmarks aim to measure how well AI agents plan, search, execute, and recover within realistic multi-tool environments, but they are almost ex
arXiv:2608.08566v1 Announce Type: cross Abstract: Malaria remains a leading cause of mortality in resource-limited settings, where expert microscopists are scarce. Automated diagnosis based on microsc
arXiv:2502.11027v5 Announce Type: replace Abstract: Large language model (LLM) scaling inference is key to unlocking greater performance, and leveraging diversity has proven an effective way to enhanc
arXiv:2505.00110v2 Announce Type: replace-cross Abstract: We show that deep Heaviside networks (DHNs) have limited expressiveness but that this can be overcome by including either skip connections or
arXiv:2608.08266v1 Announce Type: cross Abstract: Code generated by modern language models often reads naturally. Yet, it also often fails to implement what was asked. This should be no surprise, as r
arXiv:2608.07812v1 Announce Type: new Abstract: A host of recent studies have evaluated the cognitive and developmental alignment of Foundation Models (FMs). These investigations include evaluations o
arXiv:2608.09521v1 Announce Type: new Abstract: Activation-based tools are usually tied to one model's native hidden space, requiring probes, sparse autoencoders, and natural-language interpreters to
arXiv:2608.09403v1 Announce Type: new Abstract: Whole slide images (WSIs) in digital histopathology are acquired at discrete magnification levels encoding complementary diagnostic information from glo
arXiv:2608.09345v1 Announce Type: new Abstract: Advances in crop breeding have introduced an increasing number of grain varieties, creating a growing demand for efficient variety recognition and quant
arXiv:2608.08749v1 Announce Type: new Abstract: Long-horizon robot manipulation requires policies to track completed subtasks and critical interaction events. However, existing memory mechanisms heavi
arXiv:2608.09218v1 Announce Type: new Abstract: Score-driven filters multiply a scaled log-likelihood score by a gain that controls the update magnitude. We treat this gain as a decision variable and
arXiv:2608.09666v1 Announce Type: new Abstract: Recent advances in visual generative models have enabled high-quality image and video generation, but evaluating these models often demands sampling hun
arXiv:2608.07577v1 Announce Type: cross Abstract: A closed-set detector for autonomous driving must assign every object one of a fixed set of labels. On an object outside that set (a horse-drawn carri
arXiv:2608.08308v1 Announce Type: cross Abstract: Modern vision systems must operate in 'open-world' settings, where models must recognize known categories and detect unseen or anomalous content. Conv
arXiv:2608.09380v1 Announce Type: new Abstract: Long-horizon complex tasks require agents to repeatedly observe states, formulate plans, invoke tools, verify results, and recover from failures in cont
arXiv:2607.16235v3 Announce Type: replace-cross Abstract: Mobile and wearable devices offer an unprecedented opportunity for continuous, passive health monitoring and active health coaching. However,
arXiv:2608.08557v1 Announce Type: new Abstract: Visual tool use has emerged as a fundamental capability for multimodal agents to actively acquire evidence beyond a fixed image encoding. The prevailing
arXiv:2608.08265v1 Announce Type: new Abstract: Oracle routing measures how much a pool of language models could gain from per-query selection, but the diagnostic has two flaws: testing against a best
arXiv:2608.08416v1 Announce Type: new Abstract: Probably Approximately Correct (PAC) learning [Val84] is a fundamental learning model that has been extensively investigated. In this model, H subseteq
arXiv:2508.16410v3 Announce Type: replace-cross Abstract: Continuous-time Conflict Based Search (CCBS) has been widely used as an exact baseline for Continuous-time Multi-Agent Path Finding (MAPFR), a
arXiv:2505.06589v3 Announce Type: replace-cross Abstract: Modern machine learning repeatedly manipulates probability measures: empirical datasets, generated samples, latent distributions, class-condit
arXiv:2608.08311v1 Announce Type: cross Abstract: We present Ouroboros, a self-developing agent harness whose tools, prompts, context assembly, and core implementation improve through reviewed commits
arXiv:2608.08525v1 Announce Type: new Abstract: Federated Learning is a distributed machine learning paradigm that trains a global model by aggregating local clients without sharing private data of ea
arXiv:2608.09914v1 Announce Type: cross Abstract: Hardware assurance relies on scanning electron microscopy (SEM) to verify nanoscale structures, but assembling the large, high-quality datasets requir
arXiv:2608.07549v1 Announce Type: cross Abstract: Triangle meshes provide explicit and accurate surface geometry, yet their irregular topology connectivity makes 3D mesh tokenization a geometric sampl
arXiv:2608.09277v1 Announce Type: new Abstract: Verified code generation asks a large language model (LLM) to generate both an executable program and a machine-checkable proof that the program meets a
arXiv:2608.07631v1 Announce Type: cross Abstract: LLM-based full-duplex voice services allow users to speak while the assistant is responding. Because servers can generate output and advance dialogue
arXiv:2505.19766v4 Announce Type: replace Abstract: Large language models (LLMs) remain vulnerable to misalignment and jailbreaks, making external safeguards like moderation filters essential, yet exi
arXiv:2608.08368v1 Announce Type: new Abstract: Diabetic retinopathy (DR) remains a leading cause of vision loss among working-age adults worldwide, making reliable severity grading clinically importa
arXiv:2608.08049v1 Announce Type: cross Abstract: This paper presents a parameter-dependent linear matrix inequality (LMI) framework for trajectory tracking of nonholonomic mobile robots subject to se
arXiv:2608.09805v1 Announce Type: cross Abstract: Exploration has been a focus of reinforcement learning research for a long time. Recently, there has been growing evidence that it is also an importan
arXiv:2608.08753v1 Announce Type: new Abstract: Alzheimer's disease (AD) progression is a longitudinal process with subtle pathological cues in the early stages. Yet, computational constraints have li
arXiv:2608.09166v1 Announce Type: cross Abstract: Reliable uncertainty representation is essential for deploying autonomous systems that interact with their environment, as robots must reason about ho
arXiv:2608.08726v1 Announce Type: cross Abstract: On-policy self-distillation (OPSD) uses a privileged teacher to supervise a reasoning model on prefixes sampled from its own rollouts. Yet each rollou
arXiv:2608.09223v1 Announce Type: new Abstract: AI-generated image detectors generalize poorly when their training and test images originate from different generators or datasets. Despite the rich spa
arXiv:2608.08644v1 Announce Type: new Abstract: We consider the problem of sampling compositional and discrete objects from a given unnormalized posterior distribution. Notably, recent studies have sh
arXiv:2608.08078v1 Announce Type: new Abstract: Interval prediction aims to achieve a target coverage level while producing intervals that are as short as possible. Many conformal regression pipelines
arXiv:2608.07999v1 Announce Type: new Abstract: AI-generated image (AIGI) detection has become increasingly challenging due to the rapid advancement of generative models and the diminishing gap betwee
arXiv:2608.08773v1 Announce Type: new Abstract: Long-horizon multi-part object disassembly requires robots to compute feasible sequences of collision-free removal motions, even in the presence of tigh
arXiv:2510.22028v4 Announce Type: replace Abstract: Quality Estimation (QE) metrics are vital in machine translation for reference-free evaluation and increasingly serve as selection criteria in data
arXiv:2608.09931v1 Announce Type: new Abstract: Self-improvement for multimodal large language models (MLLMs) is typically driven by reward-based methods that provide only coarse scalar feedback. Dist
arXiv:2608.07543v1 Announce Type: cross Abstract: Background and Study Aims: Accurate optical diagnosis of colorectal polyps guides resection strategy and surveillance, with multimodal large language
arXiv:2608.07952v1 Announce Type: cross Abstract: Tool-augmented LLM agents can harbor implicit state that persists across sessions, activates through events, and propagates across agent boundaries---
arXiv:2608.09074v1 Announce Type: cross Abstract: We develop a new approach to Personalized Federated Learning across heterogeneous clients using Nonparametric Empirical Bayes (NPEB). Leveraging the a
arXiv:2608.09015v1 Announce Type: new Abstract: A significant challenge in exoskeleton robotics is the need to dynamically adapt control profiles to individual motion preferences, thereby ensuring bot