Knowledge catalogue
arXiv:2607.13347v1 Announce Type: cross Abstract: LLM-as-a-judge is widely used to provide feedback and selection signals in closedloop regeneration, but this use remains insufficiently validated. We
arXiv:2607.13884v1 Announce Type: new Abstract: Large Language Model (LLM) agents have shown remarkable capabilities in autonomous decision-making by generating sequential trajectories of states, acti
arXiv:2607.13469v1 Announce Type: cross Abstract: The banking sector increasingly relies on automated systems to monitor electronic transactions for signs of fraud, yet conventional rule-based approac
arXiv:2607.13655v1 Announce Type: new Abstract: Explainable Reinforcement Learning (XRL) seeks to make Reinforcement Learning (RL) policies more transparent and interpretable, a key requirement in saf
arXiv:2607.13653v1 Announce Type: new Abstract: Real-world deployment of embodied agents requires active exploration, visual grounding, and interactive intent disambiguation. However, existing framewo
arXiv:2607.13416v1 Announce Type: new Abstract: Automating analog circuit topology design is essential to reduce the extensive manual effort required to meet increasingly diverse and customized applic
arXiv:2607.13511v1 Announce Type: cross Abstract: We introduce ExTernD (Expanded-rank Ternary Decomposition), a post-training factorization of each LLM weight matrix A in R^{m imes n} into A approx B
arXiv:2607.13344v1 Announce Type: new Abstract: Constraint Answer Set Programming (CASP) is a hybrid reasoning paradigm that combines Answer Set Programming (ASP) with Constraint Processing and Satisf
arXiv:2607.13498v1 Announce Type: new Abstract: Learning a compact model of the world from interaction data is central to sample-efficient deep reinforcement learning. Spectral representation methods
arXiv:2607.13303v1 Announce Type: cross Abstract: Formal contracts are essential for software testing and verification, yet writing them remains labor-intensive and error-prone. LLMs offer a promising
arXiv:2607.13613v1 Announce Type: cross Abstract: Centroid neural network (CentNN) is an unsupervised competitive learning algorithm in which centroid splitting is triggered only after strict local st
arXiv:2607.13045v1 Announce Type: cross Abstract: Federated Learning (FL) has emerged as a key paradigm for privacy-preserving collaborative model training across distributed and heterogeneous data so
arXiv:2607.13040v1 Announce Type: cross Abstract: This paper examines where final authority should sit once capable AI systems are embedded in organizational workflows. It compares two governance mode
arXiv:2607.13661v1 Announce Type: new Abstract: Contrastive Language-Image Pre-training (CLIP) has been shown to have limitations in its fine-grained dense feature representation, due to its pre-train
arXiv:2607.13892v1 Announce Type: new Abstract: Computed tomography (CT) vision-language pretraining from paired volumes and radiology reports is a scalable yet challenging task. Existing methods comm
arXiv:2603.19063v2 Announce Type: replace Abstract: Most existing robot simulators prioritize rigid-body dynamics and photorealistic rendering, but largely neglect the thermally and optically complex
arXiv:2607.13035v1 Announce Type: cross Abstract: Cloud services experience frequent incidents that require rapid diagnosis and resolution. Troubleshooting guides help engineers respond consistently,
arXiv:2607.13553v1 Announce Type: cross Abstract: Autonomous robotic navigation in nonstationary time-varying fluid flows remains a fundamental challenge due to partial observability and the unpredict
arXiv:2607.13386v1 Announce Type: new Abstract: Building foundation models for medical imaging requires pooling data across institutions, yet privacy regulations prohibit centralized aggregation. Exis
arXiv:2607.13298v1 Announce Type: new Abstract: In online streaming video understanding, a video stream continues to arrive and queries may be issued at any time. Because streaming frames grow without
arXiv:2607.13656v1 Announce Type: new Abstract: Image-based indoor scene relighting remains challenging due to the complex interplay between cluttered geometry and local illumination, requiring precis
arXiv:2607.13624v1 Announce Type: cross Abstract: Natural language interaction provides an intuitive way for non-expert users to communicate with robotic platforms. However, transforming user requests
arXiv:2607.13546v1 Announce Type: new Abstract: Mobile crowdsensing (MC) recruits mobile users to perform sensing tasks using their smartphones, enabling large-scale applications such as traffic monit
arXiv:2607.14076v1 Announce Type: new Abstract: Building interactive worlds that respond coherently to player actions has long been a shared goal of computer graphics, games, and artificial intelligen
arXiv:2607.13587v1 Announce Type: cross Abstract: Automatic symbolic music analysis has made substantial progress, yet existing systems are typically designed for a single mode of use, such as full-sc
arXiv:2607.13651v1 Announce Type: new Abstract: The bottleneck of Earth Observation processing chains is not the arrival of new imagery but whether the surface is actually visible when the image arriv
arXiv:2607.13095v1 Announce Type: cross Abstract: We present a full-pipeline inference optimization for the MiMo-V2.5 model family, which combines Hybrid Sliding Window Attention (Hybrid SWA), sparse
arXiv:2607.13609v1 Announce Type: new Abstract: Recovering a latent potential from observed flow on a directed graph (a discrete Poisson problem with Dirichlet boundaries) is ill-posed, and the standa
arXiv:2607.13429v1 Announce Type: cross Abstract: Finetuning a pretrained vision-language model (VLM) on robot demonstrations via behavior cloning (BC) has become the standard recipe for vision-langua
arXiv:2607.13921v1 Announce Type: cross Abstract: Languages with rich static semantics, such as Rust, provide stronger guarantees for AI-generated code, but their strictness makes generation more diff
arXiv:2607.13454v1 Announce Type: cross Abstract: Although multimodal large language models (MLLMs) have achieved remarkable progress, understanding 3D spatial relationships from 2D images remains a c
arXiv:2607.13394v1 Announce Type: cross Abstract: Generative Flow Networks (GFlowNets) offer a promising alternative to reward-maximizing reinforcement learning (RL) for large reasoning models, encour
arXiv:2607.13569v1 Announce Type: cross Abstract: Transit video understanding can provide valuable fine-grained data that conventional passenger counters and fare systems cannot capture. However, supe
arXiv:2607.13960v1 Announce Type: new Abstract: World Action Models (WAMs) improve robot policy learning by jointly modeling actions and future visual observations, using future scene evolution as den
arXiv:2603.12361v2 Announce Type: replace Abstract: Motion planning through narrow passages remains a core challenge: sampling-based planners rarely place samples inside these narrow but critical regi
arXiv:2607.13481v1 Announce Type: new Abstract: Accurate 3D scene understanding is fundamental to embodied intelligence and autonomous driving, where 3D occupancy provides a unified representation of
arXiv:2607.13059v1 Announce Type: new Abstract: Data-driven embodied AI is rapidly transitioning into a paradigm that scales training through massively parallel simulation, where GPU-accelerated simul
arXiv:2607.13568v1 Announce Type: cross Abstract: Can a language model estimate its familiarity with an entity before generating an answer? We study activations at the final prompt token in twelve ins
arXiv:2607.13218v1 Announce Type: cross Abstract: In this work, we study various graph partitioning problems under a general demand model. In each such task, we are given a graph G=(V,E,c,w) with a ca
arXiv:2607.13555v1 Announce Type: cross Abstract: Bioacoustic call-type classification relies on costly expert annotation. Active learning can reduce this burden by selecting a small batch of segments
arXiv:2607.13712v1 Announce Type: cross Abstract: Despite the rapid progress of Multimodal Large Language Models (MLLMs), they still suffer from untruthfulness issues, such as visual hallucinations, c
arXiv:2607.13560v1 Announce Type: cross Abstract: Recent advances in generative and embodied AI have been driven by large-scale predictive learning over multimodal data. However, the resulting systems
arXiv:2607.13285v1 Announce Type: new Abstract: The capability of a modern AI agent depends not only on its foundation model but also on its harness, which constructs prompts, manages state, invokes t
arXiv:2607.13841v1 Announce Type: new Abstract: Heavy-tailed data arise in many domains where rare events carry disproportionate importance, such as imbalanced image datasets, financial returns, and w
arXiv:2607.13155v1 Announce Type: new Abstract: Generative molecular models can support early drug discovery by proposing new candidate compounds de novo. In practice, useful candidates must balance t
arXiv:2607.09776v2 Announce Type: replace-cross Abstract: When adapting Vision Language Action (VLA) models to downstream tasks, multiple rounds of post-training are often required to progressively ad
arXiv:2607.13217v1 Announce Type: cross Abstract: Consider the following variation on the Hierarchical Clustering problem: Usually, while building a hierarchical clustering, one recursively partitions
arXiv:2607.13468v1 Announce Type: new Abstract: Recently, a line of works can generate impressive 3D objects from a single image, but they are limited by restricted representation resolution, making t
arXiv:2607.13718v1 Announce Type: cross Abstract: As AI agents gain prevalance, users are increasingly exposed to the risks such systems entail. Prompt injection attacks, as well as hallucination, can
arXiv:2607.13548v1 Announce Type: new Abstract: Identifying root causes in production microservice failures requires reasoning over large-scale, multimodal telemetry spanning metrics, logs, and traces
arXiv:2607.13631v1 Announce Type: new Abstract: The Hessian matrix is an important quantity of interest when it comes to studying the loss landscape and optimization dynamics in deep learning, as well
arXiv:2607.13056v1 Announce Type: cross Abstract: Current vision-language-action (VLA) benchmarks primarily evaluate isolated manipulation skills while leaving human-robot interaction structure largel
arXiv:2607.13072v1 Announce Type: cross Abstract: Zero-shot object-goal navigation aims to enable an intelligent agent to explore and navigate to objects of unknown categories in an unfamiliar environ
arXiv:2607.13646v1 Announce Type: cross Abstract: Recent advances in 3D human reconstruction have improved overall performance, yet current models still fail in the most challenging real-world scenari
arXiv:2607.13573v1 Announce Type: cross Abstract: Maneuvering target tracking in three-dimensional space remains a challenging problem due to complex motion dynamics and model mismatch. To address thi
arXiv:2607.13737v1 Announce Type: new Abstract: For low-data and resource-constrained regimes typical of quantum chemistry, parameter-efficient learning is a key objective. Here, we propose a topology
arXiv:2607.13516v1 Announce Type: new Abstract: High-quality maps are fundamental for robotics tasks such as navigation and planning. Although modern graph-based LiDAR SLAM systems achieve good trajec
arXiv:2607.13300v1 Announce Type: new Abstract: Diffusion generative models have demonstrated immense potential for synthetic medical image generation. However, these models often struggle to capture
arXiv:2607.13115v1 Announce Type: new Abstract: Small language models (SLMs) have shown promise for zero-shot molecular property prediction from SMILES strings, yet they often suffer from structural b
arXiv:2607.13408v1 Announce Type: cross Abstract: Recent text-to-audio models generate high-quality audio, but often fail to follow instructions involving multiple sound events and temporal order. Thi