Knowledge catalogue
arXiv:2609.30055v1 Announce Type: cross Abstract: In the Era by Eon benchmark, each question states the rules for its answer, and code computes the answer from a generated company's data. When agents
arXiv:2609.29545v1 Announce Type: new Abstract: Deployed agents run on handed-over knowledge: a frozen policy consults a briefing of consolidated items written before the stream begins. The world then
arXiv:2609.29945v1 Announce Type: new Abstract: Robust control under delayed sensory feedback remains a key challenge in both robotics and neuroscience. Classical cerebellar models explain delay compe
arXiv:2609.28749v1 Announce Type: new Abstract: Diffusion models have shown strong potential for kilometer-scale precipitation downscaling, but their performance in geographically unseen regions and e
arXiv:2609.29496v1 Announce Type: new Abstract: Natural language explanation generation serves as a key mechanism for exposing and evaluating vision-language reasoning. Prior work on explanation-drive
arXiv:2609.29508v1 Announce Type: new Abstract: Large language model (LLM) agents may perform well on isolated tasks yet drift into inconsistency over extended interaction. We evaluate temporal consis
arXiv:2409.18486v5 Announce Type: replace Abstract: This comprehensive study evaluates the performance of OpenAI's o1-preview large language model across a diverse array of complex reasoning tasks, sp
arXiv:2609.29613v1 Announce Type: cross Abstract: We present a modular and multi-level framework for the differential diagnosis of malignant melanoma. Our framework integrates contextual information a
arXiv:2609.29016v1 Announce Type: cross Abstract: AI-driven scientific discovery accelerates research by autonomously developing solutions and designs. Large language model (LLM) agents support this p
arXiv:2609.28718v1 Announce Type: cross Abstract: We study Bayesian linear bandits with an isotropic Gaussian parameter, independent Gaussian candidate arms, and Gaussian reward noise when the horizon
arXiv:2609.29029v1 Announce Type: new Abstract: Onboard vision-language models could enable satellites to answer queries directly, but exhaustive tiled inference over high-resolution imagery is slow a
arXiv:2609.28949v1 Announce Type: new Abstract: Few-shot segmentation (FSS) aims to segment unseen object categories with a few (e.g., one or five) labeled examples, enabling efficient adaptation to n
arXiv:2609.30199v1 Announce Type: new Abstract: Scientific discovery begins where known problems end. There, AI systems must engage in exploration: framing hypotheses, designing experiments, and itera
arXiv:2609.28607v1 Announce Type: new Abstract: Intermittent time series are common in spare-parts demand and retail sales. Since the cost of forecast errors is typically asymmetric, decisions such as
arXiv:2609.29692v1 Announce Type: new Abstract: Fair allocation of scarce, indivisible resources is an important challenge in many societal problems. While there are several formal theories of fairnes
arXiv:2609.30127v1 Announce Type: new Abstract: Visuomotor policies learn a direct map from raw sensory observations to robot action sequences. Policies based on Diffusion and Flow Matching capture th
arXiv:2609.29216v1 Announce Type: cross Abstract: Diffusion models are powerful priors for linear inverse problems, but the reference guidance methods, Diffusion Posterior Sampling (DPS) and Pseudoinv
arXiv:2609.29076v1 Announce Type: cross Abstract: Predicting blood-brain barrier (BBB) permeability is critical for central nervous system drug discovery. Using the MoleculeNet BBBP dataset (n = 2039)
arXiv:2609.28695v1 Announce Type: new Abstract: This work presents a federated framework for training Averaged n-Dependence Estimators (AnDE) in distributed environments. The proposed method focuses o
arXiv:2512.16786v2 Announce Type: replace-cross Abstract: Specific emitter identification (SEI) utilizes passive hardware characteristics to authenticate transmitters, providing a robust physical-laye
arXiv:2609.29812v1 Announce Type: new Abstract: Looped Transformers have attracted substantial attention as a parameter-efficient approach to increasing computational depth through repeated applicatio
arXiv:2609.29330v1 Announce Type: new Abstract: Identifying the set of monitored websites in mixed encrypted traffic is challenging because an individual flow often provides only partial evidence of w
arXiv:2609.29006v1 Announce Type: new Abstract: Existing video deraining methods typically exploit neighboring frames through either explicit alignment or implicit spatiotemporal aggregation. Explicit
arXiv:2609.28887v1 Announce Type: new Abstract: Heterogeneous robot teams distribute complementary capabilities across specialized agents, but their physical roles and capacities typically remain fixe
arXiv:2609.28816v1 Announce Type: new Abstract: Robotic bodies are inherently distributed in sensing and actuation, yet learning-based control still commonly relies on centralized information processi
arXiv:2609.29374v1 Announce Type: cross Abstract: Conventional LiDAR-inertial odometry (LIO) or simultaneous localization and mapping (SLAM) methods heavily rely on geometric features of environments,
arXiv:2609.29125v1 Announce Type: new Abstract: In aerial RGB--IR object detection, effectively exploiting complementary information across modalities is critical for robust perception under complex i
arXiv:2609.28876v1 Announce Type: new Abstract: We introduce Forecast-Dojo, a replayable environment for benchmarking and training LLM forecasting agents. It combines resolved prediction-market questi
arXiv:2501.09223v3 Announce Type: replace-cross Abstract: This is a book about large language models. As indicated by the title, it primarily focuses on foundational concepts rather than comprehensive
arXiv:2609.29224v1 Announce Type: new Abstract: Dense metric depth from cameras is essential to real-world 3D applications, yet achieving accuracy, faithful surface geometry, and fast inference simult
arXiv:2609.29447v1 Announce Type: cross Abstract: Smart maritime infrastructures provide continuous access to heterogeneous sensing streams, enabling repeated experimentation, digital-twin development
arXiv:2609.28487v1 Announce Type: new Abstract: News headlines frame public issues both by what they select and by how they word it, yet computational framing work typically collapses these operations
arXiv:2609.29375v1 Announce Type: cross Abstract: Robust initialization is crucial for online systems. In the letter, a high-frequency and resilient initialization framework is designed for LiDAR-iner
arXiv:2609.29601v1 Announce Type: cross Abstract: End-to-end vision-language models (VLMs) bind visual competence to the scale of their language model: as the language model shrinks, perception and re
arXiv:2609.29879v1 Announce Type: new Abstract: Generative modeling approaches often focus on recovering broad statistical characteristics from the training data. In the context of graph generation, t
arXiv:2609.29983v1 Announce Type: cross Abstract: Semantic IDs (SIDs) encode each catalog item as a short token sequence, enabling generative recommenders to predict the next item autoregressively. Re
arXiv:2609.29091v1 Announce Type: new Abstract: Recent advances in agentic systems have substantially enhanced the long-horizon capability of embodied manipulation. However, many existing frameworks s
arXiv:2609.29370v1 Announce Type: cross Abstract: Science, technology, and innovation policies are crucial for competitiveness, yet their diversity and scale make them difficult to map and monitor con
arXiv:2609.28477v1 Announce Type: cross Abstract: Claims data can show that provider behavior changed but cannot by itself explain why. FWA (fraud, waste, and abuse) review requires identifying materi
arXiv:2609.30047v1 Announce Type: cross Abstract: Resistive switching in oxide-based devices is widely governed by stochastic defect processes, yet a predictive link between fabrication conditions and
arXiv:2609.29051v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) has become a popular recipe for post-training LLM agents. It supervises the agent model at the token level with a str
arXiv:2609.28942v1 Announce Type: new Abstract: Personalized value alignment has become increasingly important as large language models (LLMs) are expected to accommodate diverse user preferences. How
arXiv:2609.29750v1 Announce Type: new Abstract: Repeated excavation continuously reshapes pile geometry, requiring an autonomous excavator to adapt its digging targets and coordinate motion across suc
arXiv:2609.29283v1 Announce Type: new Abstract: Visual software often needs a decision over supplied alternatives rather than a generated explanation. We present PixelJev, a native-image decision inte
arXiv:2609.29108v1 Announce Type: new Abstract: European electricity trading in the EU operates as a constrained multi-layer system in which legal design, exchange microstructure, and network physics
arXiv:2609.29159v1 Announce Type: cross Abstract: Dynamic mode decomposition (DMD) is a data-driven method that computes the best linear approximation of the underlying dynamical system and decomposes
arXiv:2609.29670v1 Announce Type: new Abstract: In practical machine learning tasks, data are often contaminated with noise, outliers, and class imbalance, which can degrade the performance of convent
arXiv:2609.29327v1 Announce Type: cross Abstract: Selective classification enables a model to abstain from predictions on uncertain instances, but existing approaches typically reject them through con
arXiv:2609.29546v1 Announce Type: cross Abstract: The variational graph autoencoder (VGAE) regularizes its posterior toward the prior with the Kullback-Leibler divergence, a choice inherited from the
arXiv:2405.17044v4 Announce Type: replace Abstract: The rapid growth of scientific literature makes it increasingly challenging for researchers to identify novel and impactful ideas, especially across
arXiv:2609.29027v1 Announce Type: new Abstract: Atmospheric observations are sparse, heterogeneous, and unevenly distributed, whereas many generative atmospheric models learn distributions over regula
arXiv:2609.29491v1 Announce Type: cross Abstract: Voxel-based soft robots (VSRs) present a promising avenue for developing artificial organisms with lifelike intelligence. However, the vast design spa
arXiv:2609.28895v1 Announce Type: cross Abstract: Reliable intervention-oriented uncertainty quantification from Earth observation (EO) remains challenging when continuous treatment shifts, spatial de
arXiv:2609.28741v1 Announce Type: new Abstract: Multi-modal multitasking models have shown strong performance on remote sensing datasets. However, because these models are trained on heterogeneous dat
arXiv:2609.29541v1 Announce Type: cross Abstract: Referring segmentation in overhead imagery is inherently relational: a query may ask for the buildings north of the road or the pond closest to a resi
arXiv:2609.29999v1 Announce Type: cross Abstract: Post-training quantization of vision--language models (VLMs) is typically assessed through aggregate task accuracy and memory savings, but preserving
arXiv:2609.29861v1 Announce Type: new Abstract: We investigate whether GPT-6-Astra, a general-purpose foundation model, can navigate unfamiliar environments using its own perception, reasoning, and de
arXiv:2609.29328v1 Announce Type: new Abstract: Understanding how Large Language Models (LLMs) encode linguistic structures remains a fundamental challenge in interpretability research. While diagnost
arXiv:2609.30150v1 Announce Type: new Abstract: Modern infrastructure asset management constitutes a complex sequential decision-making problem, characterized by long planning horizons and system-leve
arXiv:2609.29668v1 Announce Type: cross Abstract: Large language model agents increasingly automate data workflows, but end-to-end cloud data engineering and analytical execution require reliable coor