Knowledge catalogue
arXiv:2606.24236v1 Announce Type: cross Abstract: Visual assessment of residual plots is a common approach for diagnosing linear models, but it relies on manual evaluation, which does not scale well a
arXiv:2606.24245v1 Announce Type: cross Abstract: Large language model (LLM) agents increasingly automate complex tasks by integrating language models with external tools and environments. However, th
arXiv:2501.11790v5 Announce Type: replace-cross Abstract: Recent studies have raised significant concerns regarding the reliability of current mathematics benchmarks, highlighting issues such as simpl
arXiv:2606.23870v1 Announce Type: cross Abstract: PLCverif is the most mature open-source platform for PLC formal verification, developed at CERN and in production use since 2019. Yet it has two funda
arXiv:2606.24072v1 Announce Type: new Abstract: Fabric moire is a sampling-induced aliasing artifact caused by the interaction between fine textile patterns and camera sensor grids, producing structur
arXiv:2606.23825v1 Announce Type: cross Abstract: Efficient small object detection is bottlenecked by the inherent feature scarcity of tiny targets, which is further aggravated by operations of spatia
arXiv:2505.13087v2 Announce Type: replace-cross Abstract: We propose a novel benchmarking methodology for graph neural networks (GNNs) based on the graph alignment problem, a combinatorial optimizatio
arXiv:2606.23702v1 Announce Type: cross Abstract: Modulation recognition systems rely on heterogeneous signal representations. 2D signal-image modalities such as time-frequency and cyclostationary map
arXiv:2606.23238v2 Announce Type: replace Abstract: Logical reasoning is essential for reliable AI, yet existing benchmarks are largely first-order-logic-centric, focusing on object-level deduction ov
arXiv:2606.24335v1 Announce Type: new Abstract: Counterfactual analysis is widely used to study evidence use in vision-language models, but its diagnostic value is limited on well-posed tasks: when se
arXiv:2606.24011v1 Announce Type: cross Abstract: A crucial assumption in graph signal processing (GSP) is the existence of an underlying graph that captures the pairwise similarities between nodes, a
arXiv:2506.10520v5 Announce Type: replace-cross Abstract: Graph-based multi-task learning at billion-scale presents a significant challenge, as different tasks correspond to distinct billion-scale gra
arXiv:2510.24295v2 Announce Type: replace Abstract: As many benchmarks have become saturated, it has become increasingly important to create new datasets that evaluate the generalization capacity of c
arXiv:2606.24301v1 Announce Type: new Abstract: Recovering realistic 3D vehicle models from autonomous driving scenes is crucial for synthesizing training data and building simulation environment. How
arXiv:2606.19416v2 Announce Type: replace Abstract: Loan origination is the process by which a lender creates a new loan, from application and underwriting through approval and funding. This process s
arXiv:2606.23761v1 Announce Type: cross Abstract: Spiking neural network (SNN)-based neuromorphic speech enhancement has emerged as a promising paradigm due to its energy efficiency, yet it still unde
arXiv:2606.24564v1 Announce Type: new Abstract: Reconstructing realistic, physically plausible garments from a single image remains a fundamental challenge. Template-free methods capture surface geome
arXiv:2606.24523v1 Announce Type: cross Abstract: Scam phone calls exploit vulnerable communities worldwide, yet research on detection has focused almost exclusively on English and other high-resource
arXiv:2606.23992v1 Announce Type: cross Abstract: Clinical value sets define the standardized terminology codes used in quality measurement, phenotyping, cohort construction, and clinical decision sup
arXiv:2606.24307v1 Announce Type: cross Abstract: Interactive music and live performance relies on real-time human expression, but modern generative music AI remains largely absent from this domain du
Sakana AI CEO David Ha (@hardmaru) appeared on TBS CROSS DIG’s “1on1 Tech.” He talks about our founding story, latest research and technical vision, product launches including Sakana Fugu, Japan’s AI
arXiv:2606.19623v2 Announce Type: replace Abstract: Graph neural networks (GNNs) offer a flexible framework for learning from scientific data with physical, biological, or functional associations. One
arXiv:2606.23856v1 Announce Type: new Abstract: Generative molecular models for drug design are a promising direction with much active research. In the next phase of computational drug design, such mo
arXiv:2606.13189v2 Announce Type: replace Abstract: Prompt-based LLMs are increasingly used for stance detection, but harder examples are not always repaired by clearer instructions, reasoning prompts
arXiv:2606.24255v1 Announce Type: cross Abstract: Although text-to-motion generation has achieved strong progress in synthesizing realistic single-person motions from language, extending it to text-dr
arXiv:2606.24371v1 Announce Type: cross Abstract: Convolutional Kolmogorov--Arnold Networks (KANs) replace the fixed weights of a convolutional kernel with learnable univariate functions. The dominant
arXiv:2606.23739v1 Announce Type: cross Abstract: We present an automated large-scale search pipeline for heterogeneous 4-Expert Mixture-of-Experts (MoE4) architectures within the LEMUR neural network
We open-source Qwen-AgentWorld-35B-A3B (MoE, 35B/3B active, 256K context) and AgentWorldBench. Two routes, one roadmap: 🔬 Build the simulator — scalable, controllable, surpassing real environments 🧠 I
arXiv:2606.22631v1 Announce Type: new Abstract: 4D dynamic scene understanding requires grounding language to a persistent worldline that binds identity, metric 3D motion, and synchronized multi-view
arXiv:2606.22462v1 Announce Type: new Abstract: Pre-trained foundation models have demonstrated remarkable success in many domains, enabling a unified backbone to generalize across diverse downstream
arXiv:2606.21712v1 Announce Type: cross Abstract: Batch inference has become a central mode of AI computation, yet existing inference engines still rely on execution models designed for interactive se
arXiv:2606.21789v1 Announce Type: cross Abstract: Accurate 3D seismic velocity modeling through seismic travel-time tomography using both active- and passive-source data provides critical underpinning
arXiv:2606.20909v1 Announce Type: new Abstract: Earth observation imagery plays a critical role in environmental monitoring, urban planning, disaster assessment, and climate analysis. While multi-spec
arXiv:2606.21949v1 Announce Type: new Abstract: Accurate and comprehensive video captions with consistent subject references are critical for downstream understanding and generation tasks. However, fe
arXiv:2606.22000v1 Announce Type: cross Abstract: We introduce CFAgentBench, a reproducible, self-hostable environment and benchmark for autonomous construction-finance agents: a CFO/controller-class
arXiv:2606.21562v1 Announce Type: new Abstract: Transformers are AI's workhorse with strong performance in modeling sequential data, but their computational cost becomes prohibitive when processing lo
arXiv:2606.21116v1 Announce Type: new Abstract: Proofreading--correcting segmentation errors in 3D brain reconstructions--is the rate-limiting step in synapse-resolution connectomics. We release Conne
arXiv:2606.22439v1 Announce Type: new Abstract: Commercial greenhouse cucumber production is graded by fruit length, which drives harvest scheduling, labour allocation, and logistics. Manual measureme
arXiv:2606.21337v1 Announce Type: new Abstract: Massive unstructured multimodal streams suffer from high 'data entropy,' impeding both efficient human knowledge acquisition and high-quality AI post-tr
arXiv:2510.09388v2 Announce Type: replace Abstract: Reinforcement Learning (RL) has become a key driver for enhancing the long chain-of-thought (CoT) reasoning capabilities of Large Language Models (L
arXiv:2510.26113v2 Announce Type: replace Abstract: Do Video-LLMs have consistent temporal understanding when videos capture the same event from different viewpoints? To study this question, we introd
arXiv:2606.22304v1 Announce Type: new Abstract: Recently, several learning paradigms have been introduced to enforce idempotency in generative models. The goal is to ensure that repeated application o
arXiv:2606.22307v1 Announce Type: new Abstract: Data augmentation (DA) has been proven to be an effective means for improving protein representation learning (PRL) by generating additional training sa
arXiv:2606.20757v1 Announce Type: new Abstract: Recent multimodal survival prediction models have demonstrated strong predictive performance by leveraging complementary information across modalities.
arXiv:2408.07822v2 Announce Type: replace-cross Abstract: We explore the application of large language models (LLMs), pre-trained models with massive textual data for detecting and improving attention
arXiv:2606.21030v1 Announce Type: cross Abstract: Diffusion-based image compression methods, leveraging powerful generative priors, have demonstrated remarkable perceptual quality at ultra-low bitrate
arXiv:2606.19025v2 Announce Type: replace Abstract: Pre-training Large Language Models (LLMs) typically demands large-scale infrastructure with tightly coupled hardware accelerators. Mixture-of-Expert
arXiv:2606.22258v1 Announce Type: new Abstract: Electroencephalogram (EEG) analysis remains the clinical gold standard for epilepsy diagnosis and seizure detection. While Deep Learning (DL) has signif
arXiv:2606.20661v1 Announce Type: cross Abstract: The integration of external tools has transitioned LLM agents from passive responders to autonomous systems. However, current benchmarks prioritize ex
arXiv:2606.22872v1 Announce Type: new Abstract: Global furry conventions produce massive fursuit photographs, while manual sorting brings heavy labor costs and calls for automatic identity retrieval a
arXiv:2603.01501v2 Announce Type: replace Abstract: Asynchronous execution is essential for scaling reinforcement learning (RL) to modern large model workloads, including large language models and AI
arXiv:2606.20707v1 Announce Type: new Abstract: While humans can identify physically implausible events within milliseconds, machine learning approaches addressing the same problem are extremely slow
arXiv:2606.22409v1 Announce Type: new Abstract: Robots operating in everyday environments must understand fine-grained human actions, intentions, and contextual cues from broad views where people occu
arXiv:2602.04548v2 Announce Type: replace Abstract: We develop a general mathematical framework to analyze scaling regimes and derive explicit analytic solutions for gradient flow (GF) in large learni
arXiv:2606.20799v1 Announce Type: new Abstract: Generating visually consistent multi-shot videos remains an open challenge. As videos span more shots, inconsistencies can accumulate across shots, caus
arXiv:2606.22062v1 Announce Type: cross Abstract: Simulation-to-reality transfer, often called sim-to-real transfer, is a central challenge in robot learning. Yet, the tradeoff between measuring a sys
arXiv:2606.20624v1 Announce Type: cross Abstract: Significant progress has been made in aligning LLMs with target value functions. We argue that, even when an LLM has been well aligned in (post-)train
arXiv:2606.20999v1 Announce Type: cross Abstract: Understanding the generalization capabilities of visuomotor policies is essential in the development of capable robotic agents. Generalizable models l
arXiv:2606.22801v1 Announce Type: new Abstract: Image restoration aims to recover high-quality images from degraded observations. Recent Mamba-based image restoration models have demonstrated strong p
arXiv:2606.22831v1 Announce Type: cross Abstract: This paper studies learning-augmented online weighted vertex cover with advice and a parameter lambda in (0,1). We consider two graph cases: bipartite