Knowledge catalogue
arXiv:2605.27311v1 Announce Type: new Abstract: Chart question-answering (QA) benchmarks aim to pose questions that require visual reasoning to correctly answer, but models can often reach solutions t
arXiv:2605.26192v1 Announce Type: cross Abstract: Protein structure generative models excel at predicting single protein static structures from sequence, but routinely fail to capture the correct conf
arXiv:2605.26304v1 Announce Type: new Abstract: Collaborative navigation of heterogeneous robots in unknown environments poses significant challenges due to sensing, communication, and computational l
arXiv:2605.26440v1 Announce Type: new Abstract: The rapid advancement of Large Language Models (LLMs) has outpaced the scalability of traditional evaluation benchmarks, which remain heavily dependent
arXiv:2605.26365v1 Announce Type: new Abstract: Large Language Models (LLMs) often exhibit homogenized cultural perspectives. While the World Values Survey (WVS) provides a gold standard for mapping h
arXiv:2605.26446v1 Announce Type: cross Abstract: Graph anomaly detection (GAD) aims to identify nodes or substructures whose behavior or attributes deviate significantly from the overall pattern in g
arXiv:2605.26910v1 Announce Type: cross Abstract: Large EEG Foundation Models (FMs) have shown great potential for decoding EEG signals across diverse cognitive tasks. However, existing EEG-FM studies
arXiv:2605.26785v1 Announce Type: cross Abstract: Post-trained LLMs are often optimized to align responses with human preferences, making them safe, polite, and conversationally appropriate. In advers
arXiv:2509.04310v4 Announce Type: replace Abstract: Recent research on Chain-of-Thought (CoT) reasoning in Large Language Models (LLMs) has demonstrated that agents can engage in extit{complex}, extit
arXiv:2605.26590v1 Announce Type: cross Abstract: With the advancement of artificial intelligence systems capable of autonomously generating artistic, literary, musical works, and even inventions with
arXiv:2601.03089v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly evaluated with input attribution methods, yet comparing such explanations remains challenging. E
arXiv:2605.27284v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models are increasingly expected to not only complete robot tasks, but also follow human instructions about how those tas
arXiv:2605.25570v2 Announce Type: replace Abstract: Estimating continuous optical flow is a fundamental yet challenging problem in dynamic visual perception. Event-based cameras, with microsecond late
arXiv:2605.26158v1 Announce Type: cross Abstract: Safety alignment in large language models (LLMs) and multimodal large language models (MLLMs) is commonly assumed to operate as a near-binary threshol
arXiv:2605.26391v1 Announce Type: cross Abstract: Practical garment design spans two modes: intuitive creation from high-level intent, such as a reference image or text description, and complex low-le
arXiv:2605.27360v1 Announce Type: cross Abstract: Cellular research and development (R&D) is throttled by six structural processes that each consume months of manual engineering work per iteration: (i
arXiv:2605.26893v1 Announce Type: cross Abstract: Chain-of-Thought (CoT) reasoning has advanced large language models (LLMs), but outcome-based supervision leads to pervasive post-hoc rationalization,
arXiv:2603.08413v2 Announce Type: replace-cross Abstract: Deep neural networks for image classification often exhibit overconfidence on out-of-distribution (OOD) samples. To address this, we introduce
arXiv:2605.26230v1 Announce Type: new Abstract: Multi-view 3D reconstruction has achieved remarkable progress with the advent of feed-forward 3D reconstruction models. However, these models are typica
arXiv:2605.27328v1 Announce Type: cross Abstract: Recent advances in agentic systems increasingly treat code as an executable operational substrate rather than as a disposable output artifact. Prior w
arXiv:2603.11479v2 Announce Type: replace-cross Abstract: Time Series Event Detection (TSED) aims to localize semantically meaningful events in time series data, with critical applications in high-sta
arXiv:2510.03352v3 Announce Type: replace-cross Abstract: Diffusion models have been used as priors for solving inverse problems. However, existing approaches typically overlook side information that
arXiv:2601.00575v2 Announce Type: replace Abstract: Large language models (LLMs) have demonstrated significant advancements in reasoning and code generation, but efficiently creating new benchmarks to
arXiv:2511.14993v3 Announce Type: replace-cross Abstract: This report introduces Kandinsky 5.0, a family of state-of-the-art foundation models for high-resolution image and 10-second video synthesis.
arXiv:2605.27046v1 Announce Type: new Abstract: Motor thermal management is often overlooked in the context of electrically-actuated robots, particularly legged robots, but motor overheating is a key
arXiv:2605.26244v1 Announce Type: new Abstract: Audio-visual generation is rapidly advancing from short clips to minute-long content, while existing evaluation protocols remain largely confined to sho
arXiv:2605.26333v1 Announce Type: new Abstract: Educational virtual laboratories can make experimental training more scala-ble, adaptive, and accessible, especially when students have limited access t
arXiv:2605.26712v1 Announce Type: new Abstract: Benchmarks that reflect the diversity and complexity of real-world documents are essential for accurately evaluating Automatic Text Recognition (ATR) sy
arXiv:2605.26234v1 Announce Type: cross Abstract: A recent conjecture by Joel Fine posits a relationship between the coefficients of the HOMFLY polynomial of a knot K in the 3-sphere S^3, and the sign
arXiv:2605.26693v1 Announce Type: cross Abstract: Model merging offers a promising avenue for knowledge integration and parallel development without retraining. Yet, existing methods either ignore the
arXiv:2605.27235v1 Announce Type: new Abstract: Layered image generation and editing is a fundamental capability that enables layer-wise reuse, editing, and composition of generated visual content, an
arXiv:2605.27366v1 Announce Type: new Abstract: Large language model (LLM) agents rely on reusable skills to solve complex tasks. However, existing skill creation approaches treat skills as isolated a
arXiv:2605.26814v1 Announce Type: cross Abstract: We train a pair of autoregressive models to construct zero-mean control variates to mitigate the sign problem in quantum Monte Carlo simulations. The
arXiv:2410.00357v2 Announce Type: replace Abstract: Neural scaling laws play a pivotal role in the performance of deep neural networks and have been observed in a wide range of tasks. However, a compl
arXiv:2605.26243v1 Announce Type: new Abstract: Graph neural networks (GNNs) achieve strong performance on relational data, but real-world graphs are often distributed across organizations that cannot
arXiv:2605.27068v1 Announce Type: cross Abstract: Social deduction games have become a popular testbed for probing reasoning, deception, coordination, and belief modeling in Large Language Model (LLM)
arXiv:2003.05746v4 Announce Type: replace-cross Abstract: In this paper, we explore the issue of inconsistency handling over prioritized knowledge bases (KBs), which consist of an ontology, a set of f
arXiv:2605.26513v1 Announce Type: new Abstract: Mean Deviation (MD) is a critical metric for assessing visual field loss in ophthalmology. While previous work has focused solely on predicting MD from
arXiv:2601.10566v5 Announce Type: replace Abstract: Entity-level unlearning is usually evaluated by what a model says: whether it stops naming the target, refuses a query, or shifts a Truth Ratio dist
arXiv:2605.26441v1 Announce Type: cross Abstract: This paper addresses the challenging task of weakly-supervised video temporal grounding. Existing approaches are generally based on the moment proposa
arXiv:2605.27299v1 Announce Type: cross Abstract: Modern intrusion detection systems generate thousands of alerts daily, but alert fatigue severely limits security operations effectiveness due to too
arXiv:2601.22476v2 Announce Type: replace-cross Abstract: Floorplanning determines the coordinate and shape of each module in Integrated Circuits. With the scaling of technology nodes, in floorplannin
arXiv:2605.26523v1 Announce Type: cross Abstract: Large-batch Contrastive Learning (CL), the foundation of modern representation learning, is fundamentally incompatible with the volatile resource cons
arXiv:2605.27038v1 Announce Type: new Abstract: Vision-Language Models (VLMs) provide a promising foundation for autonomous driving planning, yet bridging semantic reasoning and precise 3D spatial for
arXiv:2603.15500v2 Announce Type: replace Abstract: LLMs often exhibit Aha moments such as self-correction after tokens like 'Wait,' yet the underlying mechanism remains unclear. Standard LLMs collaps
arXiv:2602.21450v2 Announce Type: replace Abstract: Many robotic systems allow independent control of position and orientation (pose), including omnidirectional aerial vehicles, underwater robots, and
arXiv:2605.26277v1 Announce Type: cross Abstract: Blood vessel segmentation is a core task in medical image analysis for the care of vascular diseases and surgical planning, yet the challenges of prov
arXiv:2605.27313v1 Announce Type: new Abstract: Demographic information is often used to model annotator perspectives in subjective tasks such as hate speech detection, but its benefit is inconsistent
arXiv:2605.26655v1 Announce Type: new Abstract: Automated prompt optimization methods (e.g., DSpy, TextGrad) can substantially improve the performance of large language model (LLM), however, their gen
arXiv:2605.25051v1 Announce Type: new Abstract: Decentralized multi-robot LiDAR-SLAM is essential for collaborative missions but faces significant challenges in maintaining global consistency. Existin
arXiv:2605.25499v1 Announce Type: new Abstract: Importance weighting (IW) is a golden solver for joint distribution shift, where the joint distributions differ between the training and test data. To s
arXiv:2605.23945v1 Announce Type: new Abstract: Reinforcement Learning from Human Feedback (RLHF) has become a key post-training paradigm for improving model quality. However, the synchronous three-st
arXiv:2605.24301v1 Announce Type: new Abstract: Bidirectional thrust grants quadrotors a second equilibrium condition and increased control authority, expanding the envelope of possible aggressive man
arXiv:2605.26072v1 Announce Type: new Abstract: Efficient learning of user preferences is crucial for many modern decision making systems but typically requires costly labeled data. Active learning re
arXiv:2602.13203v2 Announce Type: replace-cross Abstract: Telecommunication networks experience complex failures such as fiber cuts, traffic overloads, and cascading outages. Existing monitoring and d
arXiv:2505.24876v2 Announce Type: replace-cross Abstract: Deep reasoning is fundamental for solving complex tasks, especially in vision-centric scenarios that demand sequential, multimodal understandi
arXiv:2605.23946v1 Announce Type: cross Abstract: Climate volatility, regional production concentration, labor constraints, cyber risk, and dependence on long-distance fresh-produce supply chains expo
arXiv:2605.23978v1 Announce Type: new Abstract: In algorithmic markets, predictive models become part of the data-generating process they aim to forecast. Once their outputs are converted into trades,
arXiv:2605.24266v1 Announce Type: cross Abstract: Recent advances in large language models (LLMs) have enabled deep research systems that synthesize comprehensive, report-style answers to open-ended q
arXiv:2605.25135v1 Announce Type: cross Abstract: Anomaly detection in Industrial Internet of Things (IIoT) environments is essential to protect the Industrial Control Systems (ICS) and Cyber-Physical