Knowledge catalogue
arXiv:2605.17681v1 Announce Type: new Abstract: Humanoid and legged robots interact with the environment through intermittent contacts, making accurate motion estimation fundamentally dependent on rea
arXiv:2510.18941v2 Announce Type: replace-cross Abstract: Evaluating progress in large language models (LLMs) is often constrained by the challenge of verifying responses, limiting assessments to task
arXiv:2605.18333v1 Announce Type: cross Abstract: Accurate and efficient time-series forecasting remains a challenging problem for both classical and quantum neural architectures, particularly in mult
arXiv:2509.13270v2 Announce Type: replace-cross Abstract: We introduce RadGame, an AI-powered gamified platform for radiology education that targets two core skills: localizing findings and generating
arXiv:2605.18423v1 Announce Type: new Abstract: As autonomous systems grow more advanced, objective metrics to evaluate their ethical and legal compliance are critical for informing end users of their
arXiv:2605.16651v1 Announce Type: new Abstract: Explanation mechanisms are increasingly used to support transparency and trust in vision-language models (VLMs), particularly in settings where model de
arXiv:2605.17526v1 Announce Type: cross Abstract: As autonomous coding agents become capable of handling increasingly long-horizon tasks, they have gradually demonstrated the potential to complete end
arXiv:2510.08702v2 Announce Type: replace Abstract: Code Large Language Models (LLMs) are revolutionizing software engineering. However, scaling laws that guide the efficient training are predominantl
arXiv:2412.18158v2 Announce Type: replace Abstract: Learned image compression methods have shown impressive performance but are often highly specialized for either human perception or specific machine
arXiv:2605.17341v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) have achieved remarkable success, yet their reliance on massive datasets and unintended memorization of training data ra
arXiv:2510.16416v4 Announce Type: replace-cross Abstract: Vision-language models (VLMs) have shown remarkable abilities by integrating large language models with visual inputs. However, they often fai
arXiv:2605.17834v1 Announce Type: new Abstract: Diffusion models exhibit remarkable generative capability, but their high latency limits practical deployment. Many studies have attempted to reduce sam
arXiv:2511.19320v2 Announce Type: replace Abstract: Preserving first-frame identity while ensuring precise motion control is a fundamental challenge in human image animation. The Image-to-Motion Bindi
arXiv:2506.05442v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) offer a promising approach to end-to-end autonomous driving due to their human-like reasoning capabilities. Howe
arXiv:2605.18251v1 Announce Type: cross Abstract: Self-initiated attention shifts play a critical role in voluntary behavior but are difficult to study due to the absence of explicit temporal markers.
arXiv:2511.19953v2 Announce Type: replace Abstract: Accurate nuclear instance segmentation is a pivotal task in computational pathology, supporting data-driven clinical insights and facilitating downs
arXiv:2605.17946v1 Announce Type: new Abstract: Multimodal large language models are increasingly used as agent backbones that understand multimodal inputs, plan retrieval actions, invoke external too
arXiv:2605.16530v1 Announce Type: new Abstract: Realistic surgical simulation plays a crucial role in training novice surgeons and in the development of autonomous agents. World models can scale such
arXiv:2602.16473v2 Announce Type: replace Abstract: C-RASP is a simple programming language that was recently shown to capture concepts expressible by transformers. In this paper, we develop new algor
arXiv:2502.20969v4 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) extends large language models (LLMs) with external data sources to enhance factual correctness and domain
arXiv:2605.16895v1 Announce Type: cross Abstract: End-to-end LLM trading agents have moved quickly from research curiosity to a small ecosystem of named systems, including FinCon, FinMem, TradingAgent
arXiv:2605.18063v1 Announce Type: new Abstract: Object counting is a foundational vision task with over a decade of dedicated research, yet state-of-the-art models still fail systematically in the mix
The Build with Gemini XPRIZE is a competition offering $2 million in total prizes over a 90-day period where participants select a meaningful problem and develop a profitable AI-powered business solut
arXiv:2605.18058v1 Announce Type: new Abstract: Arabic handwriting recognition (AHR) has made significant progress with deep learning models. AHR research has largely focused on performance, with secu
arXiv:2411.18234v2 Announce Type: replace-cross Abstract: Cardiovascular diseases (CVDs) are any serious illness of the heart, which require accurate diagnosis to prevent fatal consequences. Hyperpara
Autonomous security operations company Torq Ltd. today announced that it has acquired Jit.io Ltd., a cybersecurity startup whose technology generates AI-driven context graphs to inform security invest
arXiv:2512.11089v4 Announce Type: replace-cross Abstract: We introduce test prediction variance (TPV)--the first-order sensitivity of a trained model's outputs to parameter perturbations--as a unifyin
arXiv:2605.16735v1 Announce Type: cross Abstract: The deployment of 5G Multicast-Broadcast Services (MBS) is emerging as a critical technology for spectral-efficient UHD content delivery and serving a
arXiv:2509.23986v2 Announce Type: replace Abstract: Scientific discovery is often slowed by the manual development of computational tools needed to analyze complex experimental data. Building such too
arXiv:2605.16690v1 Announce Type: new Abstract: Heterogeneous LoRA-rank methods address system heterogeneity in federated fine-tuning of foundation models by assigning client-specific ranks based on c
arXiv:2605.16815v1 Announce Type: cross Abstract: Graph neural networks (GNNs) have achieved remarkable success in relational learning. However, their vulnerability to graph backdoor attacks (GBAs) po
arXiv:2602.22801v2 Announce Type: replace-cross Abstract: Diffusion models have become a popular choice for decision-making tasks in robotics, and more recently, are also being considered for solving
arXiv:2605.17748v1 Announce Type: new Abstract: In the field of Blind Image Quality Assessment (BIQA), accurately predicting the perceptual quality of authentically distorted images remains highly cha
arXiv:2510.02590v2 Announce Type: replace Abstract: The use of target networks is a popular approach for estimating value functions in deep Reinforcement Learning (RL). While effective, the target net
arXiv:2602.10989v2 Announce Type: replace-cross Abstract: We construct and analyze generative diffusions that transport a point mass to a prescribed target distribution over a finite time horizon usin
This video discusses the architectural need for a continuity layer in long-running AI agents, explaining how agents require persistent memory and state management mechanisms to maintain coherence acro
arXiv:2510.16252v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) for web agents demands environments that are both effective for evaluation and efficient enough for large-scale on
arXiv:2605.18460v1 Announce Type: new Abstract: This work presents a novel variant of the Firefly Algorithm (FA) for data clustering, addressing limitations of traditional methods like K-Means that st
arXiv:2509.17680v2 Announce Type: replace Abstract: Table question answering (TableQA) is a fundamental task in natural language processing (NLP). The strong reasoning capabilities of large language m
arXiv:2601.16398v2 Announce Type: replace-cross Abstract: Algorithmic audits are essential tools for examining systems for properties required by regulators or desired by operators. Current audits of
arXiv:2605.17912v1 Announce Type: cross Abstract: World models have emerged as a central paradigm for embodied intelligence, enabling agents to predict action-conditioned future and reason about envir
arXiv:2605.15475v1 Announce Type: new Abstract: We introduce an empowered transposed Fully Connected Weighted (t-FCW) graph representation to embed point clouds into a metric space. While original t-F
arXiv:2605.16153v1 Announce Type: new Abstract: This paper provides an algebraic exposition of the theory of dyadic morality (TDM), a psychological model of moral judgment grounded in a simple two-nod
arXiv:2605.15281v1 Announce Type: cross Abstract: Modern web test suites rot. A UI refactor breaks locators, a timing change causes race conditions, and within weeks developers abandon the suite entir
arXiv:2507.16806v2 Announce Type: replace-cross Abstract: When language models (LMs) are trained via reinforcement learning (RL) to generate natural language 'reasoning chains', their performance impr
arXiv:2605.15984v1 Announce Type: cross Abstract: Toxic speech detection has become a crucial challenge in maintaining safe online communication environments. However, existing approaches to toxic spe
arXiv:2601.21357v2 Announce Type: replace Abstract: Bayesian Optimization (BO) is a principled framework for optimizing expensive black-box functions, with Expected Improvement (EI) among its most wid
arXiv:2605.15734v1 Announce Type: new Abstract: The use of large language models to assess user states in conversational and adaptive systems is based on the assumption that the metrics used for such
arXiv:2605.16089v1 Announce Type: cross Abstract: Federated Learning (FL) has emerged as a promising paradigm for collaborative model training across distributed edge devices while preserving data pri
arXiv:2605.15999v1 Announce Type: new Abstract: This paper introduces a motion planning framework to plan morphology and trajectory for morphing quadrotors under extremely constrained environments. We
arXiv:2605.15904v1 Announce Type: new Abstract: Cybersecurity Knowledge Graphs (CKGs) unify diverse Cyber Threat Intelligence (CTI) sources into structured, queryable formats, offering scalable soluti
arXiv:2605.15549v1 Announce Type: cross Abstract: The demand for clean energy is ever increasing, with new nuclear technologies presenting a complementary solution to renewable energies. However, desi
arXiv:2605.15254v1 Announce Type: new Abstract: Physics-Informed Neural Networks (PINNs) combine deep learning with physical constraints for solving partial differential equations (PDEs), and are wide
arXiv:2505.06982v3 Announce Type: replace Abstract: Accurate and privacy-preserving diagnosis of ophthalmic diseases remains a critical challenge in medical imaging, particularly given the limitations
arXiv:2410.01990v3 Announce Type: replace Abstract: This paper explores alternative formulations of the Kolmogorov Superposition Theorem (KST) as a foundation for neural network design. The original K
arXiv:2503.00794v2 Announce Type: replace Abstract: Accurate gait event detection is crucial for gait analysis, rehabilitation, and assistive technology, particularly in exoskeleton control, where pre
arXiv:2605.15518v1 Announce Type: new Abstract: The effective detection and governance of Large Language Model (LLM) generated content has become increasingly critical due to the growing risk of misus
arXiv:2601.00361v3 Announce Type: replace-cross Abstract: We introduce the first iterative algorithm for constructing a arepsilon-coreset that guarantees deterministic ell_p subspace embedding for any
arXiv:2605.15368v1 Announce Type: new Abstract: Group-convolutional neural networks (GCNNs) are among the most important methods for introducing symmetry as an inductive bias in deep learning: In each
arXiv:2605.15477v1 Announce Type: new Abstract: Egocentric world models present a promising direction for enabling agents to predict and plan, but their performance is constrained by the limited avail