Knowledge catalogue
arXiv:2605.13037v1 Announce Type: new Abstract: Current interactive LLM agents rely on goal-conditioned stepwise planning, where environmental understanding is acquired reactively during execution rat
arXiv:2605.13527v1 Announce Type: new Abstract: Reusable skills have become a core substrate for improving agent capabilities, yet most existing skill packages encode reusable behavior primarily as te
arXiv:2605.13058v1 Announce Type: new Abstract: Wheeled-legged robots hold promise for traversing complex terrains and offer superior mobility compared to legged robots. However, wheeled-legged robots
Natively-agentic is just one of the perks. The overall experience is a step change from anything out there. And then you add the focus to details, user experience, and how supreme the team behind this
arXiv:2605.13086v1 Announce Type: new Abstract: This paper presents an object manipulation strategy for the Variable Topology Truss (VTT) system, a truss robot that comprises actuated truss members co
arXiv:2605.13025v1 Announce Type: new Abstract: We study the problem of learning Nash equilibria in offline two-player zero-sum Markov games. While existing approaches often rely on explicit pessimism
arXiv:2605.12566v1 Announce Type: cross Abstract: The rapid development of low-altitude economy has driven the proliferation of Unmanned Aerial Vehicle (UAV) applications, including logistics, inspect
arXiv:2510.21060v3 Announce Type: replace-cross Abstract: Policy optimization (PO) is a cornerstone of modern reinforcement learning (RL), with diverse applications spanning robotics, healthcare, and
AMD confirmed FSR 4 upscaling for RDNA 3 in July 2026 and RDNA 2 in early 2027 , addressing long-standing gamer frustration over the feature's initial exclusivity to newer RDNA 4 cards. AMD's team had
arXiv:2605.12541v1 Announce Type: cross Abstract: Electrocardiography (ECG) is the clinical standard for cardiac assessment but requires dedicated hardware that does not scale to daily-life monitoring
arXiv:2605.13583v1 Announce Type: new Abstract: Recent advances have demonstrated that coded aperture snapshot spectral imaging (CASSI) systems show great potential for capturing 3D hyperspectral imag
arXiv:2605.13579v1 Announce Type: new Abstract: Assistive agents for Blind and Visually Impaired (BVI) users require accessibility alignment as a first-class design objective. Despite rapid progress i
arXiv:2507.22095v5 Announce Type: replace-cross Abstract: We consider fully connected and feedforward deep neural networks with dependent and possibly heavy-tailed weights, as introduced in [26], to a
arXiv:2508.14950v2 Announce Type: replace-cross Abstract: 4D Flow Magnetic Resonance Imaging (4D Flow MRI) enables non-invasive quantification of blood flow and hemodynamic parameters. However, its cl
arXiv:2605.12504v1 Announce Type: cross Abstract: We develop conjectures and theorems expressing the idea that the prime sequence exhibits computational irreducibility in the transition from one prime
arXiv:2510.13385v3 Announce Type: replace Abstract: Although both data availability and the demand for accurate forecasts are increasing, collaboration between stakeholders is often constrained by dat
arXiv:2605.12981v1 Announce Type: cross Abstract: Automated program synthesis has reduced the cost of producing candidate implementations, but it introduces a harder governance problem: determining wh
arXiv:2605.12567v1 Announce Type: cross Abstract: The inherent electronic and speckle noise complicates clinical interpretation of ultrasound images. Conventional denoising methods rely on explicit no
arXiv:2511.17855v2 Announce Type: replace Abstract: Robots must learn from both what people do and what they say, but either modality alone is often incomplete: physical corrections are grounded but a
arXiv:2507.15867v2 Announce Type: replace-cross Abstract: Rare diseases affect 1 in 10 Americans yet remain systematically underdocumented in clinical records. ICD-based systems cannot capture their b
arXiv:2510.19471v2 Announce Type: replace-cross Abstract: Recent work has shown that sample-based Minimum Bayes Risk (MBR) decoding outperforms beam search in text-to-text generation tasks, such as ma
arXiv:2605.12511v1 Announce Type: cross Abstract: Social media serves as a primary source of information in the current digital era. Many people consume a vast range of information in a very short spa
Artificial intelligence sovereignty is a growing topic of discussion as governments and enterprises increasingly see AI as strategic infrastructure with economic, security, intellectual property and o
arXiv:2605.13031v1 Announce Type: cross Abstract: This paper addresses the problem of estimating the relative pose (position and orientation) and velocity of a vehicle with respect to a moving target,
arXiv:2603.20527v3 Announce Type: replace Abstract: Preconditioned adaptive methods have gained significant attention for training deep neural networks, as they capture rich curvature information of t
arXiv:2605.13730v1 Announce Type: cross Abstract: Transthoracic echocardiography (TTE) is the first-line imaging modality for diagnosing bicuspid aortic valve (BAV), yet diagnostic performance varies
arXiv:2506.00982v3 Announce Type: replace Abstract: Deep multi-agent reinforcement learning (MARL) has been demonstrated effectively in simulations for multi-robot problems. For autonomous vehicles, t
This article presents Sea's perspective on how agentic software development—systems that can autonomously plan and execute coding tasks—will evolve with tools like Codex, OpenAI's code generation mode
arXiv:2605.11505v2 Announce Type: replace Abstract: Reinforcement learning with verifiable rewards helps reasoning, but GRPO-style methods stall on hard prompts where all sampled rollouts fail. SORT a
arXiv:2510.12837v3 Announce Type: replace-cross Abstract: Cultural evolution allows ideas and technologies to accumulate across generations, reaching their most complex and open-ended form in humans.
Theo Wayt / The Information: Sources: 50+ researchers and engineers have left xAI since the SpaceX acquisition via layoffs, firings, and voluntary departures; many have joined Meta and TML — Call it t
arXiv:2605.12513v1 Announce Type: cross Abstract: Influence maximization (IM) in real platforms is challenged by incomplete, noisy social graphs and non-stationary diffusion dynamics. We propose SP-GC
arXiv:2605.13079v1 Announce Type: cross Abstract: Muon orthogonalizes the momentum buffer before each update, replacing its singular values with ones via Newton-Schulz iterations. This simple change l
arXiv:2605.13181v1 Announce Type: cross Abstract: Precipitation nowcasting remains challenging due to the highly localized, rapidly evolving, and heterogeneous nature of atmospheric dynamics. Although
arXiv:2605.12743v1 Announce Type: cross Abstract: Existing physical adversarial attacks on vision-based autonomous driving induce time-evolving perception errors, including biased object tracking or t
arXiv:2511.09771v3 Announce Type: replace Abstract: Accurate 6D pose estimation and tracking are core capabilities for physical AI systems, yet real-world deployment remains brittle and labor-intensiv
arXiv:2605.13153v1 Announce Type: new Abstract: Temporal Knowledge Graph Reasoning (TKGR) aims at inferring missing (especially future) events from historical data. Current evaluation in TKGR uniforml
arXiv:2605.13312v1 Announce Type: new Abstract: We present Supervised Deep Multimodal Matrix Factorization (SD3MF), an interpretable framework for integrative brain network analysis that generalizes S
arXiv:2601.17326v2 Announce Type: replace Abstract: Retinal prostheses restore limited visual perception, but low spatial resolution and temporal persistence make reading difficult. In sequential lett
arXiv:2506.12075v2 Announce Type: replace-cross Abstract: High school English Literature teachers often encounter barriers to assembling diverse, thematically aligned text sets due to limited planning
arXiv:2605.13316v1 Announce Type: new Abstract: Action diffusion excels at high-fidelity action generation but incurs heavy computational costs owing to its iterative denoising nature. Despite current
arXiv:2511.16868v2 Announce Type: replace Abstract: The Gromov-Wasserstein (GW) distance serves as a powerful tool for matching objects in metric spaces. However, its traditional formulation is constr
arXiv:2605.12547v1 Announce Type: cross Abstract: Public procurement is vulnerable to error, fraud and corruption, yet high transaction volumes overwhelm oversight. While research often focuses on ten
this is an early beta. we still have much to improve, which is why its so exciting for people to start trying. the goal is for Grok Build to be the best coding agent when it comes to solving real and
arXiv:2605.13748v1 Announce Type: new Abstract: Semidefinite programming (SDP) provides a principled framework for convex relaxations of nonconvex geometric constraints in motion planning, yet existin
arXiv:2605.12584v1 Announce Type: cross Abstract: Recently, multimodal graph learning (MGL) has garnered significant attention for integrating diverse modality information and structured context to su
arXiv:2605.13401v1 Announce Type: new Abstract: We propose a data augmentation method for offline reinforcement learning, motivated by active positioning problems. Particularly, our approach enables t
Try this early Grok Build (anything) beta and let us know what to improve. Much appreciated! An early beta of Grok Build, an agentic CLI for coding, building apps, and automating workflows is now avai
v0.30.0-rc16 is a pre-release version of Ollama that changes the architecture to directly support llama.cpp instead of building on top of GGML, and allows for compatibility with GGUF file format. MLX
v0.30.0-rc17 is a pre-release version of Ollama that changes the architecture to directly support llama.cpp instead of building on top of GGML and allows compatibility with GGUF file format, with MLX
arXiv:2605.12571v1 Announce Type: cross Abstract: Long video question answering requires locating sparse, time-scattered visual evidence within highly redundant content. Although current MLLMs perform
arXiv:2605.12745v1 Announce Type: cross Abstract: Discrepancies between an agent's actual knowledge and what a person thinks the agent knows can hinder interactions. If an agent could detect such disc
arXiv:2605.13032v1 Announce Type: new Abstract: Graph neural networks are widely used for node classification, but they remain vulnerable to out-of-distribution (OOD) shifts in node features and graph
arXiv:2605.13328v1 Announce Type: cross Abstract: Vision-and-Language Navigation (VLN) is a cornerstone of embodied intelligence. However, current agents often suffer from significant performance degr
arXiv:2605.12947v1 Announce Type: cross Abstract: LLM-enabled AI workflows increasingly produce outputs through iterative generate-evaluate-revise loops. Each iteration can improve the candidate, but
arXiv:2511.13658v2 Announce Type: replace-cross Abstract: Deceptive reviews mislead consumers, harm businesses, and undermine trust in online marketplaces. Machine learning classifiers can learn from
arXiv:2605.12543v1 Announce Type: cross Abstract: Some conscious contents disappear after access; others return repeatedly, long after their triggering conditions have ceased. We propose Canxianizatio
arXiv:2605.12770v1 Announce Type: cross Abstract: We introduce WriteSAE, the first sparse autoencoder that decomposes and edits the matrix cache write of state-space and hybrid recurrent language mode
arXiv:2605.13465v1 Announce Type: new Abstract: Recent advances in 3D Gaussian Splatting (3DGS) have enabled significant progress in photorealistic novel view synthesis. However, traditional 3DGS reli
AI21 Labs discusses four common agent optimization methods used to balance accuracy against cost and latency constraints. The post indicates the team evaluated all four approaches, likely covering tec