Knowledge catalogue
You can now /schedule one-time tasks from the CLI or the Routines UI via the Schedule -> Once Trigger. Use it to: * /schedule cleanup this feature flag in 1 week */schedule give me a launch report for
arXiv:2604.19584v1 Announce Type: new Abstract: The growing interest of Natural Language Processing (NLP) in minority languages has not yet bridged the gap in the preservation of oral linguistic herit
arXiv:2603.22885v4 Announce Type: replace Abstract: BACKGROUND: General aviation fleet expansion demands intelligent health monitoring under computational constraints. Real-world aircraft health diagn
Cursor, an AI-powered code editor, offers integration with Slack through an API endpoint that allows users to install a Slack app for enhanced collaboration. This integration likely enables developers
arXiv:2604.08404v2 Announce Type: replace Abstract: Out-of-distribution (OoD) generalization occurs when representation learning encounters a distribution shift. This occurs frequently in practice whe
arXiv:2604.18576v2 Announce Type: replace Abstract: We present BLF (Bayesian Linguistic Forecaster), an agentic system for binary forecasting that achieves state-of-the-art performance on the Forecast
arXiv:2604.18805v1 Announce Type: new Abstract: Large language model (LLM)-based systems are increasingly deployed to conduct scientific research autonomously, yet whether their reasoning adheres to t
arXiv:2604.19685v1 Announce Type: new Abstract: Answering open-ended questions remains challenging for AI systems because it requires synthesis, judgment, and exploration beyond factual retrieval, and
arXiv:2604.19747v1 Announce Type: new Abstract: Sparse-view 3D reconstruction is essential for modeling scenes from casual captures, but remain challenging for non-generative reconstruction. Existing
This Databricks article examines whether Large Language Model agents can effectively optimize SQL join order, a critical query optimization problem in database systems. The analysis likely evaluates L
arXiv:2604.18616v1 Announce Type: cross Abstract: LLM-based coding agents can generate functionally correct GPU kernels, yet their performance remains far below hand-optimized libraries on critical co
arXiv:2604.19204v1 Announce Type: cross Abstract: LLMs are increasingly being considered for prediction tasks in high-stakes social service settings, but their algorithmic fairness properties in this
arXiv:2604.15034v2 Announce Type: replace Abstract: Recent advances in LLM based agent systems have shown promise in tackling complex, long horizon tasks. However, existing agent protocols (e.g., A2A
been using claude design and loving it it's basically claude code under the hood but with a very opinionated ontology for input and output - the input ontology is logo, colors, fonts, etc. - the outpu
arXiv:2509.26238v4 Announce Type: replace Abstract: Monitoring large language models' (LLMs) activations is an effective way to detect harmful requests before they lead to unsafe outputs. However, tra
arXiv:2604.19281v1 Announce Type: cross Abstract: The use of Large Language Models (LLMs) to support patients in addressing medical questions is becoming increasingly prevalent. However, most of the m
arXiv:2604.19394v1 Announce Type: new Abstract: This paper narrows the performance gap between small, specialized models and significantly larger general-purpose models through domain adaptation via c
arXiv:2604.18883v1 Announce Type: cross Abstract: Current AI-assisted programming tools are predominantly linear and chat-based, which deviates from the iterative and branching nature of programming i
arXiv:2604.19741v1 Announce Type: new Abstract: We address the problem of generating a 3D-consistent, navigable environment that is spatially grounded: a simulation of a real location. Existing video
Claude Code won a Webby! Thanks to all of our users and customers for the support and love, and for always giving us feedback so that we can continue making the product a little better every day. http
arXiv:2604.19211v1 Announce Type: new Abstract: Current AI agent frameworks have made remarkable progress in automating individual tasks, yet all existing systems serve a single user. Human productivi
arXiv:2508.14170v2 Announce Type: replace Abstract: The increasing deployment of large language models (LLMs) in natural language processing (NLP) tasks raises concerns about energy efficiency and sus
arXiv:2604.18587v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated significant potential in formal theorem proving, yet state-of-the-art performance often necessitates pr
arXiv:2603.16091v2 Announce Type: replace-cross Abstract: In factual question answering, many errors are not failures of access but failures of commitment: the system retrieves relevant evidence, yet
arXiv:2604.19262v1 Announce Type: cross Abstract: Large language models (LLMs) are now deployed worldwide, inspiring a surge of benchmarks that measure their multilingual and multicultural abilities.
arXiv:2604.18745v1 Announce Type: new Abstract: Automated segmentation of structural defects from visual inspection imagery remains challenging due to the diversity of damage types, extreme class imba
arXiv:2604.19124v1 Announce Type: new Abstract: Existing detoxification methods for large language models mainly focus on post-training stage or inference time, while few tackle the source of toxicity
arXiv:2604.19566v1 Announce Type: cross Abstract: Reliable biomedical and clinical retrieval requires more than strong ranking performance: it requires a practical way to find systematic model failure
arXiv:2604.19135v1 Announce Type: new Abstract: This paper presents the first exploration of text-to-image diffusion models for zero-shot sketch-based 3D shape retrieval (ZS-SBSR). Existing sketch-bas
arXiv:2604.19264v1 Announce Type: new Abstract: Agentic multimodal models have garnered significant attention for their ability to leverage external tools to tackle complex tasks. However, it is obser
arXiv:2604.18660v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used in education, yet their default helpfulness often conflicts with pedagogical principles. Prior work
arXiv:2604.18953v1 Announce Type: new Abstract: Deep learning surrogates for CFD flow-field prediction often rely on large, complex models, which can be slow and fragile when data are noisy or incompl
arXiv:2604.19171v1 Announce Type: new Abstract: Heterogeneous graphs have attracted increasing attention for modeling multi-typed entities and relations in complex real-world systems. Multi-label node
arXiv:2503.16683v2 Announce Type: replace-cross Abstract: Vision Transformer (ViT) has been widely used in computer vision tasks with excellent results by providing representations for a whole image o
arXiv:2604.18933v1 Announce Type: cross Abstract: Robotic manipulation tasks exhibit varying memory requirements, ranging from Markovian tasks that require no memory to non-Markovian tasks that depend
arXiv:2604.19345v1 Announce Type: new Abstract: This paper investigates the intrinsic geometrical features of highly similar objects and introduces a general self-supervised framework called the Geome
This post likely references a humorous or unexpected use case involving a rear brake lever, possibly demonstrating an unintended design flaw, unconventional application, or double meaning related to b
Abner Li / 9to5Google: Google says Meet's Gemini-powered Take Notes for Me feature can now be used for in-person meetings and adds support for Teams and Zoom — Besides Workspace Intelligence, Google a
arXiv:2604.18907v1 Announce Type: cross Abstract: A central challenge in program induction has long been the trade-off between symbolic and neural approaches. Symbolic methods offer compositional gene
arXiv:2604.18973v1 Announce Type: cross Abstract: Air pollution is a worldwide public health threat that can cause or exacerbate many illnesses, including respiratory disease, cardiovascular disease,
arXiv:2604.19274v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as co-authors in collaborative writing, where users begin with rough drafts and rely on LLMs to compl
arXiv:2604.19669v1 Announce Type: new Abstract: Enforcing constraint satisfaction in neural network outputs is critical for safety, reliability, and physical fidelity in many control and decision-maki
arXiv:2604.19189v1 Announce Type: new Abstract: For news headlines to influence beliefs and drive action, relevant information needs to be retained and retrievable from memory. In this probing study w
arXiv:2604.18866v1 Announce Type: new Abstract: Despite advances in object detection, aerial imagery remains a challenging domain, as models often fail to generalize across variations in spatial resol
arXiv:2604.18874v1 Announce Type: new Abstract: Tool-integrated agents are deployed on the premise that external tools ground their outputs in reality. Yet this very reliance creates a critical attack
Conversational analytics, as demonstrated by Databricks' Genie and Lakebase, helps CIOs, CTOs, and CDOs bypass the traditional Business Intelligence (BI) bottleneck. This approach allows enterprises t
arXiv:2604.18847v1 Announce Type: new Abstract: As LM agents gain the ability to execute actions on real computer systems, we need ways to not only prevent harmful actions at scale but also effectivel
arXiv:2604.19673v1 Announce Type: new Abstract: Training embodied agents to understand 3D scenes as humans do requires large-scale data of people meaningfully interacting with diverse environments, ye
arXiv:2509.21080v2 Announce Type: replace-cross Abstract: Advancements in Large language models (LLMs) have enabled a variety of downstream applications like story and interview script generation. How
arXiv:2604.19538v1 Announce Type: new Abstract: Agentic AI, with goal-directed, proactive, and autonomous decision-making capabilities, offers a compelling opportunity to address movement-related risk
arXiv:2602.06430v2 Announce Type: replace-cross Abstract: In the field of natural language processing, some studies have attempted sentiment analysis on text by handling emotions as explanatory or res
arXiv:2601.11632v2 Announce Type: replace Abstract: Multi-modal Large Language Models (MLLMs) for Visual Question Answering (VQA) often suffer from dual limitations: knowledge hallucination and insuff
arXiv:2604.19301v1 Announce Type: new Abstract: The conformity bias exhibited by large language models (LLMs) can pose a significant challenge to decision-making in LLM-based multi-agent systems (LLM-
LiteParse: our open-source, layout-aware PDF parser for AI agents. The secret? Grid projection. Instead of heavy ML layout models or flat text extraction, it projects text onto a monospace grid so ali
arXiv:2604.19117v1 Announce Type: new Abstract: When a language model agrees with a user's false belief, is it failing to detect the error, or noticing and agreeing anyway? We show the latter. Across
arXiv:2602.01219v4 Announce Type: replace-cross Abstract: The attention operator in Transformers can be viewed as a two-layer fast-weight MLP, whose weights are dynamically instantiated from input tok
arXiv:2604.19670v1 Announce Type: cross Abstract: Effective human-robot teaming is crucial for the practical deployment of robots in human workspaces. However, optimizing joint human-robot plans remai
arXiv:2604.19093v1 Announce Type: cross Abstract: Multi-modal test-time adaptation (TTA) enhances the resilience of benchmark multi-modal models against distribution shifts by leveraging the unlabeled
arXiv:2603.11665v2 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) have been widely adopted as MLLM-as-a-Judges due to their strong alignment with human judgment across vario
arXiv:2604.19022v1 Announce Type: new Abstract: A major challenge for niche scientific and technical domains in leveraging coding agents is the lack of access to up-to-date, domain- specific knowledge