arXiv subjects

Chris Weaver

Publications and source records attributed to Chris Weaver.

3 recordsLinked to original sources

arXiv2026

The Rubin Observatory Target-of-Opportunity System in the First Year of Operations

The NSF/DOE Vera C. Rubin Observatory is a discovery machine, with unprecedented survey speed, which can be used to identify exotic astrophysical transients. In its prime mission, the ten year Legacy Survey of Space and Time will use 3% of its total time for Target of Opportunity observations, which includes response to gravitational wave events, high energy neutrinos, potentially-hazardous asteroids, and other astrophysical phenomena. Target of Opportunity observations exist outside of the usual LSST operational mode, requiring special attention to maximize performance. We review the Rubin Target of Opportunity system during its first year of Rubin Observatory operations, the Targets of Opportunity pursued since LSST first light, and the overall efficiency of the system.

astro-ph.IM↗

arXiv2026

EnterpriseRAG-Bench: A RAG Benchmark for Company Internal Knowledge

Retrieval-Augmented Generation (RAG) has become the standard approach for grounding large language models in information that was not available during training. While existing datasets and benchmarks focus on web or other public sources, there is still no widely adopted dataset that realistically reflects the nature of company-internal knowledge. Meanwhile, startups, enterprises, and researchers are increasingly developing AI Agents designed to operate over exactly this kind of proprietary data. To close this gap, we release a synthetic enterprise corpus, its generation framework, and a leaderboard. We present EnterpriseRAG-Bench, a dataset consisting of approximately 500,000 documents spanning nine enterprise source types (Slack, Gmail, Linear, Google Drive, HubSpot, Fireflies, GitHub, Jira, and Confluence) and 500 questions across ten categories that test distinct retrieval and reasoning capabilities. The corpus is generated with cross-document coherence (grounded in shared projects, people, and initiatives) and augmented with realistic noise such as misfiled documents, near-duplicates, and conflicting information. The question set ranges from simple single-document lookups to multi-document reasoning, constrained retrieval, conflict resolution, and recognizing when information is absent. The generation framework lets teams generate variants tailored to their own industry, scale, and source mix. The dataset, code, evaluation harness, and leaderboard are available at https://github.com/onyx-dot-app/EnterpriseRAG-Bench.

cs.IR↗

arXiv2025

Animated Visual Encoding and Layer Blending for Identification of Educational Game Strategies

Game-Based Learning has proven to be an effective method for enhancing engagement with educational material. However, gaining a deeper understanding of player strategies remains challenging. Sequential game-state and action-based tracking tools often gather extensive data that can be difficult to interpret as long-term strategy. This data presents unique problems to visualization, as it can be fairly natural, noisy data but is constrained within synthetic, controlled environments, leading to issues such as overplotting which can make interpretation complicated. We propose an animated visual encoding tool that utilizes kinetic visualization to address these issues. This tool enables researchers to construct animated data narratives through the configuration of parameter interpolation curves and blending layers. Finally, we demonstrate the usefulness of the tool while addressing specific interests as outlined by a domain expert collaborator.

cs.HC↗