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cs.IR updates on arXiv.org

From Top-1 to Top-K: A Reproducibility Study and Benchmarking of Counterfactual Explanations for Recommender Systems Impact of large language models on peer review opinions from a fine-grained perspective: Evidence from top conference proceedings in AI Diagnosable ColBERT: Debugging Late-Interaction Retrieval Models Using a Learned Latent Space as Reference Enhancing Unsupervised Keyword Extraction in Academic Papers through Integrating Highlights with Abstract CAST: Modeling Semantic-Level Transitions for Complementary-Aware Sequential Recommendation IndiaFinBench: An Evaluation Benchmark for Large Language Model Performance on Indian Financial Regulatory Text Think Before Writing: Feature-Level Multi-Objective Optimization for Generative Citation Visibility RARE: Redundancy-Aware Retrieval Evaluation Framework for High-Similarity Corpora Personalized Benchmarking: Evaluating LLMs by Individual Preferences Modular Representation Compression: Adapting LLMs for Efficient and Effective Recommendations JFinTEB: Japanese Financial Text Embedding Benchmark UsefulBench: Towards Decision-Useful Information as a Target for Information Retrieval SIMMER: Cross-Modal Food Image--Recipe Retrieval via MLLM-Based Embedding Rethinking the Necessity of Adaptive Retrieval-Augmented Generation through the Lens of Adaptive Listwise Ranking BioHiCL: Hierarchical Multi-Label Contrastive Learning for Biomedical Retrieval with MeSH Labels Learning Behaviorally Grounded Item Embeddings via Personalized Temporal Contexts Collaborative Filtering Through Weighted Similarities of User and Item Embeddings IG-Search: Step-Level Information Gain Rewards for Search-Augmented Reasoning Metric-agnostic Learning-to-Rank via Boosting and Rank Approximation GenRec: A Preference-Oriented Generative Framework for Large-Scale Recommendation Uncertainty-aware Generative Learning Path Recommendation with Cognition-Adaptive Diffusion CPGRec+: A Balance-oriented Framework for Personalized Video Game Recommendations Don't Retrieve, Navigate: Distilling Enterprise Knowledge into Navigable Agent Skills for QA and RAG NewsTorch: A PyTorch-based Toolkit for Learner-oriented News Recommendation Controlling Authority Retrieval: A Missing Retrieval Objective for Authority-Governed Knowledge APEX-MEM: Agentic Semi-Structured Memory with Temporal Reasoning for Long-Term Conversational AI ID and Graph View Contrastive Learning with Multi-View Attention Fusion for Sequential Recommendation Large Language Models to Enhance Business Process Modeling: Past, Present, and Future Trends Dual-Enhancement Product Bundling: Bridging Interactive Graph and Large Language Model Evaluation of Agents under Simulated AI Marketplace Dynamics
An Interactive Metrics Dashboard for the Keck Observatory...
G. Bruce Berriman, Min Phone Myat Zaw · 2026-02-10 · via cs.IR updates on arXiv.org

Since 2004, the Keck Observatory Archive (KOA) has operated as a NASA-funded collaboration between the NASA Exoplanet Science Institute ( NExScI) and the W.M. Keck Observatory. It ingests and serves all data acquired by the twin 10-meter Keck telescopes on Mauna Kea, Hawaii. In the past three years, KOA has begun a modernization program to replace the architecture and systems used since the archive's creation with a new modern Python-based infrastructure. This infrastructure will position KOA to respond to the rapid growth of new and complex data sets that will be acquired by new instruments now in development, and enable follow-up to identify the deluge of alerts of transient sources expected by new survey telescopes such as the Vera C. Rubin Observatory. Since 2022, KOA has ingested new data in near-real time, generally within one minute of creation, and has made them immediately accessible to observers through a dedicated web interface. The archive is now deploying a new, scalable, Python-based, VO-compliant query infrastructure built with the Plotly-Dash framework and R-tree indices to speed-up queries by a factor of 20. The project described here exploits the new query infrastructure to develop a dashboard that will return live metrics on the performance and growth of the archive. These metrics assess the current health of the archive and guide planning future hardware and software upgrades. This single dashboard will enable, for example, monitoring of real-time ingestion, as well as studying the long-term growth of the archive. Current methods of gathering metrics that have been in place since the archive opened will not support the archive as it continues to scale. These methods suffer from high latency, are not optimized for on-demand metrics, are scattered among various tools, and are cumbersome to use.