mlsanityData to 14 Oct 2026

Advancing AI Challenges for the United States Department of the Air Force

arXiv 2511.00267 ↗First posted 31 Oct 2025cs.AI cs.CY cs.GLcited by 1

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Abstract (arXiv)

The DAF-MIT AI Accelerator is a collaboration between the United States Department of the Air Force (DAF) and the Massachusetts Institute of Technology (MIT). This program pioneers fundamental advances in artificial intelligence (AI) to expand the competitive advantage of the United States in the defense and civilian sectors. In recent years, AI Accelerator projects have developed and launched public challenge problems aimed at advancing AI research in priority areas. Hallmarks of AI Accelerator challenges include large, publicly available, and AI-ready datasets to stimulate open-source solutions and engage the wider academic and private sector AI ecosystem. This article supplements our previous publication, which introduced AI Accelerator challenges. We provide an update on how ongoing and new challenges have successfully contributed to AI research and applications of AI technologies.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 20 of its 58 references that are in the map. First posted on arXiv 31 Oct 2025.

Builds on

20 of 58 references.

References · 20

  1. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,718
  2. 2024Jamba: A Hybrid Transformer-Mamba Language Modelcited by 401
  3. 2023From Words to Watts: Benchmarking the Energy Costs of Large Language Model Inferencecited by 170
  4. 2018Interactive Supercomputing on 40,000 Cores for Machine Learning and Data Analysiscited by 110
  5. 2019FlightGoggles: A Modular Framework for Photorealistic Camera, Exteroceptive Sensor, and Dynamics Simulationcited by 22
  6. 2023Clover: Toward Sustainable AI with Carbon-Aware Machine Learning Inference Servicecited by 17
  7. 2023Toward Sustainable HPC: Carbon Footprint Estimation and Environmental Implications of HPC Systemscited by 12
  8. 2024Sustainable Supercomputing for AI: GPU Power Capping at HPC Scalecited by 9
  9. 2025L2M: Mutual Information Scaling Law for Long-Context Language Modelingcited by 9
  10. 2023Score-based Source Separation with Applications to Digital Communication Signalscited by 8
  11. 2022Exploiting Temporal Structures of Cyclostationary Signals for Data-Driven Single-Channel Source Separationcited by 7
  12. 2024RF Challenge: The Data-Driven Radio Frequency Signal Separation Challengecited by 6
8 more
  1. 2018GraphChallenge.org: Raising the Bar on Graph Analytic Performancecited by 3
  2. 2023On Neural Architectures for Deep Learning-based Source Separation of Co-Channel OFDM Signalscited by 3
  3. 2022Developing a Series of AI Challenges for the United States Department of the Air Forcecited by 2
  4. 2023Lincoln AI Computing Survey (LAICS) Updatecited by 2
  5. 2024A Benchmark Dataset for Tornado Detection and Prediction using Full-Resolution Polarimetric Weather Radar Datacited by 2
  6. 2024Anonymized Network Sensing Graph Challengecited by 2
  7. 2015Computing on Masked Data to improve the Security of Big Datacited by 1
  8. 2024Extracting TCPIP Headers at High Speed for the Anonymized Network Traffic Graph Challengecited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Applied AI-Enhanced RF Interference Rejectioncited by 1