What project AB7 buys
This project supports the Army Collaborative Research Alliances (CRAs) and Collaborative Technology Alliances (CTAs). CRAs and CTAs are partnerships between Army laboratories and centers, private industry, and academia that focus on the rapid transition of innovative technologies to the Warfighter to enable the Army's Future Force. The collaboration between industry, academia, and the government is a key element of the alliance concept as each member brings with it a distinctly different approach to research. Academia is known for its cutting-edge innovation; the industrial partners are able to leverage existing research results for transition and to deal with technology bottlenecks; and the Army researchers keep the program oriented toward solving complex Army technology problems. This approach enables an Alliance to bring together world class research and development talent and focus it on Army-specific technology objectives for application to Army needs. The topics covered by CRAs and CTAs include cyber security (funded in PE 0601121A / Cyber Collaborative Research Alliance), the internet of battlefield things, distributed and collaborative intelligent systems technology, neuroergonomics and neuroscience, advanced materials, exploitation of quantum effects, semiconductor modeling, convergent manufacturing, autonomous maneuver and machine learning and artificial intelligence. Work in this project is performed by the Army Research Laboratory (ARL).
Project AB7 funding, FY2025–FY2031
Prior years are actuals, the budget year is the request, and the outyears are the FYDP plan. Estimate types are colored and never summed into one figure. Projects carry the full five-year plan; the activities inside them stop at the budget year.
| Fiscal Year | Estimate Type | Amount ($M) |
|---|---|---|
| FY2025 | Actual | 55.5 |
| FY2026 | Enacted | 29.7 |
| FY2027 | Request | 47.9 |
| FY2028 | Outyear | 48.0 |
| FY2029 | Outyear | 48.2 |
| FY2030 | Outyear | 48.8 |
| FY2031 | Outyear | 53.2 |
22 accomplishments / planned programs
The R-2A exhibit — the only level of the budget that describes work that has not happened yet. Activities carry the prior, current and budget year only, no five-year plan. 2 of them describe FY2027 work in enough detail to have their own page; the rest are shown here in full. Coverage is partial across the corpus, so count activities, never total them.
Advanced Energetics Center: Will conduct research on the inclusion of metal particles in energetic material formulations to enhance energy release; explore new techniques for optical and thermal characterization of post-detonation analysis; investigate additive manufacture methods to identify structure-function relationships in…
Read the FY2027 plan →Will explore electrolyte phase behavior for new battery chemistries; investigate the influence of ion dynamics on battery degradation; analyze selective metal deposition for enhanced energy density; study morphological properties that induce increased battery stability; conduct research on the effects of temperature on mechanical…
Read the FY2027 plan →FY2027 planned work Will support three to five HBCU/MI research partnerships each with a duration of five years, selected to enhance existing research under an individual Army CRA, CTA, or Center, and recruit, educate, and train students and post-doctoral researchers in science and technology areas relevant to the Army.
FY2026 to FY2027 change This is not a new start. FY 2027 funding increase reflects the consolidation of other ongoing effort within this project from HBCU/MI Research Partnerships to support the creation of HBCU/MI Outreach. Funding increase reflects additional research in Army relevant problems being conducted by HBCU/MI Research Partners.
FY2025 accomplishments Will conduct research to provide distributed intelligent analytics at scale through cooperative data communication, fusion, and processing; continue research and development of algorithms that prioritize and filter information from vast amounts of ubiquitous heterogenous sensors/actuators; investigate novel methods for joint sensing, computation, and communication in resource-constrained networks; explore trust assessment to maintain safety and security assurances when leveraging uncontrolled sensors/processors for analytics composition; examine the use of hybrid, rule-based plus data-based algorithms for tactical edge processing; research real-time adaptive task scheduling algorithms to…
FY2025 accomplishments Will develop adaptive machine learning models for use in resource-constrained tactical environments to enable distributed analytics; develop improved methods for machine learning model training with limited or unlabeled data; investigate local and global optimization schemes for network and computational resource monitoring, orchestration, and allocation subject to multiple analytical task requests; investigate methodologies for optimizing neural network model development and training for use in a range of Army applications.
FY2026 to FY2027 change Funding decrease reflects realignment to Army Collaborative Research and Technology Alliances within this project.
FY2026 plans — current year Will build towards program capstone by leveraging the common foundation for learning-based multi-robot collaboration to understand scalability and resiliency against simplified Army-relevant scenarios in complex environments; adapt outcomes of research in the science of deception to capstone-inspired problems and environments to understand scalability and performance limitations; develop and refine experimental framework for applying formalisms and planning techniques that utilize advances in artificial intelligence (AI) foundation models for language to bridge multiple program-developed behaviors and AI models into a cohesive mission-planning framework; design and conduct experimentation…
FY2025 accomplishments Will unify joint perception, action, and communication capabilities previously developed to establish the common foundation for learning-based multi-robot collaboration in complex, adversarial environments; expand a new science of deception in autonomous maneuver that links adversarial reasoning, team coordination, autonomous behaviors, learning, and game theory to support operations in complex environments against intelligent and adaptive adversaries; develop formalisms and planning techniques for multi-robot mission specifications that build on natural language reasoning and semantic environment representations and enable adaptive mission execution and scaling to large heterogeneous…
FY2025 accomplishments Will investigate and identify brain areas that allow humans to reason and understand new environments to inform artificial intelligence models and deep neural networks to better recognize and respond to dynamic changes in the environment; conduct experiments to understand the cognitive and neurobiological mechanisms of complex decision making; examine how different brain areas interact and network during the transition from novice to expert in complex task completion.
FY2025 accomplishments Will investigate theory-of-mind approaches to allow for humans and agents to predict each other's capabilities and strategies to improve team performance in tasks that require flexibility and adaptability; identify new methodologies to create agents based on the brain's spatial reasoning and decision making networks and conduct experiments to assess their efficacy in human-machine teams; discover new human-machine team interactions that improve team adaptability in tasks which require super-human capabilities; analyze the impact of new types of machine intelligence such as large language models to enable unprecedented human-technology team interactions; explore ways to enable hybrid…
FY2026 to FY2027 change Funding decrease reflects realignment to Army Collaborative Research and Technology Alliances within this project.
FY2026 plans — current year Will focus on new topic areas, including Large Pre-Trained Models (LPTM) and AI/ML enabled command and control (C2); improve methods for sensor placement, information processing, and semantic models for battlefield information; investigate means of improving communication and collaboration between human and robotic assets in C2 tasks; research challenges of building LPTMs including cognition-inspired world models, use of synthetic data, and programming adherence to ethical guidance; develop LPTM capabilities in processing Army doctrine, knowledge distillation at the edge, holistic experimentation, and establishing practical guardrails.
FY2025 accomplishments Will conduct lab experiments to investigate multi-robot coordination algorithms in Military Operations in Urban Terrain (MOUT) environments; create neural networks for Internet of Battlefield Things (IoBT) edge-network resource management; investigate joint understanding and dialogue interface for improved natural language communications between robots and robot operators; create adaptable command-and-control decision support tools to operationalize new scenarios from original training scenarios; investigate robotic ability to learn and create physics abstractions from real-world interactions in the field.
FY2025 accomplishments Will develop the theoretical understanding and experimental techniques needed to create UWBG devices that exceed state-of-art technology with respect to signal gain, output power density, and power added efficiency; develop an artificial intelligence and machine learning (AI/ML) augmented theoretical framework and experimental validation methodology to enable the design of novel UWBG semiconductor devices intended for millimeter-wave (mm-wave) operation with increased power density.
FY2025 accomplishments Will create a Design-Build-Test-Learn framework comprised of novel computational and high-throughput tools for elucidating sequence-structure-function-property relationships for engineered biological materials; explore the ability of the Design-Build-Test-Learn framework to enable the predictive design of multifunctional biomaterials with differing properties (e.g., electronic, optical, sensing); explore the predictive design of engineered cellular systems by identifying microbial consortia amenable to carrying synthetic functions, rendering them receptive to engineering, designing genetic functions to work in never-before-tried species, and then simulating environments to assess engineered…
FY2026 to FY2027 change Funding decrease reflects realignment to Army Collaborative Research and Technology Alliances within this project.
FY2026 plans — current year Will explore and validate a physics-based reactive burn model for heterogeneous energetic materials to investigate the effects of microstructure and geometry on energetic release; examine novel metal particle synthesis through tunable design of surface reactions that if successful will create architectures that will enable faster energy release; conduct experiments that couple optical diagnostics and analysis methods to study phase-specific particle transport chemical reaction measurements that will inform multi-scale modeling efforts; study novel mixed organic-inorganic formulations to better understand detonation properties and the interactions of detonations with materials; conduct…
FY2025 accomplishments Will explore non-traditional synthetic methodologies to enable novel energetic materials; conduct experiments with new diagnostic and modeling techniques to study the role of microstructure and geometry on energetic release; develop a novel technique that enables in-operando fast response and high resolution imaging of the thermal and reaction front of energetic materials; explore a new aluminum shell chemistry that will promote controllable surface reactions and aluminum energy release rates at time scales relevant to a detonation; create a physics-based reactive burn model to study the reaction dynamics of heterogeneous energetic materials in the weak-to-moderate shock regimes; explore…
FY2025 accomplishments Will investigate methods and techniques that allow small teams of agents to mimic human navigation tactics based on limited training samples; conduct fundamental research on strategies to counter anticipated movement through partial unknown environments; continue to investigate theoretical approaches to gaining tactical advantages with respect to increasingly complex and capable adversarial models.
FY2025 accomplishments Will investigate approaches to rapidly assess samples of nitrogen-vacancy (NV) centers in diamond and other defect systems for use in quantum sensors and clocks; investigate collective effects in nanofiber over 10x longer ranges; develop stable cryogenic ion traps; discover methods to reduce background noise when generating telecom-compatible photons for long-range entanglement.
FY2025 accomplishments Will investigate non-reactive hybrid additive manufacturing processes to study dissimilar metal-metal and metal-ceramic interfaces using novel embedded design technique; validate working convergent manufacturing (CM) platform by converging two manufacturing processes (additive and subtractive) and two dissimilar materials in one manufacturing platform; develop and assess first generation digital twin for CM.
FY2025 accomplishments Will examine the performance of Geiger and linear mode avalanche photodiode arrays; identify mitigation strategies for blinking pixels; develop breakdown simulation models for wide band gap/ultrawide band gap devices.
FY2026 to FY2027 change Funding decrease reflects realignment to HBCU/MI Outreach within this project.
FY2026 plans — current year Will continue to support three to five HBCU/MI research partnerships each with a duration of five years, selected to recruit, educate, and train students and post-doctoral researchers in science and technology areas relevant to the Army and to enhance existing research under an individual Army CRA, CTA, or center.
FY2025 accomplishments Will continue to support three to five HBCU/MI research partnerships selected to enhance existing research under an individual Army CRA, CTA, or center, and recruit, educate, and train students and post-doctoral researchers in science and technology areas relevant to the Army.
FY2026 to FY2027 change Funding decrease reflects realignment to Army Collaborative Research and Technology Alliances within this project.
FY2026 plans — current year Will continue to conduct foundational research through annual, competitively awarded seedling efforts and develop mechanisms to build capabilities at Senior Military Colleges in areas aligned with Army supported major programs and recommendations for policy and strategy in ethics, operations, business, and legal domains for Army modernization.
FY2025 accomplishments Will continue to conduct foundational research through annual, competitively awarded seedling efforts in areas such as photonics, autonomy, power and energy, quantum sensing, cyber operations, materials for hypersonic systems, and recommendations for policy and strategy in ethics, operations, business, and legal domains for Army modernization.
FY2025 accomplishments Will refine models in context of a defined domain (such as, inclusion/exclusion criteria) to characterize fundamental mechanisms of how human-technology relationships drive belief formation; explore how coordinated information presentation at extremely high-rates and multiple sources leads to biases, limitations, and misperceptions in the human's innate systems for aggregating information into judgements.
FY2025 accomplishments Will design a reflexive controls architecture coupled with a highly dynamic morphology to optimize the maneuverability of the platform; explore skeletal-musculature for an autonomous agent capable of exceptional maneuverability; develop control responses via perception and actuation within the platform, and design control architectures for actualizing complex maneuvers to include external perception.
FY2025 accomplishments Will identify most promising physics-informed models and investigate validation methodologies; conduct experiments on natural language processing for data-mining of materials literature; investigate high-throughput synthesis of artificial intelligence-predicted materials; investigate utilization of machine learning to expand sparse data sets; conduct high-throughput experiments on surrogate high-strain rate tests; examine automation and autonomous experimentation; assess integration of a data management platform into Army infrastructure.
FY2026 to FY2027 change Funding decrease reflects realignment to Army Collaborative Research and Technology Alliances within this project.
FY2026 plans — current year Will explore equivalence classes to map complex multi-disciplinary concepts and associated capabilities to families of strategies within a mathematical framework; investigate what-if algorithmic assessment over dynamic sets of decision trees to identify optimal conditions that lead to desired outcomes; examine the combination of algorithmic game theory with machine learning to scale decision-making in a way that is computationally feasible and theoretically justifiable; analyze algorithmic complexity to develop strategies that increase the difficulty of adversarial decision making; identify theoretical principles underlying large-scale deception and counter-deception in adversarial games.