What the FY2027 request buys
Verbatim from the R-2A exhibit for project 1501 of PE 0601153N. This is the budget justification's own description of work that has not happened yet — the one thing no other level of the budget carries.
- Continue research exploring integration of domain knowledge and physical models with machine learning for fast, robust learning of diverse complex concepts and tasks without the need for labeled data. - Continue research efforts regarding the use of artificial intelligence to advance the scientific understanding of AI-machine collaborative decision-making typical of Naval command decision making. - Continue research of formal verification and validation methods for artificial intelligence in the Naval domain to enhance trust. - Continue research investigations exploring explainable and verifiable artificial intelligence to enhance human-machine collaboration. - Continue research exploring new brain-inspired artificial intelligence algorithms and architecture to provide rich computational capabilities, with an emphasis on analog/digital hybrid systems, local core memory and multimodal sensing. - Continue research investigations of hardware designs based on brain models that are suitable for low power opto-electronic computation and signal processing in small Naval platforms. - Continue research efforts regarding autonomous problem solving and curiosity-driven search for solutions to enable robust performance under unexpected conditions. - Continue research efforts exploring composable computational models of vision-language interactions for intelligent agents that can learn and reason about the real world with high levels of complexity. - Continue both exploratory and confirmatory research with future naval application in Artificial Intelligence research area. - Continue research efforts into the use of Artificial Intelligence (incorporating, but not limited to Intelligent Agents, Cognitive Science, Robotics, Autonomy, Machine Learning (ML), and Multi-agent control planes/protocols) to explore the viability of future distributed autonomous unmanned systems and swarms. These research efforts will include present and future theoretical endeavors in the Cognitive S&T for the deployment of autonomous/interactive intelligent agents compatible with Allies and mission partners. This research area will address foundational theory in computational cognitive modeling for warfighter collaboration, S&T supporting deployment of adaptive autonomy, instructible autonomous systems, resilient agent-control to maintain desired states for deployed distributed autonomous unmanned swarms - including counter/disrupt/combat enemy swarms, and integrated, multi-agent (heterogeneous, human in the loop) autonomous systems. - Continue efforts into High Assurance Computing aimed at the theoretical underpinnings of assurance of deep neural networks, autonomous software-defined networking (SDN) orchestration, automation for reverse engineering and vulnerability analysis, Game-theoretic AI for defenses against cyber-attacks, adversarial threat modeling, privacy-enhancing technologies, covert and encrypted communications, field-programmable gate array (FPGA) based hardware assurance, automatic generation of Correct-by-Construction code. This endeavor also includes ground breaking basic S&T in intelligent agents for autonomic cyber maneuver, scaling and automation of algorithms for vulnerability discovery, mitigation, and weaponization, techniques for rapid verification of customized and/or reprogrammed versions of software/hardware as well as scalable cryptographic computing, cyber-ISR capabilities for an increasingly balkanized Internet, minimalism and composability in systems engineering and design, and integrated cyber-physical vulnerability analysis.
Funding increase due to increase of naval research into U.S. Navy AI research that is a critical strategic imperative required to outpace global competitors and secure maritime dominance. This investment will directly enhance warfighting effectiveness by scaling autonomous operations, achieving decision superiority through rapid data analysis, and maximizing fleet readiness with predictive maintenance and logistics. Mastering these AI-driven capabilities is essential for building a more lethal, resilient, and intelligent naval force prepared for future conflicts.
FY2026: the year under way
Prior-year accomplishments and current-year plans from the same exhibit. Context for the FY2027 plan, not a series — an activity partitions its project exactly in the request year, but can under-cover it in earlier years.
- Continue research exploring integration of domain knowledge and physical models with machine learning for fast, robust learning of diverse complex concepts and tasks without the need for labeled data. - Continue research efforts regarding the use of artificial intelligence to advance the scientific understanding of AI-machine collaborative decision-making typical of naval command decision making. - Continue research of formal verification and validation methods for artificial intelligence in the naval domain to enhance trust. - Continue research investigations exploring explainable and verifiable artificial intelligence to enhance human-machine collaboration. - Continue research exploring new brain-inspired artificial intelligence algorithms and architecture to provide rich computational capabilities, with an emphasis on analog/digital hybrid systems, local core memory and multimodal sensing. - Continue research investigations of hardware designs based on brain models that are suitable for low power opto-electronic computation and signal processing in small naval platforms. - Continue research efforts regarding autonomous problem solving and curiosity-driven search for solutions to enable robust performance under unexpected conditions. - Continue research efforts exploring composable computational models of vision-language interactions for intelligent agents that can learn and reason about the real world with high levels of complexity. - Continue research efforts into the use of Artificial Intelligence (incorporating, but not limited to Intelligent Agents, Cognitive Science, Robotics, Autonomy, Machine Learning (ML), and Multi-agent control planes/protocols) to explore the viability of future distributed autonomous unmanned systems and swarms. These research efforts will include present and future theoretical endeavors in the Cognitive S&T for the deployment of autonomous/interactive intelligent agents compatible with Allies and mission partners. This research area will address foundational theory in computational cognitive modeling for warfighter collaboration, S&T supporting deployment of adaptive autonomy, instructible autonomous systems, resilient agent-control to maintain desired states for deployed distributed autonomous unmanned swarms - including counter/disrupt/combat enemy swarms, and integrated, multi-agent (heterogeneous, human in the loop) autonomous systems. - Continue efforts into High Assurance Computing aimed at the theoretical underpinnings of assurance of deep neural networks, autonomous software-defined networking (SDN) orchestration, automation for reverse engineering and vulnerability analysis, Game-theoretic AI for defenses against cyber-attacks, adversarial threat modeling, privacy-enhancing technologies, covert and encrypted communications, field-programmable gate array (FPGA) based hardware assurance, automatic generation of Correct-by-Construction code. This endeavor also includes ground breaking basic S&T in intelligent agents for autonomic cyber maneuver, scaling and automation of algorithms for vulnerability discovery, mitigation, and weaponization, techniques for rapid verification of customized and/or reprogrammed versions of software/hardware as well as scalable cryptographic computing, cyber-ISR capabilities for an increasingly balkanized Internet, minimalism and composability in systems engineering and design, and integrated cyber-physical vulnerability analysis. - Continue both exploratory and confirmatory research with future naval application in Artificial Intelligence research area (realigned from project unit 1099).
Three years, and no five-year plan
An R-2A activity publishes the prior year, the current year and the budget year. The FYDP outyears exist at project and program-element level and are deliberately absent here rather than inferred. Estimate types are colored and never summed into one figure.
| Fiscal Year | Estimate Type | Amount ($M) |
|---|---|---|
| FY2025 | Actual | 0.0 |
| FY2026 | Enacted | 2.0 |
| FY2027 | Request | 13.4 |
This activity is 48% of project 1501's FY2027 request and 2.6% of PE 0601153N's. In the request year the activities under a project sum to it exactly; in the current year they under-cover it in about 9% of cases, so an activity's delta can legitimately exceed its parent's and the two must not be compared row to row.
4 activities in project 1501
Every R-2A line of this project, largest FY2027 request first. Linked where the activity has enough of its own narrative to carry a page; the rest are shown in full on the project page.