Printed from https://fiscalreceipts.com/program/0602181A/ — data as of August 12, 2026. Every figure is citation-backed; see the page online for per-number provenance.
All Domain Convergence Applied Research
Budget Figures
FY2026 award data is a partial year — USASpending awards are reported on a rolling basis and the fiscal year does not close until September 30. why partial FY2026 data? →
| Fiscal year | Amount |
|---|---|
| FY24 | $13.8MR-1 TOA · PB2026 |
| FY25 | $12.3MR-1 TOA · PB2026 |
| FY26 | $0P-40 detail · PB2026 |
The vertical scale does not start at zero: the baseline sits just below this program’s smallest year, so a low point on this line is not a small amount. Read the shape for direction and the grid below for the figures.
● actuals (line) · ○ enacted · ◇ request — gaps are editions the program is absent from, never interpolated.
| Series | FY20 | FY21 | FY22 | FY23 | FY24 | FY25 |
|---|---|---|---|---|---|---|
| Actuals | $0 | $0 | $25.0M | $26.4M | $13.8M | |
| Enacted | – | $0 | $26.0M | $27.4M | $14.3M | $12.3M |
| Request | – | – | $26.0M | $27.4M | $14.3M | $12.3M |
blank = series not published for this year; – = absent from that edition.
Asked vs spent: the PB2023 book requested $27.4M for FY2023; the PB2025 book reported $26.4M as actual total obligation authority — $1.04M below the request. 26.4 − 27.4 = -1.0 USD millions — the compact figures above are rounded for reading.
Program Lineage
No predecessor/successor lineage was recorded for this program element — no FY-to-FY transfer into or out of this line was stated in the ingested J-books, and none was inferred from the program structure.
Description
Mission — All Domain Convergence Applied Research
The Program Element (PE) executes research as part of a campaign of learning to assess feasibility of technologies in an operational environment, learning from early failure and re-scope research to improve speed of response, scalability, interoperability, and range of engagement. This PE will investigate technologies that will enable sensor to shooter applications, from tactical to strategic level, taking a system design approach in support of Army experimentation events and Department of Defense (DoD) Combined Joint All-Domain Command and Control (CJADC2). The research will enable optimal lethal and non-lethal effects across all domains using artificial intelligence and machine learning to improve how we recognize threats, augment and enhance leader decision-making, and replicate tactical behaviors to enable autonomous capabilities. Work in this PE complements PE 0602145A (Next Generation Combat Vehicle Technology), PE 0602146A (Network C3I Technology) and PE 0603463 (Network C3I Advanced Technology). The cited work is consistent with the Under Secretary of Defense for Research and Engineering Priority focus areas and the Army Modernization Strategy. Work in this program is performed by the Army Research Laboratory (ARL).
Mission — Collaborative Convergence Applied Research
This Project supports research required to oppose adversary technologies in the threat based early operational environment. Focus is on those technologies that will aid in reducing the sensors to shooters timelines. This is accomplished using Artificial Intelligence (AI) algorithm decision agent design architectures, advanced methods for processing data, and improved AI performance. Additionally, this Project will research technologies and solutions necessary to enable mission command in multi-domain operations. The project will accelerate emerging research to achieve sensor to shooter dominance. Work in this Project complements Program Element (PE) 0603041A (All Domain Convergence Advanced Technology), PE 0602146A (Network C3I Technology) and PE 0603463 (Network C3I Advanced Technology). The cited work is consistent with the Under Secretary of Defense for Research and Engineering priority focus areas and the Army Modernization Strategy. Work in this Project is performed by the Army Research Laboratory (ARL).
Mission — All Domain Convergence Applied Research
The Program Element (PE) executes research as part of a campaign of learning to assess feasibility of technologies in an operational environment, learning from early failure and re-scope research to improve speed of response, scalability, interoperability, and range of engagement. This PE will investigate technologies that will enable sensor to shooter applications, from tactical to strategic level, taking a system design approach in support of Army experimentation events and Department of Defense (DoD) Combined Joint All-Domain Command and Control (CJADC2). The research will enable optimal lethal and non-lethal effects across all domains using artificial intelligence and machine learning to improve how we recognize threats, augment and enhance leader decision-making, and replicate tactical behaviors to enable autonomous capabilities. Work in this PE complements PE 0602145A (Next Generation Combat Vehicle Technology), PE 0602146A (Network C3I Technology) and PE 0603463 (Network C3I Advanced Technology). The cited work is consistent with the Under Secretary of Defense for Research and Engineering Priority focus areas and the Army Modernization Strategy. Work in this program is performed by the Army Research Laboratory (ARL).
Mission — Collaborative Convergence Applied Research
This Project supports research required to oppose adversary technologies in the threat based early operational environment. Focus is on those technologies that will aid in reducing the sensors to shooters timelines. This is accomplished using Artificial Intelligence (AI) algorithm decision agent design architectures, advanced methods for processing data, and improved AI performance. Additionally, this Project will research technologies and solutions necessary to enable mission command in multi-domain operations. The project will accelerate emerging research to achieve sensor to shooter dominance. Work in this Project complements Program Element (PE) 0603041A (All Domain Convergence Advanced Technology), PE 0602146A (Network C3I Technology) and PE 0603463 (Network C3I Advanced Technology). The cited work is consistent with the Under Secretary of Defense for Research and Engineering priority focus areas and the Army Modernization Strategy. Work in this Project is performed by the Army Research Laboratory (ARL).
Justification
Accomplishments & Planned Programs (6)
AI-Enabled Decision Support in Distributed Networks
This effort research techniques to understand and model complex multi-platform tactical networks in Multi-Domain Operational environments to develop training data sets for AI-enabled tactical decision support capabilities. This effort leverages Army research informed by Army Doctrine on data value, consensus, uncertainty, human-agent teaming and network science to optimize decision support training data production. Supports AI-enabled decision support capabilities for Next Generation Combat Vehicle, Network, Future Vertical Lift, and Long-Range Precision Fires Army Modernization Priorities.
Synthetic Data for AI-Enabled Decision Support
This effort research approaches to incorporate synthetic data to augment Army training data sets and optimize AI performance for uncommon Multi-Domain Operations (MDO) targets and environments. This effort investigates efficacy and optimal application of synthetic training data developed using multiple technical methods, including physics-based models and generative adversarial techniques. This effort will experiment with artificially augmented data sets to enable classification of rare targets and cost-effective enterprise-level training data generation. Supports AI-enabled decision support capabilities for Next Generation Combat Vehicle, Network, Future Vertical Lift, and Long-Range Precision Fires Army Modernization Priorities.
Data Characterization for AI-Enabled Decision Support
This effort will investigate techniques for data management, characterization, curation, labeling, and classification to enable repeatable, robust performance of trained AI-enabled decision support capabilities for complex, multi-platform tactical networks in varied tactical Multi-Domain Operations (MDO) environments. Supports AI-enabled decision support capabilities for Next Generation Combat Vehicle, Network, Future Vertical Lift, and Long-Range Precision Fires Army Modernization Priorities.
AI-Enabled Decision Support in Distributed Networks
This effort research techniques to understand and model complex multi-platform tactical networks in Multi-Domain Operational environments to develop training data sets for AI-enabled tactical decision support capabilities. This effort leverages Army research informed by Army Doctrine on data value, consensus, uncertainty, human-agent teaming and network science to optimize decision support training data production. Supports AI-enabled decision support capabilities for Next Generation Combat Vehicle, Network, Future Vertical Lift, and Long-Range Precision Fires Army Modernization Priorities.
Synthetic Data for AI-Enabled Decision Support
This effort research approaches to incorporate synthetic data to augment Army training data sets and optimize AI performance for uncommon Multi-Domain Operations (MDO) targets and environments. This effort investigates efficacy and optimal application of synthetic training data developed using multiple technical methods, including physics-based models and generative adversarial techniques. This effort will experiment with artificially augmented data sets to enable classification of rare targets and cost-effective enterprise-level training data generation. Supports AI-enabled decision support capabilities for Next Generation Combat Vehicle, Network, Future Vertical Lift, and Long-Range Precision Fires Army Modernization Priorities.
Data Characterization for AI-Enabled Decision Support
This effort will investigate techniques for data management, characterization, curation, labeling, and classification to enable repeatable, robust performance of trained AI-enabled decision support capabilities for complex, multi-platform tactical networks in varied tactical Multi-Domain Operations (MDO) environments. Supports AI-enabled decision support capabilities for Next Generation Combat Vehicle, Network, Future Vertical Lift, and Long-Range Precision Fires Army Modernization Priorities.
Budget Line Items(workbook-cited)
P-1/R-1 workbook Total Obligation Authority basis (USD thousands) · PB2026.
Exhibit R-1
| Account | Org | Type | Amount |
|---|---|---|---|
| Research, Development, Test and Evaluation, Army | A | FY24 Actuals | $13.8M |
| Research, Development, Test and Evaluation, Army | A | FY25 Enacted | $12.3M |
| Research, Development, Test and Evaluation, Army | A | FY25 Total | $12.3M |
Budget Details(R-2/P-40 facts)
J-book detail basis (R-2/P-40, USD millions) · PB2026 — a different accounting basis from the P-1/R-1 workbook TOA above; where the two disagree, the reconciliation strip under Budget Figures shows both.
Wider than this screen — swipe the table sideways for the remaining fiscal-year columns.
| Project | FY24 Actuals | FY25 Total | FY26 Request |
|---|---|---|---|
| Program Element | $13.8M | $12.3M | $0 |
| CM7: Collaborative Convergence Applied Research | $13.8M | $12.3M | — |
No follow-the-dollar view — this program's awards haven't been crosswalked at high confidence (flows cover 17 of 1,741 programs). why coverage is partial? →
Awards
No awards are linked to this program element at high confidence — the budget→award crosswalk only asserts links it can defend, and this line has none yet.
Lobbying Mentions
No Senate LDA lobbying filing in the tracked data mentions this program element by code or alias.
Oversight
Department-level designation (not specific to this program)
GAO lists 5 high-risk areas for DOD as a whole. That designation covers the department, not All Domain Convergence Applied Research — no program-specific GAO finding for this line is in the ingested data. See the DOD oversight record.
No research dossier for this program — dossiers cover 50 of 1,741 programs, the largest fully J-book-detailed lines by FY2026 requested dollars. why no dossier here? →