ONHS Report: FA875023S7006

ARTIFICIAL INTELLIGENCE AND NEXT GENERATION DISTRIBUTED COMMAND AND CONTROL

Solicitation ID: FA875023S7006

Agency: Department of the Air Force/AFRL/Information Directorate

Type: Presolicitation

Deadline: 2028-08-30T16:00:00 ET

Eligibility: Maybe (Dual Approval Required)

Funding: Total funding of approximately $99M. Individual awards will normally range from $200K to $20M.

Doc Type: BAA

Clearance: TS/SCI

Program Manager: andrew.porter.13@us.af.mil

Last Updated: 2025-11-24 13:11

Analyzed File: 23-06 Amend 10 second repub and add TA9.docx

Eligibility Reasoning

Federally Funded Research and Development Centers (FFRDCs) and Government entities... are subject to applicable direct competition limitations and cannot propose to this BAA in any capacity unless they meet the following conditions: FFRDCs must clearly demonstrate that the proposed work is not otherwise available from the private sector; and FFRDCs must provide a letter on official letterhead from their sponsoring organization citing the specific authority establishing their eligibility to propose to Government solicitations and compete with industry... FFRDC and Government entity eligibility will be determined on a case-by-case basis.

Note: This is an AI-generated summary...

Summary

This Broad Agency Announcement (BAA FA8750-23-S-7006) from the Air Force Research Laboratory (AFRL) seeks to accelerate research and development of novel Artificial Intelligence (AI) and distributed Command and Control (C2) capabilities to support the Department of the Air Force's Joint All Domain C2 (JADC2) objectives. With a total estimated funding of $99 million through FY28, the BAA is open until August 30, 2028, utilizing a two-step process where interested parties first submit a white paper and may be invited to submit a full proposal. Individual awards are expected to range from $200K to $20M over periods up to 48 months. The BAA outlines nine technical areas (TAs) of interest: (1) Command & Control of AI Systems for mission-tailored AI adaptation and deployment; (2) a Federated, Composable Autonomy & AI Toolbox for interoperability; (3) Advanced Wargaming Agents for reusable strategic simulation; (4) Interactive Learning for human-in-the-loop C4I problem solving; (5) C2 Complexity Dominance to impose complexity on adversaries; (6) Generative AI applications for C4I; (7) Software Defined Distributed C2 for orchestrating nodes in contested environments; (8) Tactical AI for distributed signal detection using the TAK ecosystem; and (9) Mission Planning for Autonomous Collaborative Platforms (ACPs) to enable autonomous re-tasking in denied environments.

Overall Technical Areas

AI Model Lifecycle ManagementAI Drift Detection and MitigationFederated Machine LearningAutonomous Mission PlanningGenerative AI for C4IAdvanced Wargaming AgentsInteractive Machine LearningDistributed C2 OrchestrationDecentralized Task AllocationTactical Edge AIDistributed Signal GeolocationAdversarial Decision ModelingComposable AI ArchitecturesReinforcement Learning Algorithms

Probable LBNL Areas

BSACSAEESAESAETALDPSA

Focus Areas

Example Priorities:

The resultant 'Battle Management of AI' will enable operators to reason through a complex space of competing design factors and mission dependencies to select and deploy AI components that are compatible with mission tactics and evolving battlefield conditions.
This process to detect shifts in performance of AI-based systems and adapt models for new environments is analogous to traditional battle management during conflict, where assets are provisioned and dynamically revectored to prosecute new targets in short order.
Develop and testing toolkits to enable third party development of AI/ML components and interoperability across a diverse set of AI/ML components, and workflow engine to facilitate orchestration, coordination, and composition of AI/ML components to form novel pipelines based on mission need.
AI Monitoring – enables plug-and-play integration of drift detection algorithms and live performance feedback (i.e., ground truth) from operators.
CRUD for AI – APIs to control reads, updates, and deletion of AI models onboard weapons platforms from ground stations.

Specific Technical Skills:

MLOps / DevSecOpsAI Model ManagementPerformance MonitoringAI Drift DetectionModel Adaptation / Fine-TuningWorkflow OrchestrationAPI DesignFederated SystemsMetadata ManagementCommon Operating Picture (COP)

Potential PIs for this Area:

Name	Score	Organization
Gazi Mahmud	0.70	BSA \| Environmental Genomics & Systems Biology \| Molecular Ecosystems Bio
Sufi Kaur	0.70	CSA \| SciData \| Data Science Applications
Oluwamayowa Amusat	0.69	CSA \| SciData \| Data Science Applications
Shreyas Cholia	0.69	CSA \| SciData \| Data Science Applications
Jean Luca Bez	0.69	CSA \| SciData \| Data Science Research
Xiaoya Chong	0.69	ESA \| ALS \| PS Computing staff
Hannah Cohoon	0.68	CSA \| SciData \| Data Science Applications
Danielle Svehla Christianson	0.68	CSA \| SciData \| Data Science Applications
Vinay Sawal	0.68	CSA \| NERSC \| Data Center Department
Ana Kupresanin	0.68	CSA \| Computing

Example Priorities:

The C2 Complexity Dominance technical area is focused on research to exploit operational complexity... to impose complexity on both human and AI agents on the adversary’s side, in order to ultimately shape the adversary’s decisions and actions.
Interactive Learning (IL) is a data efficient ML approach that uses human in or on the loop to quickly train a model to make inferences.
This technology is focused more on learning agents such as reinforcement learning or genetic algorithms but can expand to other types of algorithms such as Monte Carlo Tree Search.
Utilize unstructured data by exploiting latent embedding space learned by latest Generative Transformer architectures (ChatGPT).
Assess: Train GPT style models to aggregate IL>6 information to allow objective conditional probabilities (in contrast to probabilities of token occurrence) to be extracted from unstructured data.

Specific Technical Skills:

Reinforcement LearningGenerative AI / LLMsInteractive Machine LearningHuman-in-the-Loop SystemsWargaming & SimulationAgent-Based ModelingGame TheoryNatural Language ProcessingCourse of Action (COA) GenerationTemporal Causal Logic

Potential PIs for this Area:

Name	Score	Organization
Haitam Laarabi	0.69	ETA \| Energy Analysis & Environmental Impacts \| Sustainable Energy Dept
Gazi Mahmud	0.69	BSA \| Environmental Genomics & Systems Biology \| Molecular Ecosystems Bio
Shawn Tornga	0.68	LD \| LD \| ONHS
Zhe Bai	0.68	CSA \| AMCR \| Computer Science
Oluwamayowa Amusat	0.68	CSA \| SciData \| Data Science Applications
Neel Rajeshbhai Vora	0.68	PSA \| ATAP \| BACI
Alexander Sim	0.68	CSA \| SciData \| Data Science Research
Kunxiaojia (Tammy) Yuan	0.68	EESA \| CESD \| Earth System Sciences
Marcos Lopez de Prado	0.68	CSA \| SciData \| Data Science Research
Sunhee Baik	0.68	ETA \| Energy Analysis & Environmental Impacts \| Energy Markets & Policy

Example Priorities:

The Software Defined Distributed C2 technical area is focused on research to optimize and orchestrate distributed C2 nodes with the associates C2 processes, functions, and resources that are distributed across the theater.
We refer to a software system that is capable of on-board, autonomous re-tasking of a team of ACPs as an Autonomous Aerial Mission Manager (A2M2).
The decentralized re-tasking will be accomplished via P2P messaging between ACPs. The contested environment in which ACPs will operate will likely introduce communication latencies and imperfect information.
Develop, demonstrate, and assess the scalability of efficient command and control protocols and intelligent central control processes to enable distributed signal detection and geolocation.
Approaches should consider factors such as client sensing duty cycle, proximity to other sensors, status of nearby devices, battery, performance optimizations, threat levels/posture, and reporting schedules.

Specific Technical Skills:

Distributed SystemsAutonomous Systems C2Decentralized AlgorithmsMission PlanningResource OrchestrationPeer-to-Peer (P2P) CommunicationResilient ArchitecturesTactical Edge ComputingAgile Combat Employment (ACE)Contested Environments

Potential PIs for this Area:

Name	Score	Organization
Vinay Sawal	0.70	CSA \| NERSC \| Data Center Department
Khaled Ibrahim	0.69	CSA \| AMCR \| Computer Science
Anand Krishnan Prakash	0.69	ETA \| Building Technology & Urban Systems \| Bldg Technologies Dept
Georgios Michelogiannakis	0.69	CSA \| AMCR \| Computer Science
Dan Bonachea	0.68	CSA \| AMCR \| Computer Science
Sudip Dosanjh	0.68	CSA \| Computing \| National Energy Research Scientific Computing Center (NERSC)
Alexander Sim	0.68	CSA \| SciData \| Data Science Research
Sean Peisert	0.68	CSA \| SciData \| Data Science Applications
Aydin Buluc	0.67	CSA \| AMCR \| Computer Science
Samuel Williams	0.67	CSA \| AMCR \| Computer Science

Potential LBNL PIs (Overall)

Note on PI Matching: These suggestions are generated through an AI-driven semantic analysis of LBNL staff profiles.

Name	Score	Organization
Prabal Dutta	0.72	ETA \| Building Tech. & Urban Sys. \| Bldg Technologies Dept · Distributed Signal Geolocation
Alexandre Bayen	0.72	ETA \| Energy Analysis \| Systems & Energy Tech Analysis · Adversarial Decision Modeling
Anna Giannakou	0.72	CSA \| SciData \| Data Science Applications · Tactical Edge AI
Cees De Laat	0.72	CSA \| ESNET \| Tech Adv and Engagement · Decentralized Task Allocation
Gazi Mahmud	0.72	BSA \| Env. Genomics & Sys. Biology \| Molecular Ecosystems Bio · AI Model Lifecycle Management
Tiffany Connors	0.72	CSA \| NERSC \| Data Center Department · Distributed C2 Orchestration
Suren Byna	0.72	CSA \| SciData \| Data Science Research · Federated Machine Learning
Talita Perciano Costa Leite	0.71	CSA \| SciData \| Data Science Research · Reinforcement Learning Algorithms
Houjun Tang	0.71	CSA \| SciData \| Data Science Applications · Advanced Wargaming Agents
Claire Tomlin	0.71	BSA \| Biological Systems & Engineering \| Department of BioEngineering and BioMedical Sciences · Autonomous Mission Planning

Fundamental Research Exemption (FRE)

Any employee who is not a U.S. citizen or a permanent resident will be restricted from working on any resultant contract unless prior approval of the Department of State or the Department of Commerce is obtained via a technical assistance agreement or an export license.