CJF CRA BAA 2024-08-12 0800 amendment 01.pdf

Version: 2024-08-12 0800

Distribution Statement A: Approved for public release; distribution is unlimited

ARMY RESEARCH LABORATORY

BROAD AGENCY ANNOUNCEMENT

FOR

Collective Judgment Formation Collaborative Research Alliance

Fiscal Year 2024

W911NF-24-S-0010

Amendment 01

This amendment provides notification of change in event dates for proposal submission

ISSUED BY:

U.S. Army Contracting Command

Aberdeen Proving Ground Research Triangle Park Division

P. O. Box 12211 Research Triangle Park, NC 27709-2211

Issued: 14 Aug 2024 White papers Due:

25 September 2024, no later than 3:00 PM Eastern Daylight Time Final Proposals Due:

21 November 2024 06 December 2024, no later than 3:00 PM Eastern Daylight Time

(This page intentionally left blank)

Collective Judgment Formation - Collaborative Research Alliance

(CJF CRA)

Broad Agency Announcement

Table of Contents

I. Overview of the Funding Opportunity

A. REQUIRED OVERVIEW CONTENT

1. Federal Awarding Agency Name

2. Research Opportunity Title

3. Announcement Type

4. Funding Opportunity Number

5. Catalog of Federal Domestic Assistance (CFDA) Number(s)

6. CJF CRA Website

7. Submission of Questions

8. Key Dates

9. Proposers Day Details

II. Detailed Information about the Funding Opportunity

A. PROGRAM DESCRIPTION

1. Purpose

2. Background

3. Collective Judgment Formation Definitions

4. Motivating Research Questions

5. Program Structure

B. TECHNICAL AREAS

1. TA1: Social Science Theories for Human-Agent Systems for Intelligence Activities (Cooperative Agreement; CUI)

2. TA2: Computational Models for Human-Agent Systems for Intelligence Activities (Cooperative Agreement; CUI)

3. TA3: Testing, Evaluation, and Transitions for Human-Agent Systems for Intelligence Activities (Contract; Cleared Facility; TS/SCI eligible)

4. Collaboration

5. Associate Performer Agreement

6. Program Intent

C. FEDERAL AWARD INFORMATION

1. Anticipated Funding

2. Funding

3. Profit/Fee

4. Cost Sharing

5. Award Instrument

D. ELIGIBILITY INFORMATION

1. Eligible Applicants

2. CUI and TS/SCI Performers

3. Cost Sharing or Matching

4. Other

E. WHITE PAPER SUBMISSION INFORMATION

1. Overview

2. Format and Content of White papers

3. Restrictive Markings on white papers

4. Evaluation and Disposition of white papers

5. White paper Submission

F. PROPOSAL APPLICATION AND SUBMISSION INFORMATION

1. Address to View Broad Agency Announcement

2. Content and Form of Application Submission

3. Unique Entity Identifier and System for Award Management (SAM)

4. Submission Dates and Times

5. Intergovernmental Review

6. Funding Restrictions

7. Other Submission Requirements

G. PROPOSAL REVIEW INFORMATION

1. Criteria

2. Review and Selection Process

H. AWARD ADMINISTRATION INFORMATION

1. Award Notices

2. Administrative and National Policy Requirements

3. Reporting

I. SUBMISSION DATES AND TIMES

J. AGENCY CONTACTS

K. HUMAN SUBJECTS RESEARCH (HSR)

L. ASSOCIATE PERFORMER AGREEMENT (APA)

I. Overview of the Funding Opportunity

A. REQUIRED OVERVIEW CONTENT

1. Federal Awarding Agency Name

Combat Capabilities Development Command (DEVCOM) U.S. Army Research Laboratory 2800 Powder Mill Road Adelphi, MD 20783-1197

Issuing Acquisition Office:

U.S. Army Contracting Command – Aberdeen Proving Ground, Research Triangle Park (RTP) Division 800 Park Office Drive Suite #4229 Research Triangle Park, NC 27709

2. Research Opportunity Title

Collective Judgment Formation Collaborative Research Alliance (CJF CRA)

3. Announcement Type

Initial

4. Funding Opportunity Number

W911NF-24-S-0010

5. Catalog of Federal Domestic Assistance (CFDA) Number(s)

12.630 - "Basic, Applied, and Advanced Research in Science and Engineering"

6. CJF CRA Website https://arl.devcom.army.mil/cras/cjf-cra (website pending activation at time of announcement posting)

7. Submission of Questions cjf-cra-baa@army.mil

8. Key Dates

The following is a summary of the events and dates associated with this CJF CRA Broad Agency Announcement (BAA):

EVENT ESTIMATED DATE/TIMEFRAME

Opportunity released 14 Aug 2024 Proposers Day (virtual) 04 Sep 2024, 1100 EDT Deadline for Questions on BAA 10 Sep 2024 White Papers Due 25 Sep 2024, 1500 EDT White Paper Feedback 18 Oct 2024 Deadline for Questions on Proposals 30 Oct 2024 07 Nov 2024, 1500 EST Proposals Due 21 Nov 2024 06 Dec 2024, 1500 EST Notification to Recipients 19 Dec 2024 31 Jan 2025 (expected) Award Execution Feb 2025 March 2025 (expected)

9. Proposers Day Details

The Collective Judgment Formation Proposers Day will be held virtually on Microsoft Teams. The event will take place on 04 Sep 2024 from 1100 to 1300 EDT. Any changes to the date or time will be posted on grants.gov and SAM.gov. Registration is required to attend.

To register, please send an email to cjf-cra-baa@army.mil with the Subject: “CJF Proposers Day Registration”. Include in your email the full name, university/organization, and email address of each person who will participate. Each named participant will receive an email with the link to attend the live event.

Questions about this BAA can be submitted in advance of Proposers Day. Please send an email to cjf-cra-baa@army.mil with the Subject: “CJF Proposer Question”. Questions will not receive individual email responses. Rather, these questions will be answered during the meeting and posted to the Q&A section on grants.gov and SAM.gov.

Participants may be able to submit questions during the event via the chat feature of Teams.

(End of section)

Collective Judgment Formation Collaborative Research Alliance

(CJF CRA)

II. Detailed Information about the Funding Opportunity

A. PROGRAM DESCRIPTION

1. Purpose

The U.S. Army Combat Capabilities Development Command (DEVCOM) Army Research Laboratory (ARL) is focused on researching fundamental understanding and informing the art-of-the-possible for warfighter concepts through research to greatly improve the Army’s capabilities in the Information Operating Environment (IOE) to gather and process multi-modal, multi-sourced information; convert that information into deep understanding; and ultimately leverage that knowledge for strategic advantage.

The Collective Judgment Formation (CJF) Collaborative Research Alliance (CRA) program is focused on basic research to create and expand theoretical and scientific understanding of human-agent interactions in AI-supported team decision making. The representative domain for the program is intelligence activities. Work within this program will require the problem space to be addressed from multiple disciplinary perspectives working in concert to discover novel knowledge and to advance our scientific foundations of (a) humans working with each other and with novel forms of software agents and (b) teams of intelligence analysts responding to novel methods that create verbose structured and unstructured content. Through this CRA, ARL seeks to partner with performers to advance the scientific state-of-the-art in human-agent systems for intelligence activities.

Beyond helping the Army, the research outcomes from this foundational, collaborative research program are intended to inform the public with regard to human-agent systems opportunities for information processing and to offer a perspective for understanding of and protecting against misinformation campaigns. Research conducted in the CJF CRA is also applicable to challenges in academia and industry.

2. Background

The Army, and more broadly the Department of Defense (DoD) and the Intelligence Community (IC), rely heavily on deriving knowledge for effective decision making from information that originates from multiple sources. The challenges for processing the high volumes of information are becoming exponentially more difficult with the expanding capabilities of generative artificial intelligence technologies, yet the need for high confidence assessments remains steadfast. During information processing tasks, the formation of judgments based on information from a wide variety of sources by analysts (working alone and in teams) is influenced by (a) other people in their networks, (b) the available information they consume, (c) potential interactions with virtual agents, and (d) their own internal predispositions based on background, training, values, and external communities in which they are embedded. These analysts’ capabilities and performance on such tasks can be expanded and augmented through intelligent systems or information agents leveraging technologies such as Large Language Models (LLMs), and through additional information gained from social media and social networking platforms. However, such human-agent teaming approaches can lead to the emergence of new biases and challenges surrounding the reliability of such judgments made to accept/reject each piece of information being processed. These phenomena reside at the conscious and subconscious levels, further exacerbating the challenges to understand their impact on judgment formation, especially when software agents are folded into the team.

3. Collective Judgment Formation Definitions

A judgment in the context of this document is a proposition that summarizes a body of evidence and is believed to be true or false with some known estimated confidence level. A proposition may be a simple important fact or relationship, or instead a complex hierarchy of propositions at different levels of abstraction. Judgment formation is the process by which the proposition is reached. This process encompasses individual cognitive processing by human analysts; exchange of information and discussion in teams of analysts; processing of information by computer systems; and the interactions between any or all of these entities. Finally, a judgment is collective in being a team accepted outcome of this process, notably including any governing rules, procedures, or conventions.

For example, a collective judgment may represent total consensus reached at the end of a shift as to which information is most critical; it might instead be the judgment of the team lead alone, drawing on information unavailable to everyone else, whereby the structured roles of the team come into play. Likewise, for agent driven information processing, judgments could be the selection of which information within a large corpus is presented to analysts for further consideration and processing. The algorithms developed would exemplify the formation process. Then, human analysts would reach a collective judgment through their own work and the process by which they integrate agent provided information.

Collective judgment formation is a multi-scale, complex process that needs to be understood at the component level, and from a systemic perspective. Without an understanding of collective judgment formation, a team may produce interpretations that do not account for individual biases or that arise from interpersonal relationships; for information item limitations; for unrecognized connections between disparate information; for processes for data collection, validation, and sharing that do not match domain or task constraints; and so forth.

4. Motivating Research Questions

With the above background in mind, the following questions (derived from DEVCOM ARL RFI-24-01-HC) may help potential proposers to understand the types of scientific and technical challenges the CJF CRA program seeks to address.

5. Program Structure

To address the human-agent systems for intelligence activities (HAS-IA) problem space, the CJF program will consist of three technical areas (TAs):

• TA1: Social Science Theories for HAS-IA

• TA2: Computational Models for HAS-IA

• TA3: Testing, Evaluation, and Transitions for HAS-IA

Details for each technical area are provided in the next section. Each TA team is expected to draw performers from multiple disciplines such as psychology, communication, business, economics, computer science, electrical engineering, physics, and mathematics. TA1, Social Science for HAS-IA, will require a team led by a social scientist, with a focus on social science approaches to the problems space; the team must contain at least one computer scientist to support integration with TA2 and TA3. TA2, Computational Models for HAS-IA, will require a team led by a computer scientist, with a focus on computational modeling approaches to the problem space; the team must contain at least one social scientist to support integration with TA1 and TA3.

Each proposal may address only a single TA. Proposers may submit multiple proposals if they seek to address multiple TA, but not multiple proposals to the same TA. A

1. What types of cognitive biases are involved in judgment formation when information technologies such as LLMs, social media, and social networking platforms are involved? What new cognitive biases might emerge?

2. How might human-agent interactions influence the development of individual beliefs and knowledge systems? How might agent-facilitated human interactions influence the transfer of beliefs and knowledge from one individual or team to another?

3. How do the five Vs of big data (volume, value, variety, velocity, and veracity), plus uncertainty and deception, affect judgment formation? And how is collective judgment formation affected by differences in the modalities of information presentation (e.g., free-form text, instructions, tables, maps, diagrams, audio, and video) in neurodiverse teams?

4. Intelligence analysis happens around the clock. How can human-agent systems be leveraged to support shift-work for information analysts? What measurable impacts on effective decision making have been observed by adding intelligent information agents into human teams of knowledge workers?

proposing team may submit proposals for all three technical areas. The decision as to which proposal to select for award is at the discretion of the Government.

B. TECHNICAL AREAS

1. TA1: Social Science Theories for Human-Agent Systems for Intelligence Activities (Cooperative Agreement; CUI)

The primary objective for the TA1 team is to formulate and test novel hypotheses about collective judgment formation within human-agent systems engaged in information processing for intelligence activities. The proposed work must be grounded in strong social science theories, including but not limited to Transactive Memory Theory1 and/or Theory of Mind2. These theories provide a theoretical lens in which to understand collectives through shared mental models and are being extended to incorporate expanded teaming capabilities arising with artificial social intelligence3,4 and generative artificial intelligence. For example, transactive memory systems have been expanded into transactive systems framework5 for including transactive reasoning and transactive attention. And Theory of Mind has been extended beyond humans to define an artificial theory of mind6 for agents’ perceptions of humans. In addition to these theoretical lenses, successful proposals will focus on shift handover coordination, information consolidation and synthesis, and critical information highlights to accommodate dynamic team environments.

Beyond the above, it is expected that proposers will choose to focus on two or more of the following potential research areas.

• Cognitive biases and their impacts on collective judgment formation o Individual cognitive biases o Human-teaming biases o Emergent human-agent teaming biases o Neurodiversity7,8,9 with respect to information processing strengths

1 Palazzolo, E.T. (2017). Transactive Memory. In International Encyclopedia of Organizational Comm.

Scott and Lewis (Editors-in-Chief). John Wiley & Sons, Inc. DOI: 10.1002/9781118955567.wbieoc208 2 Baron-Cohen, S. (2001). Theory of Mind in normal development and autism. Prisme. 34, 174-183.

3 DARPA Program: Artificial Social Intelligence for Successful Teams (ASIST):

https://www.darpa.mil/program/artificial-social-intelligence-for-successful-teams 4 Artificial Social Intelligence for Successful Teams: https://artificialsocialintelligence.org/ 5 Gupta, P., & Woolley, A.W. (2021). Articulating the role of AI in collective intelligence: A transactive systems framework. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 65.

6 Williams J, Fiore S.M. and Jentsch F. (2022). Supporting Artificial Social Intelligence With Theory of Mind. Front. Artif. Intell. 5:750763. doi: 10.3389/frai.2022.750763 7 Armstrong, T. (2011). The Power of Neurodiversity: Unleashing the Advantages of Your Differently Wired Brain. Da Capo Lifelong Books.

8 RAND's Weinbaum discuss neurodiversity in U.S. National Security Community:

https://abcnews.go.com/Nightline/video/measures-place-make-military-accessible-people-autism- 109777209 9 Weinbaum et al. (2023). Why National Security Needs Neurodiversity.

https://www.rand.org/pubs/research_briefs/RBA1875-1.html

• Exogenous pressures and obstacles on collective judgment formation o Time constraints o Turnover o Dynamic global events

• Impact of information source types on collective judgment formation o For example: free-form text, instructions, tables, maps, diagrams, pictures, audio, and video

• Narratives: building, following, detecting changes o Story arcs o Patterns of life o Building and tracking knowledge threads o Counter-narrative information integration

• Impact of discovering violations to data integrity & provenance on belief systems

• Misinformation/deception within corpus of data o Identification o Negation o Management

• Behavioral indicators of collective judgment o Individual and collective predictors of values, beliefs, and opinions o Indicators for the dynamics of belief formation o Predictors of successful coordination and information consolidation

• Uncertainty management

• Game theory o Establishing the rules and predicting value/utility for information

• Measurable team performance o Enhancements from humans teaming with agents o Detriments from humans teaming with agents o Agent support for distributed analyst teams

TA1 proposals should describe the state-of-the-art and how it will be advanced. TA1 proposers should describe how they will minimize the ambiguity of their claims, and how their constructs will be measured. Proposals should also describe methods for verifying the robustness of analyses. TA1 proposals should include a detailed description of theoretical variables and relationships, along with any existing published claims from the literature that can be tested in the TA3 testbed. Wizard of Oz solutions will not be accepted for consideration. Rather, the TA1 performer will work closely with TA2 performer to inform the design of agent architectures and will collaborate with the TA3 team to inform the design of the testbed for agent interaction and for hypothesis testing such that humans can be tested as a part of actual human-agent teams within this program. The extent of collaboration will be further clarified based on the type of award provided.

2. TA2: Computational Models for Human-Agent Systems for Intelligence Activities (Cooperative Agreement; CUI)

The objectives for the TA2 team are twofold: (1) create formal models of collective judgment formation that ground the social science models of TA1 in appropriate logical, mathematical, and computational terms, and (2) design intelligent systems that can be used to demonstrably improve collective judgment formation. The systems developed will support human analysts doing knowledge work, unobtrusively learning who knows whom, who knows what, who needs what information, and when. Improvements are expected to apply to the judgments produced (e.g., with greater accuracy and speed) as well as to the functioning of the team (e.g., alternative organizational structures or communication pathways, robustness in team substitutions).

The Army believes that a new opportunity for collective judgment formation has appeared, brought about by recent scientific advances in the military information sciences. One is the advent of newly capable large language models (LLMs), with regular releases to the public, and a continuing stream of findings in the research literature about their strengths and limitations.10 LLMs are a promising new avenue for knowledge ingestion and human-agent interaction. Beyond LLMs, the whole class of generative AI technologies may prove relevant to this program. In a different subfield, new game-theoretic models and algorithms are demonstrating super-human performance in collaborative and competitive action on tasks of interest to the Army.

Intelligence analysis is fundamentally an adversarial task with respect to external entities, a cooperative task with respect to collaborating analysts. Finally, new foundations for probabilistic and causal reasoning are being proposed and applied to challenging, real-world problems. Any single proposal may not touch on all these threads; they are mentioned as possible approaches.

Different technical approaches to understanding collective judgment formation are possible. For example, consider the analysts as forming a dynamic hierarchical network that represents team structure, with information propagating from one node to another until some conclusion has been reached. One might focus on the quality of data (e.g., accuracy, precision, uncertainty, completeness, timeliness) and surrounding characteristics (e.g., reliability, cost of acquisition), to better understand how these factors influence the quality of judgments. One might concentrate on patterns in network structure that make agreement on a correct interpretation easier or harder to develop. One might look to mechanisms that create incentives for more or less collaboration and information sharing, as appropriate to context. One might focus on how information is processed by individual analysts (e.g., how disparate uncertain evidence is combined) to understand how evolving context contributes to understanding at a more abstract organizational level. Each of these perspectives should reflect theories developed under TA1 and in the literature as well as computational modeling considerations. The task then is to leverage such a model of collective judgment

10 Achiam, J., et al. (2023). GPT-4 Technical Report. arXiv preprint arXiv:2303.08774.

formation to improve performance. From a data quality perspective above, for example, a large language model might contribute to integration of sensor tracking data with social media reports, to highlight otherwise ambiguous changes in monitored entities.

From a network structure or game theoretic perspective, analysis of the network as a graph of agents might lead to suggestions about changes in how much or what kind of information should be shared at different stages. The information processing perspective might suggest different specialized filters and tools for analysts, small analyst groups, and the entire team.

To flesh out the above, it is expected that proposers will choose to focus on two or more of the following potential research areas.

• Models of rationality

• Models of inference (e.g., probabilistic networks, theorem proving, neural models)

• Big data and data-driven decision making

• Centralized and distributed decision making

• Theory of Mind

• “Genres” of knowledge representation and reasoning (e.g., causality, narrative, norms)

• Generative artificial intelligence, including large language models

• Computational game theory

• Models of human cognition and social cognition

• Models of explanation, instruction, advice taking, i.e. the communication of judgments

Consistent with the TA1 description in the previous section, TA2 proposals should describe the state-of-the-art and how it will be advanced. TA2 proposers should describe their models, algorithms, evaluation metrics, and evaluation procedures. In the end, TA2 research should lead to models of collective judgment formation that have explanatory value, attributing good or poor performance to features of the problem space. Models are expected to be validated in collaboration with performers in TA1.

The models should further be part of a rigorous analysis in which improvements can be evaluated both theoretically and empirically. As with TA1, the TA2 team will collaborate with the TA3 team to inform the design of the testbed for agent interaction and for hypothesis testing such that humans can be tested as a part of actual human-agent teams within this program. By the end of the program, the agents should be made available as a resource to the broader scientific community or provide a means by which future developments by other parties, after the completion of the project, can be fairly compared with results produced through use of these agents during the project.

3. TA3: Testing, Evaluation, and Transitions for Human-Agent Systems for Intelligence Activities (Contract; Cleared Facility; TS/SCI eligible)

Testbed: The TA3 performer will leverage an existing testbed through adaptation, extension, and implementation for a scalable testbed environment. The TA3 performer will either own their own testbed for use in this research program or utilize an open source testbed. Further, this team will organize and execute human subjects experiments in this testbed, and coordinate with other CJF performers to assure protocols and APIs are stable and functioning prior to experiments. The testbed must provide an environment that allows for both simple and complex challenge problems for human-agent integration with a single human, multiple humans (teams as large as 10), and complex teams with individuals possessing specialized roles, skills, and working in shifts or split across time zones.

Strong TA3 proposals will adapt and extend an existing testbed with environments that are accessible by distributed teams for the advancement of human-agent systems. After each experiment, the version of the testbed environment should be made available to TA1 and TA2 performers for continued testing, while TA3 develops the next version of the testbed. Strong proposals will discuss ways in which to make the testbed environments available to TA1 and TA2 performers early in the program. By the end of the program, this experimentation environment should be sufficiently mature that it can be made open source as a resource to the broader scientific community or provide a means by which future developments by other parties, after the completion of the project, can be fairly compared with results produced through use of the testbed during the project.

Experiments: In collaboration with the TA1 and TA2 performers, the TA3 performer will organize, design, and execute at least four experiments (approximately one per year).

Proposed testbeds must be able to challenge human and hybrid teams with both simple and complex tasks. The testbed should include the capability to: present multimodal information to end users; support the development of information processing agents and interactive agents for human engagement; free-form note taking and sharing by human participants; and adaptable data capture capabilities to support TA1 and TA2 research needs. Example measures to quantify during experiments include but are not limited to agent usefulness, trust, adaptation and resilience time, coordination effort required after perturbation, team effectiveness, development of transactive memory system, and development of human and agent theory of mind. Strong proposals will discuss additional relevant metrics and measures to be quantified during evaluation exercises.

Additionally, proposed testbeds should be cable of creating realistic intelligence analyst experiences, similar to the exemplar presented earlier.

HSR: The TA3 performer will be responsible for managing all Human Subjects Research (HSR) for the program, will likely serve as the Single IRB (Institutional Review Board) for research oversight, and leading coordination with the Army’s Human Research Protection Office (HRPO). The TA3 performer will ensure that all HSR approvals are in place prior conducting experiments and/or allowing access by performers to human subjects data.

Data management: The TA3 performer will work with government-provided and other publicly available dataset(s), and handle identifying, gathering, cleaning, and making available to the program dataset(s) that are capable of testing relevant hypotheses for the program. The TA3 performer will coordinate with TA1 and TA2 performers for the duration of the program to ensure timely data collection and access to both collected and curated data.

Technical Exchanges: The TA3 team will host technical exchange meetings with all performers approximately three months before each experiment. Technical meetings will include a two-day all-hands principal investigator (PI) meeting (plan for attendance of roughly 50-75 people to include PIs, graduate students, postdocs, and government scientists). Location for technical meetings will be local to the TA3 performer and/or the Washington D.C. area. TA3 proposers should discuss how they will facilitate these events, including the acquisition and provisioning of appropriate event facilities and resources.

Transitions: Strong TA3 proposals will include plans for transition support for the program as theoretical and technical advancements occur, as well as at the end of the program. Transitions can be to other agencies within the Department of Defense, to include operational units, and the Intelligence Community, as well as to potential industry partners.

4. Collaboration

The CJF CRA requires a multidisciplinary, collaborative effort that synergistically views every new funded effort as part of a network of researchers to grow and support an ecosystem of high-quality, innovative research actively sharing knowledge and collaboratively addressing scientific gaps critical to the Department of Defense. The success of this program will require meaningful collaborative partnerships between government, academia, and industry to advance the science. This program has been developed in coordination with other related ARL-funded collaborative efforts (see descriptions of ARL collaborative alliances at https://www.arl.army.mil/business/collaborative-alliances/) and shares a common vision of highly collaborative academia-industry-government partnerships. Proposals should address their intellectual property (IP) approach and how their approach will foster collaboration with ARL and other funded CJF CRA teams, and how their solution will further advance the state-of-art and, where possible, contribute to the open source community and/or ARL/government owned solutions.

Applicants should identify their collaboration plans for topic areas in this BAA. The collaboration plan must:

• Identify how simulated, realistic, and operationally relevant networks, data, and ML models will be generated for use in the proposed research. This plan should include opportunities for input and collaboration with ARL researchers.

• Frequency of meetings

• Frequency of reviews/evals, etc.

5. Associate Performer Agreement.

To ensure a fully collaborative program, all TA1, TA2, and TA3 performers will sign an Associate Performer Agreement to facilitate program-wide collaboration between all performers and government researchers such that the work is performed as a single, though complex, program of research. It is expected that all performers and government researchers will meet virtually multiple times throughout each year of the program to ensure coordination and teaming not just within their Technical Area, but across all Technical Areas. Additionally, all performers and government researchers will gather annually to facilitate deep connections between researchers, to support collaborations including with graduate students and postdocs across institutions, and to provide the government and Review Boards with status updates.

6. Program Intent

It is the intent of this BAA to solicit the most creative, innovative, and flexible approaches to the ultimate goal of generating and exploiting research to solve pressing research gaps and issues impacting both the military and commercial sectors. This BAA seeks proposals which will result cooperative agreements (CAs) for TA1 and TA2, and a contract for TA3. Proposals will be solicited for innovative solutions that will advance the state-of-art in the research technical areas detailed above and that will result in experiments demonstrating the art-of-the-possible to inform future warfighter concepts and capabilities. Compliant proposals will be evaluated, as discussed below.

One proposal for each technical area will be selected for funding from this BAA. ARL reserves the right to negotiate with an Applicant to re-scope their proposal’s technical focus, period of performance, and associated costs to maximize the available program funding, balance of research topics across the program, and overall impact to the program.

NOTE: Under this BAA, the Government is NOT looking for proposals for existing solutions to employ, NOR is the Government looking for proposals to select a contractor to perform a broad range of services as required by the Government. Each proposal received will be subject to a scientific or peer review as delineated later in this document.

C. FEDERAL AWARD INFORMATION

It is anticipated the awards will be made in the form of contracts and cooperative agreements. While other vehicles such as grants and contracts will be considered, the strong preference for TA1 and TA2 is for a cooperative agreement as the award type to allow for maximum interaction, cooperation, and collaboration between the Government and the awardee. The awards will be made at funding levels commensurate with the proposed research, investigator/team type, as well as availability of funding.

We realize the preparation of a research proposal often represents a substantial investment of time and effort by the applicant. Therefore, in an attempt to minimize this burden, we are requiring applicants interested in funding for TA1 and TA2 under this BAA to submit white papers describing the type of research effort to be proposed;

interested TA3 applicants are strongly encouraged to submit a white paper in that it will provide an opportunity for the Government to provide early feedback. White papers received will be reviewed by a Government panel. A detailed description of the white paper submissions and evaluations can be found in Section E.

1. Anticipated Funding: Pending availability of funds, we anticipate that one award will be made for each TA and that they will have the following funding profiles ($k).

The TA1 and TA2 Cooperative Agreements will be awarded with a 36-month Base Period with an additional 24-month Option Period. The TA3 Contract will be awarded with a 12-month Base Period and four (4) 1-year Option Periods. In addition to the three core performers selected through this BAA announcement, it is anticipated that this Program will support one or more 3-year efforts for TA1 and TA2 starting in FY27.

Proposals are expected to be bid at a cost commensurate with the level of effort, not to exceed the funding profiles offered above. All funding is expected to be expended within the fiscal year of performance.

2. Funding: This BAA is issued subject to the availability of funds. ARL has submitted the requisite documents to request funding for the period covered by the program.

Funding levels specified in this BAA are estimated funding levels and are for proposal preparation purposes only; actual funding levels of the awards will be updated annually as part of the federal appropriation process.

3. Profit/Fee: Profit/fee is not permitted for TA1 and TA2 under this BAA.

4. Cost Sharing: Cost sharing is not required under this BAA.

5. Award Instrument: The ACC-APG RTP Division has the authority to award a variety of instruments on behalf of ARL. Anticipated awards will be made in the form of cooperative agreements or contracts. The ACC-APG RTP Division reserves the right to select the type of instrument most appropriate for the effort proposed. Applicants should familiarize themselves with these instrument types and the applicable regulations before submitting a proposal. Following are brief descriptions of the possible award instruments:

a. Cooperative Agreement. A legal instrument which, consistent with 31 U.S.C. 6305, is used to enter into the same kind of relationship as a grant, except that substantial involvement is expected between the Federal Government and the recipient when carrying out the activity contemplated by the cooperative agreement. The term does not include "cooperative research and development agreements" as defined in 15 U.S.C.

3710a. No fee or profit is allowed.

Cooperative agreements for institutions of higher education, nonprofit organizations, foreign organizations, and foreign public entities are primarily governed by the following:

i. Federal statutes

ii. Federal regulations

iii. 2 CFR Part 200

iv. 2 CFR 1104

v. 32 CFR Parts 21, 22, 26, and 28

vi. DoD Research and Development General Terms and Conditions

vii. Agency-specific Research Terms and Conditions

Cooperative agreements for for-profit and nonprofit organizations exempted from Subpart E—Cost Principles of 2 CFR Part 200, are primarily governed by the following:

i. Federal statutes

ii. Federal regulations

iii. 32 CFR Part 34 – Administrative Requirements for Grants and Agreements with For-Profit Organizations

iv. 32 CFR Parts 21, 22, 26, and 28

v. DoD Research and Development General Terms and Conditions

vi. Agency-specific Research Terms and Conditions

b. Procurement Contract. A legal instrument, consistent with 31 U.S.C. 6303, which reflects a relationship between the Federal Government and a state government, a local government, or other entity/contractor when the principal purpose of the instrument is to acquire property or services for the direct benefit or use of the Federal Government.

Contracts are primarily governed by the following regulations:

i. Federal Acquisition Regulation (FAR)

ii. Defense Federal Acquisition Regulation Supplement (DFARS)

iii. Army Federal Acquisition Regulation Supplement (AFARS)

iv. Fee or profit is allowed under this BAA

c. Regulations Resources. The following websites may be accessed to obtain an electronic copy of the governing regulations and terms and conditions:

i. FAR, DFARS, and AFARS: https://www.acquisition.gov

ii. Code of Federal Regulations (CFR): https://www.ecfr.gov/

iii. DoD Research and Development General Terms and Conditions:

https://www.nre.navy.mil/work-with-us/manage-your-award/manage-grant-award/grants-terms-conditions

iv. Agency-specific Research Terms and Conditions:

https://www.arl.army.mil/resources/baa-forms/#terms-and-conditions

d. Proposal Submission: The application process consists of proposal submissions from applicants under this BAA. Applicants should note there are page limitations and other requirements associated with the submission process. Submissions in connection with this BAA are due by the date and time specified above. This BAA may be amended in the future for additional topics and will include the submission requirements for those proposals. Additional proposal instructions may be provided during the time of white paper feedback.

e. Classified Submissions: Proposals containing classified information are not accepted under this BAA. However, proposals may discuss the need to access or generate controlled unclassified information (CUI) for the proposed work. If an award requires access to CUI, DFARS Clause 252.204-7012 will be included in the award, as well as additional ARL specific requirements as determined necessary. Similarly, TA3 proposals may discuss the need to access or generate classified information up to TS/SCI, but cannot contain classified information in the proposal. If an award requires access to classified information than the award will be modified with the appropriate protective language before any classified work can be conducted on this contract.

f. Period of Performance: The CA Awards made as a result of this BAA will provide for a period of performance of 36 months, with the potential to exercise an option period for up to 24 months. The Contract Award made as a result of this BAA will provide for a period of performance of 12 months, with the potential to exercise four (4) 12-month option periods.

g. Place of Performance: There is no limitation within the United States or its territories on the place of performance, although on-site collaboration at ARL facilities and with ARL researchers as well as with other Recipients is encouraged. It is mandatory that all Recipients present research progress at annual program review events.

h. Contact Information: All questions or comments concerning this BAA must be submitted to the Government through the CJF CRA mailbox: cjf-cra-baa@army.mil.

Comments or questions submitted should be concise and to the point. In addition, the relevant part and paragraph of the BAA to which a comment or question pertains must be referenced. Responses to non-proprietary questions received will be posted without attribution to the CJF CRA BAA (W911NF-24-S-0010) at https://sam.gov and https://grants.gov under the “General Information/Questions & Answers" section for the benefit of all interested parties. All clearly identified and marked proprietary questions submitted will be responded to via an individual email response. Applicants are encouraged to submit questions as early as possible. The deadline for submission of questions which will be answered under this BAA is listed in Event timeline above. Any answers provided to questions do not change the requirements of this BAA.

D. ELIGIBILITY INFORMATION

1. Eligible Applicants

Eligible applicants under this BAA include institutions of higher education, nonprofit organizations, for-profit organizations (i.e., large and small businesses) in the United States or its territories. White papers and proposals will be evaluated only if they are for fundamental scientific study and experimentation directed toward advancing the scientific state of the art or increasing basic knowledge and understanding. White papers and proposals focused on specific devices or components are beyond the scope of this BAA. More than one white paper and/or proposal is allowable from any single institution or organization, but each white paper and/or proposal must be limited to a single TA.

2. CUI and TS/SCI Performers

a. TA1 and TA2 will be awarded as cooperative agreements at the Controlled Unclassified Information (CUI) level. As such, all proposing members of TA1 and TA2 teams, and their post docs, must meet CUI eligibility requirements post-award which are:

i. Citizenship: As CUI access will only occur at Adelphi Laboratory Center

(ALC) and Aberdeen Proving Ground (APG), these individuals will need to be U.S. citizens.

ii. Background Investigations: Individuals who require access to CUI must undergo a T1 background investigation and receive and maintain a favorable Homeland Security Presidential Directive 12 (HSPD-12) adjudication for the duration of the CUI access. Individuals requiring a background investigation will be submitted at the discretion of the ARL CAM in coordination with ARL Security Office.

iii. Training: All individuals requiring access to CUI under this agreement will complete initial and annual CUI training by taking the DOD Mandatory Controlled Unclassified Information (CUI) Training IF141.06 through the Center for Development of Security Excellence located at https://securityawareness.usalearning.gov/cui/index.html or another medium approved by ARL. Certification of completion will be kept on file by the Recipient and made available upon request by ARL.

iv. Information Systems Requirements: Access to CUI on information systems is limited to government owned and accredited information systems (i.e., computers) only at Adelphi Laboratory Center (ALC) and Aberdeen Proving Ground (APG), MD.

b. TA3 will be awarded as an unclassified contract. All members of the proposing team and their post docs must meet CUI requirements as explained above.

While the original contract will not contain any classified components, it may be modified during the program to allow for TS/SCI classified work under this contract. As such, at least one member of the proposing team must hold an active TS/SCI or equivalent clearance with the U.S. Government. Additionally, the proposing team’s organization must have an active facility clearance to support TS/SCI work.

c. White papers and proposals must include a list of key personnel, their CUI eligibility, and what, if any, clearance they hold. This information should also be clearly indicated on the top of each submitted 2-page biographical sketch.

3. Cost Sharing or Matching

Generally, there is no requirement for cost sharing, matching, or cost participation to be eligible for award under this BAA. Cost sharing and matching is not an evaluation factor used under this BAA.

In addition, if cost sharing is proposed on a grant or cooperative agreement proposal submitted by a nonprofit or institution of higher education, the award will be subject to the restrictions at 2 CFR 200.306. If cost sharing is proposed on a contract proposal, the award will be subject to the restrictions at FAR 35.003.

4. Other

Federally Funded Research and Development Centers (FFRDCs) are subject to applicable direct competition limitations and cannot propose to this solicitation in any capacity unless they meet the following conditions. (1) FFRDCs must clearly demonstrate that the proposed work is not otherwise available from the private sector.

(2) FFRDCs must provide a letter, on official letterhead from their sponsoring organization, that (a) cites the specific authority establishing their eligibility to propose to Government solicitations and compete with industry, and (b) certifies the FFRDC’s compliance with the associated FFRDC sponsor agreement’s terms and conditions.

These conditions are a requirement for FFRDCs proposing to be awardees or sub-awardees.

E. WHITE PAPER SUBMISSION INFORMATION

1. Overview

White papers should focus on describing details of the proposed research, including how it is innovative, how it could substantially increase the scientific state of the art, Army/DoD/IC relevance, and potential impact.

White papers are limited to seven (7) total pages; one (1) cover page, four (4) pages for technical content, and a two (2) page addendum for all team members biographical sketches as discussed below. Evaluators will only review the white paper cover page, up to four technical content pages, and the two-page addendum. Please do NOT send additional documents as they will NOT be evaluated.

White papers must be in the following format but do not require any special forms:

• Page Size: 8 ½ x 11 inches

• Margins – 1 inch

• Spacing – single

• Font – Arial, 12 point

Combine all files and forms into a single PDF before submitting.

2. Format and Content of White papers

a. COVER PAGE (not to exceed one page)

The white paper cover page shall include at a minimum: Title of the white paper, name and contact information of the individual and organization submitting the white paper, the Technical Area that will be addressed, and the BAA number of this announcement

(W911NF-24-S-0010).

b. TECHNICAL CONTENT (not to exceed four pages)

i. Scientific (max. 3 pages) - What is your basic idea? Why is it innovative? What are the technical challenges to this idea you will be focused on with your research?

What are the scientific and technical approaches to overcome the challenges?

ii. Impact (max. 0.5 page) - If successful, how will this work improve the capabilities of intelligence analysts?

iii. Programmatic (max. 0.5 page) – To the extent known at this point, provide details on the research team (max. 6 members) with CUI eligibility and any clearances, timeline, deliverables, and estimated cost of the research. Brief per year descriptions are an acceptable level of granularity. For TA3 proposers, please include information about your facility clearance.

c. ADDENDUM (not to exceed 2 pages total for all PI/Co-PIs)

Within two (2) pages, include biographical summaries of the key personnel who will perform the research, and succinctly highlighting their qualifications, areas of expertise, and relevant experience.

3. Restrictive Markings on white papers

a. Proprietary Information

Any proprietary data that the applicant intends to be used only by the Government for evaluation purposes must be clearly marked. In the absence of such identification, the Government will conclude there are no limitations or restrictions on technical data or computer software included in the white paper. Records or data bearing a restrictive legend may be included in the white paper. All proposals are treated as procurement sensitive and are disclosed only for the purpose of evaluation. Proposals will be subject to a peer review by highly qualified subject matter experts, both Government employees and in certain cases non- Government employees.

Care must be exercised to ensure that classified, sensitive, and critical technologies are NOT included in a white paper.

b. Freedom of Information Act (FOIA)

Applicants are cautioned, however, that portions of the white papers may be subject to release under terms of the Freedom of Information Act, 5 U.S.C. 552, as amended.

4. Evaluation and Disposition of white papers

a. Evaluation Process:

i. Applicants are advised that invitations for proposals will be made based on the white paper submissions and the availability of funding.

ii. Each white paper will be evaluated by a Government Panel for

a) the concept's fit within the proposed TA of submission

b) scientific merit

c) potential contributions of the effort to the Army, Department of Defense, and/or the Intelligence Community mission.

iii. Applications of the highest merit will be invited by a CJF Contracting Officer to submit full proposals.

iv. A TA1 or TA2 Applicant that does NOT receive an invitation from the Government to submit a Full Proposal is NOT eligible to submit a Proposal.

v. TA3 Applicants are encourage to submit White Papers, but may submit a Proposal without a Contracting…