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Operating, maneuvering, engaging, defending, and commanding the Combat Vehicles on the modern battlefield requires a significant level of crew communication, systems management, and situational awareness. As a result, it imperative to reduce the cognitive burden on vehicle crews and enable rapid decision-making, especially during combat. To support this, the US Army is seeking innovative solutions that will enable autonomous maneuvers in response to crew commands issued with natural language.

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Several approaches to CoA generation for military applications currently exist. These methods leverage large language models (LLM) applied to Doctrine or deep learning/hierarchical modeling applied to wargaming. Each existing LLM approach is inadequate for CoA generation at BN due to lack of proper consideration of Tactics, Techniques and Procedures (TTPs) and Standard Operating Procedures (SOPs) for both friendly and enemy forces. The existing learned/hierarchical approaches that depend on wargaming are also inadequate due to a lack of fully automated wargaming capability at the BN echelon and/or due to CoA outcomes that are not aligned with best practices (TTPs and SOPs). Proposals submitted under this topic will leverage state-of-the-art Artificial Intelligence (AI) approaches to create a CoA recommendation module narrowly tailored to the BN echelon. This capability will expand to encompass higher echelons in later phases.

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Develop, demonstrate, and deliver solutions that increase the precision of automated drone landings. The system should be capable of landing a drone onto either moving or non-moving unmanned platforms, or the ground and should be able to function in highly congested or adverse environments (such as dense foliage, close buildings, or multiple aerial operations) with little or no input from a human operator.

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The U.S. Army Engineer Research and Development Center's Cold Regions Research and Engineering Laboratory is looking to obtain innovative solutions or potential new capabilities in the following categories: Building Cold Region Domain Awareness, Enhancing Mobility and Maneuver in Cold Region Environments, Integrated Ice Operations, Advanced Materials Development and Applications in Extreme Cold Environments, and Resilient Cold Region Energy Systems.

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Competitive applicants will have reviewed the parts list provided on DLA Small Business Innovation Program (SBIP) website, (Reference 4) as well as the technical data in the cFolders of DLA DiBBs, (Reference 3). Proposals can evolve in one of four ways depending on the availability of technical data and NSNs for reverse engineering as follows. Information on competitive status, RPPOB, and tech data availability will be provided on the DLA SBIP website, (Reference 4). a. Fully Competitive (AMC/AMSC-1G) NSNs where a full technical data package is available in cFolders are not eligible for this program. b. Other than (AMC/AMSC-1G) NSNs where a full Technical Data Package (TDP) is available in cFolders. These items may also require a qualification of a Representative Article. The SBM proposal should reflect timeline, statement of work, and costs associated with producing a Source Approval Request (SAR) and (if applicable) qualification of a Representative Article. Contact the TPOC if necessary. The scope and procedures associated with development of a SAR package are provided in Reference 1. c. Repair Parts Purchase or Borrow (RPPOB) or Surplus may be an option for other than 1G NSNs where partial or no technical data is available in cFolders. NSNs, if available, may be procured or borrowed through this program for the purposes of reverse engineering. The instructions for RPPOB can be found on the websites, Reference 5. The SBM proposal should reflect timeline, statement of work and costs associated with the procuring the part and reverse engineering of the NSN. Depending on complexity, producing both the TDP and SAR package may be included in Phase I.

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The DoD currently has a need for SWaP-favorable solutions for pulse sharpening of high-voltage impulses. Current methods of high-voltage pulse sharpening from solid-state pulse generators rely on commercial off the shelf (COTS) devices. However, these devices are limited in the amplitude of the impulses they can sharpen and have limited operational lifetimes. The DoD has a need for a robust pulse sharpening solution which may be optimized for specific high-voltage impulses and can scale to sharpen impulse amplitudes greater than 100 kVs. Ferrimagnetic shocklines offer the potential to meet this requirement and deliver a capability which may be optimized and can scale to the required high-voltage amplitudes as well as handling pulse repetition rates on the order of 10s of kHz. Proposed pulse sharpening solutions should offer the capability to sharpen nanosecond pulse width high-voltage impulses to generate outputs with 10%-90% rise time pulses on the order of 150 picoseconds and full width half maximums of 500 picoseconds.

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On September 22, 2021, the US House Veterans Affairs Committee held a hearing entitled, “Veteran Suicide Prevention: Innovative Research and Expanded Public Health Efforts” [1]. The hearing followed the release of annual data from the US Department of Veterans Affairs showing that the disproportionate rate of veteran suicide is a public health crisis [2]. Although there is no single reason why veterans commit suicide, evidence suggests that stable housing, financial security, access to healthcare, addressing social isolation and loneliness, and treating the effects of trauma are important components of a comprehensive suicide prevention strategy; all of which require coordination and cooperation across families, communities, and at all levels of government. Recent advances in AI, and specifically LMs, have the potential to help lessen the effects of social isolation/loneliness and trauma. According to a 2019 report on “Sleep and timing of death by suicide among US Veterans 2006-2015” [McCarthy, et al. 2019], the raw proportion of veteran suicides peaks between the hours of 1000 and 1200; however, the peak prevalence of suicide, after accounting for the population being awake, is between the hours of 0000 and 0300 (p < 0.00001, F = 0.88). The highest Standardized Incidence Ratio (SIR) is at midnight; US Veterans are eight times more likely to die by suicide than expected given the population awake (SIR = 8.17; 95% Confidence Interval = 7.45-8.94). In other words, when clinical help is likely unavailable or difficult to access, technology has the potential to provide critical assistance. Recent advances in the field of natural language processing have allowed LMs (for example, Chat GPT (Generative Pre-trained Transformer)) to be fine-tuned using reinforcement learning based on human feedback [3]. Prior efforts have shown that it is possible to create a highly conversational model based on 40,000 pieces of feedback [4]. Other recent research work in the field suggests promising results in prompt engineering [5], using memory-based machine learning with dramatic improvements in the LM’s ability to stay on task, and return more accurate and precise results. [6] LM4VSP seeks to develop a clinical co-pilot based on LMs specific to the mental health subdomain, and in close collaboration with mental health subject matter experts (SMEs). The goal is for the LM4VSP clinical co-pilot to enable caregivers to offer around-the-clock assistance and accelerate their understanding and assessments and improve the effectiveness of intervention.

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The primary problem this topic addresses is the growing complexity of traditional and AI/ML-based cybersecurity threats to DoD weapon systems, increasingly connected and automated weapon systems architectures, and the need for advanced tools to enhance development-phase cyber resiliency efforts and exploit modeling. This topic is essential because it will improve US DoD aviation and missile platforms’ resilience, survivability, and lethality through the enhanced identification and exploitation modeling of system vulnerabilities during system development. This will achieve significant impact in the most cost-effective phase of the system lifecycle. The proposed approach leverages state-of-the-art LLMs to develop a specialized tool for system security engineering risk reduction efforts (blue team and AAMCATs) and provides critical exploitation inputs to DoD-approved red team operations. This topic innovates on existing technology by fine-tuning a foundational LLM built to analyze complex weapon system codebases, identify vulnerabilities, and generate proof-of-concept exploits. This approach combines natural language processing and code analysis capabilities, creating a powerful tool that surpasses current manual and automated analysis methods. The proposed LLM will be trained on the large amounts of relevant data available to DEVCOM AvMC including code, network traffic, static and dynamic analysis results, external software code bases and malicious software examples, and threat intelligence from partner US Government organizations. The LLM will be integrated with AvMC software and hardware integration labs to provide further tuning and address the issue of run-time analysis when software code is unavailable to the cyber resiliency team.

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Internet-of-Things (IoT) devices have seen unprecedented growth [1] and yet remain one of the weakest links when it comes to cybersecurity [2]. User and device authentication for battery-operated IoT devices (e.g., smartphones and wearables) is challenging due to limitations on the available energy, user interface, and processing power [3]. Over the last few years, multiple authentication techniques have been developed to address these challenges, for example, location-based authentication techniques [4] and gait-based authentication techniques [5]. However, existing techniques face challenges in terms of performance overhead, power consumption, and overall efficiency of cryptographic operations [6]. To address these challenges, DARPA seeks novel, continuous [7], multi-factor authentication [8] solutions for small weight and power devices.

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Naval Special Warfare Command (NSWC) seeks to accelerate the development, procurement and integration of unique capabilities into deployable warfighting capabilities in support of the Joint Force and our allied Special Operations Forces (SOF).

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Warfighters are often deployed to emerging disease hotspots. To help mitigate potential exposure risks, DoD entities tasked with force health protection [1] rapidly assess, often on-site, a range of sample types for potential biological threats. Current rapid on-site identification (ID) methods include sequencing [2,3] and lateral flow assays (aka dipstick tests) [4], which destroy both the sample & the organism(s), inhibiting further analysis. While sequencing costs continue to decrease and cheap lateral flow assays continue to increase in organism scope, forward operators often triage the number of sites and samples collected due to resource, personnel, and time constraints [5]. Recent advances in metasurfaces [6,7]7, optical waveguides [8], microfluidics [9], and (super)high resolution imaging [10], now suggest accurate organism ID and viability maintenance can co-occur; however, current non-destructive systems lack field utility due to their lab-centric designs [7]. This SBIR will address both the significant limitations to rapid, on-site biosurveillance in resource constrained environments and the lab-centric designs for non-destructive pathogen ID by developing human-portable, low size, weight, and power (SWaP) technology to rapidly and non-destructively ID viruses on-site and in the field. Final Phase II prototypes must be: ≤ 1 ft3; ≤ 5 lbs; and ≤ 200-Watt peak power input, with all SWaP requirements inclusive of power delivery mechanisms, software/data processing, consumables, and reagents, and should function with a wide range of simulated clinical samples (e.g., blood, saliva, nasal swabs, etc.) and contrived environmental samples (swipes/wipes, chicken rinse, etc.). Systems should non-destructively ID viruses faster than current state-of-the-art sequencing and lateral flow assays (≤ 15 min per sample, not including sample pre-processing) while maintaining viral infectivity for downstream lab-based analysis. By the end of Phase II systems should ID viruses faster than current state-of-the-art sequencing and lateral flow assays (≤ 15 min per sample), in the field sample pre-processing, if any, should be no more than 20 minutes per sample, and viral ID should be independent of cloud connectivity (e.g., database access, analysis and ID can occur on the device without cloud access).

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As one of the fundamental building blocks of military systems, materials or material configurations represent a set of unique opportunities for innovation in military systems and mission applications. The Department of Defense seeks to tap into the innovation and industrial capacity of the small business sector to generate novel materials or material configurations, demonstrate their scalability and manufacturability, and apply them to relevant challenges and use cases.

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The DoD needs to ensure that connectivity across a heterogeneous swarm of attritable multi-domain autonomous platforms remains intact. Communications equipment shall be capable of reliable operations in a contested spectrum along with ability to operate at lower classification levels in conjunction with attrition resilience concurrent with the vehicles that they are on. Anti-jam (AJ) and Low Probability of Intercept (LPI) Line of site (LOS) and Beyond Line of Site (BLOS) resilient connectivity, including automated and seamless join and exit to the network, are preferred. Networking solutions shall maintain network awareness and routing across multiple radio frequency communications paths. DoD needs the ability to push one-way updated commands via a broadcast through the LOS and BLOS radio frequency and data networks. Communications across the network must be secure, applying best industry practices of commercial grade transmission and communications security. DoD requires a network control channel and management tool to direct and/or redirect the UxV team in both real-time and non-real-time disconnected operations.

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DLA SBIR Program has a legacy of success in the advancement of critical manufacturing technologies that support our national security, along with a growing small business manufacturing network that fortify DLA supply chains. These efforts together with our Service partners are helping the DLA to build a resilient SBM base to reduce the acquisition and supportability costs of defense weapons systems, reduce manufacturing and repair cycle times across the life cycles of such systems, and transition manufacturing research and development processes into production. Competitive proposals should originate from small business manufacturing firms and include their capability to manufacture a National Stock Number (NSN) or component for specific weapon platform. Proposals with software or integrated manufacturing solutions will not be evaluated. Projects of this open topic can develop in several ways: a) SBMs can identify NSNs on the DLA Internet Bid Board System (DIBBS). More details are available at Ref. 2. JCP Certification required as described in Ref. 3. b) SBMs can identify NSNs through partnerships with the Air Force, Navy, Army or Marine Corps or Original Equipment Manufacturer (OEM). Service partner needs to be specifically identified with contact information for verification purposes. c) SBMs can propose advanced manufacturing methods for existing NSNs to improve cost, reduce lead time and/or improve quality. None of these projects can proceed without appropriate sponsorship from the DLA or one of the military Services. Identify specific partnerships and points of contact to strengthen your proposal. A specific NSN must be identified to participate in the open topic through independent SBM research. NSN’s will not be provided. The Offeror must fully understand the path to becoming an approved source for the proposed NSN and describe it in their proposal. PROJECT DURATION and COST: : Proposals exceeding these limits will not be evaluated.

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This RFI is to provide a mechanism to inform the office of new capabilities and concepts and the potential performers who will provide them. DARPA will use this information to determine the companies and individuals that STO would invite to have further substantive discussions to inform future projects and programs in appropriately classified settings when necessary.

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A technology solution is needed for wireless data transmission from Digital Source Collectors (DSCs) on Tactical Wheeled Vehicles back to a centralized collection point in the motorpool, specifically the Army Vantage dashboard. Transmission of Predictive Logistics data wirelessly during field operations with acceptable speed and network availability will significantly enhance the efficiency and effectiveness of vehicle operations. This data transport capability will enable Unit Commanders and Maintainers to monitor the health status, fuel and ammunition of their platforms in near real-time, facilitating tactical adjustments and prompt responses to critical maintenance issues. Solutions should focus on movement of High Priority critical fault and fuel/ammo status notifications.

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The Department of Defense (DOD) aims to increase small business participation within the industrial base and has boosted its involvement in providing goods, services, and research and technology to support national defense. However, significant obstacles persist that dissuade non-traditional vendors from entering the defense ecosystem of contracting and acquisition. Prominent among them is the complex industrial facility security clearance (FCL) process. The FCL process is instrumental in vetting businesses for their capability to handle classified information and bid on classified contracts. However, this process can be daunting for small businesses and non-traditional vendors due to fragmented information, heavy reliance on “government speak”, and the significant time and resources required to complete the process.DARPA’s Bringing classified Innovation to Defense and Government Systems (BRIDGES) has sought to connect innovation from small and nontraditional companies to classified Department of Defense (DoD) research and development (R&D) efforts, specifically by sponsoring innovative companies for facility clearances so that they can directly interact with DoD customers at classified levels. During execution of the BRIDGES program, DARPA observed firsthand the challenges that these small companies faced while going through the FCL process, most notably:• Misunderstanding what information was required on the required forms• Filling out forms incorrectly• Not fully understanding submission timelinesThis resulted in additional workload on DARPA security to answer questions and provide guidance, delays in processing for FCLs, and some FCL packages being rejected by DCSA.To address these challenges, DARPA is looking to develop a software application, leveraging recent advancements in language model technology, that can assist small and nontraditional companies apply for their FCLs, as well as their associated personnel clearances (PCLs). Information on the FCL and PCL processes, while disaggregated, is publicly available. A targeted language model can be trained on the processes, forms, and other related information, and distill this information into more easily understandable wording. Then, just like the popular and commercially available TurboTax software, this model can provide appropriate prompts to users to gather the required information, and then autocomplete the required forms. Additionally, it could provide insights into the deadlines and timelines, and provide a “chat bot” feature to answer questions about the processes.In this SBIR, the proposer will develop a prototype system ready to be tested by representatives from the target audience (small businesses and nontraditional contractors who are not familiar with the FCL and/or PCL processes) during the base effort. If successful, they will then oversee test and evaluation of the prototype by end users, ideally performers on DARPA’s BRIDGES program. During this test and evaluation period, the proposer will verify both the inputs and outputs of the application, collect feedback from users, and incorporate appropriate updates. The end goal will be a system that removes errors from FCL packages, improves processing timelines by removing delays/extension requests, while also improving the user experience and reducing the time and effort required by the end user.

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