United States

Humanitarian Assistance and Disaster Relief (HADR) operations require rapid damage assessment to effectively allocate resources and save lives, yet traditional satellite imagery analysis can take days to complete. This delay creates a critical gap between disaster occurrence and informed response, costing lives and resources. Our team at CMU&#39;s Software Engineering Institute (SEI) presents a modernized, cloud-based platform that enables domain experts and mission users to deploy custom computer vision models for damage assessment within minutes rather than days, significantly accelerating response times across multiple Areas of Responsibility (AORs).
Our approach addresses a fundamental challenge in operational disaster response: the need to quickly adapt pre-trained models to new, out-of-distribution imagery from emerging incidents with diverse geographic, structural, and disaster characteristics. CMU SEI previously co-hosted the xView2 Prize Challenge and deployed machine learning solutions for EUCOM (Turkey &amp; Ukraine, 2022-2024) and CENTCOM (Syria 2023). However, we observed critical limitations regarding fragmented workflows for model training and inferencing. Our platform leverages an image segmentation base model pre-trained on satellite imagery to predict damage classes across disaster types. When users encounter new scenarios, they upload imagery and rapidly train lightweight model adapters using LoRA (Low-Rank Adaptation), a Parameter-Efficient Fine-Tuning technique requiring limited computational resources and training data.
The adapter-based architecture provides several operational advantages: trained adapters can be reused across similar scenarios, fine-tuned for evolving situations, and shared across organizations. Domain experts can rapidly customize models for their specific AORs without deep machine learning expertise, democratizing access to advanced AI capabilities.
Our platform&#39;s cloud-agnostic architecture enables deployment on both commercial and government cloud environments and on-premises infrastructure, operating seamlessly from tactical edge environments to strategic operations centers, enabling cross-echelon collaboration. This infrastructure-agnostic design ensures damage assessment capabilities remain available regardless of network constraints or deployment environment, making it a force multiplier for operations where interoperability and rapid information sharing are paramount.
Preliminary results demonstrate that providing limited (2-16) labels to post-disaster satellite imagery yields substantial performance improvements over the base model, with low computational costs measured in minutes of GPU time. This efficiency is critical for first-wave response scenarios where every minute impacts mission outcomes. By enabling iterative refinement as new imagery arrives, our platform empowers Battle Damage Assessment to make data-driven resource allocation decisions at the speed required by modern HADR operations.

NAML 2026

Computer Vision for Accelerating HADR

battle damage assessment

humanitarian assistance

mlops

disaster response

image segmentation

computer vision

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-26 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

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AAAI 2026

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

Humanitarian Assistance and Disaster Relief (HADR) operations require rapid damage assessment to effectively allocate resources and save lives, yet traditional satellite imagery analysis can take days to complete. This delay creates a critical gap between disaster occurrence and informed response, costing lives and resources. Our team at CMU's Software Engineering Institute (SEI) presents a modernized, cloud-based platform that enables domain experts and mission users to deploy custom computer vision models for damage assessment within minutes rather than days, significantly accelerating response times across multiple Areas of Responsibility (AORs).
Our approach addresses a fundamental challenge in operational disaster response: the need to quickly adapt pre-trained models to new, out-of-distribution imagery from emerging incidents with diverse geographic, structural, and disaster characteristics. CMU SEI previously co-hosted the xView2 Prize Challenge and deployed machine learning solutions for EUCOM (Turkey & Ukraine, 2022-2024) and CENTCOM (Syria 2023). However, we observed critical limitations regarding fragmented workflows for model training and inferencing. Our platform leverages an image segmentation base model pre-trained on satellite imagery to predict damage classes across disaster types. When users encounter new scenarios, they upload imagery and rapidly train lightweight model adapters using LoRA (Low-Rank Adaptation), a Parameter-Efficient Fine-Tuning technique requiring limited computational resources and training data.
The adapter-based architecture provides several operational advantages: trained adapters can be reused across similar scenarios, fine-tuned for evolving situations, and shared across organizations. Domain experts can rapidly customize models for their specific AORs without deep machine learning expertise, democratizing access to advanced AI capabilities.
Our platform's cloud-agnostic architecture enables deployment on both commercial and government cloud environments and on-premises infrastructure, operating seamlessly from tactical edge environments to strategic operations centers, enabling cross-echelon collaboration. This infrastructure-agnostic design ensures damage assessment capabilities remain available regardless of network constraints or deployment environment, making it a force multiplier for operations where interoperability and rapid information sharing are paramount.
Preliminary results demonstrate that providing limited (2-16) labels to post-disaster satellite imagery yields substantial performance improvements over the base model, with low computational costs measured in minutes of GPU time. This efficiency is critical for first-wave response scenarios where every minute impacts mission outcomes. By enabling iterative refinement as new imagery arrives, our platform empowers Battle Damage Assessment to make data-driven resource allocation decisions at the speed required by modern HADR operations.

poster

### Welcome to the 10th annual workshop on Naval Applications of Machine Learning!

NAML is the premier annual event showcasing current machine learning research relevant to Naval applications. We welcome participation from the defense community, industry, and academia. The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team. 
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The 10th Annual Workshop on Naval Applications of Machine Learning (NAML 2026) was held in San Diego, California, March 2-5, 2026. NAML is a forum for interdisciplinary experts from government, industry, and academia to discuss results of ongoing research on theory and applications of machine learning to address some of the most challenging problems faced by our naval forces. 



The Finding Responsive Intelligence in Multimodal Analysis (FRIDAY) project seeks to accelerate the development of artificial general intelligence (AGI) by integrating multiple, complex holonic algorithms into high‑fidelity flight simulators for next‑generation drone training and performance enhancement. Building on a one‑year deep‑analysis effort, FRIDAY proposes a hierarchical control architecture—Human → LLM Manager → LLM Executors—through which mission directives are translated into actionable drone behaviors. 

Central to the effort is an agentic LLM system that orchestrates swarms of specialized LLM‑agents, each tasked with mastering distinct operational capabilities. By grounding these agents in a modular skill set, the system can rapidly configure and deploy drone units tailored to diverse mission profiles. 

If realized, FRIDAY could transform military drone operations: autonomous platforms would dynamically adapt to battlefield conditions, conduct sophisticated reconnaissance, and execute precision tasks with minimal human oversight. The modular skill framework enables swift fielding of specialized drone formations for applications ranging from search‑and‑rescue and persistent surveillance to target acquisition and kinetic engagement. 

This work outlines the conceptual design, simulation‑based validation strategy, and anticipated operational benefits, offering a roadmap for leveraging AGI‑driven autonomy especially in an offline manner.

Finding Responsive Intelligence in Multimodal Analysis (FRIDAY) project

As Naval forces increasingly operate in contested, disconnected, intermittent, and limited environments, reliance on cloud-tethered Artificial Intelligence becomes a critical vulnerability. Large Language Models and Generative AI offer clear value for predictive maintenance, technical document synthesis, and decision support, but most current architectures depend on reach-back to hyper-scale data centers. This dependency is not viable for vessels operating under EMCON or in bandwidth-denied theaters.

This presentation examines the architectural and operational considerations for deploying sovereign AI at the tactical edge. The focus is the convergence of ruggedized compute and air-gapped Generative AI models running entirely within the shipboard perimeter.

We introduce a reference architecture for a disconnected maintenance advisor. The system uses Retrieval-Augmented Generation to ingest large volumes of unclassified and CUI technical manuals. By hosting the embedding models, vector database, and inference engine locally on edge hardware, operators can query complex repair procedures and receive context-aware citations without emitting traffic or relying on satellite links.

A key element is the methodology used to validate these systems before deployment. The discussion covers how edge AI stacks can be stressed against simulated tactical constraints and the technical challenges that emerge during integration, including:
Inference Optimization: Balancing model quantization and accuracy to fit efficient LLMs within tactical hardware limits.
Multimodal Ingestion: Using on-premise OCR and vision models to convert analog or handwritten maintenance logs into digital inputs.
Hallucination Control: Enforcing strict RAG patterns that require the model to cite specific technical manuals, reducing the risk of generative error in critical repair workflows.

This session focuses on the practical implementation of sovereign, self-contained AI capabilities within disconnected environments. By pairing edge-ready infrastructure with rigorous validation practices, Naval forces can reduce mean time to repair and maintain information advantage regardless of connectivity conditions.

Deploying Disconnected Generative AI for Naval Maintenance and Decision Superiority

In many areas of the world, changes in tactical threat capabilities have significantly increased the threat to U.S. and allied forces. Our ability to rapidly acquire, provide high confidence target identification, track, and estimate increasingly challenging, dynamic targets in high intensity environments using EO, RF, and EW sensors is critical to mission effectiveness.
Liquid AI is a new class of time-integrative neural networks from dynamical systems theory that are ideally suited to continuous-time data analysis. Unlike traditional feed-forward neural networks, Liquid AI learns complex spatiotemporal patterns using high lateral interconnectivity. Data from position encoders, velocity and accelerometers (found on UAVs), LADAR, 3D imaging (found in manufacturing processes), Dynamic Vision Sensor (DVS or event cameras), and time-varying signals in the audio to higher radar frequencies are aptly suitable for Liquid AI signal processing. 
Results from 2025 Raytheon Internal Research and Development (IRaD) on Liquid AI showed promising results open-source data. A solution was then developed using Raytheon-proprietary data. It was shown to outperform an industry standard (i.e., hand-crafted algorithms) as well as a current neural network solution, using 4-6X fewer parameters. In the literature, Liquid AI uses up to 62X fewer parameters, making it ideal for low-SWaP high throughput applications.
In this presentation, we present the different types of Liquid AI, including Liquid Time-Constant (LTC) networks, Neural ODE (Ordinary Differential Equations), and Continuous-time Recurrent Neural Networks (CTRNN). We then explain various machine learning approaches, such as backpropagation-of-errors, surrogate modeling & translation, and ODE hyper-parameter tuning. We outline our technical approach and show comparative results from the 2025 IRaD. Finally, we present a generalized roadmap showing what types of problems are applicable to novel Liquid AI solutions.


Spatiotemporal Processing with Liquid AI

This project explores lessons learned in designing, prototyping, and evaluating AI agents for intelligence analysis for a cyber defense use case. Despite growing interest, there remains little practical guidance on how to design and build AI agents, particularly guidance grounded in real-world implementations rather than vendor marketing. This project investigates the unique challenges of workflow design and agent evaluation, especially for novel or evolving processes.

Agentic AI systems including AI agents leverage augmented Large Language Models (LLMs) to autonomously complete tasks, workflows, or make decisions. Augmented LLMs are LLMs equipped with tools, access to memory, and retrieval capabilities. Using the capabilities of an augmented LLM, agents can autonomously search for information by generating their own queries, can select and use tools to complete specific actions, and can store and recall information to build context and improve performance.

Overall, AI agents have the potential to increase speed and scale, which may mean freeing up humans to handle more challenging or creative tasks. However, AI agents also present unique challenges, in that they are non-deterministic systems, meaning they can produce stochastic, and in some cases, incorrect, results. Furthermore, AI agents can rely on many Machine Learning (ML) components, increasing the chance of cascading errors.

This work presents lessons learned about the opportunities and challenges related to building AI agents. This work centers around a case study in which the team designed, prototyped, and evaluated two AI agents, built from scratch in LangGraph, for a cyber operations use case. The team worked closely with a Subject Matter Expert (SME) to design and refine the agents.

The first agent defines Priority Intelligence Requirements (PIRs) based on inputs such as the ACARE-I threat actors, a terrain map, and commanders' intent. The second agent then takes the same inputs, along with the newly created PIRs, to identify associated MITRE ATT&CK Tactics, Techniques, and Data Sources that a cyber team will need to be aware of for their upcoming mission.

Through this work, the team developed a set of practical recommendations for teams looking to build AI agents for mission critical use cases. These recommendations range from guidance on incorporating traceability into agents during early design, to implementing agents modularly throughout development and experimentation. These lessons learned are intended to help guide future agentic AI projects in developing agents iteratively with stakeholders, designing agents in a manner that is useful and user-centered, and identifying where traditional software engineering and ML practices are still required. This work presents actionable guidance informed by operator experience to support effective adoption by government organizations such as the Department of War (DoW).

Agentic AI Unpacked: Design and Engineering Lessons from a Mission-Focused Case Study

Reliable vehicle tracking from remote sensing data often requires robust state estimation 
across widely varying temporal resolutions. In this work, we propose a physics-informed 
attentive neural process (PI-AttNP) for multi-scale vehicle tracking that explicitly integrates 
simplified kinematic models as structural priors within a neural process framework. This 
design enforces adherence to physical motion laws while reducing error and accelerating 
convergence. The model leverages time-aware attention weights to capture statistical 
dependencies across irregularly spaced observations and employs split conformal 
prediction to provide distribution-free uncertainty guarantees. We evaluate the PI-AttNP on 
two distinct remote sensing applications—aircraft tracking using ADS-B data and ship 
trajectory prediction using AIS data—spanning sampling intervals from one minute to one 
day. Results demonstrate that PI-AttNP achieves competitive or superior performance 
compared to state-of-the-art baselines while offering principled confidence measures. 
These findings highlight the potential of physics-informed neural processes as a general 
and reliable tool for uncertainty-aware vehicle tracking in remote sensing.


State estimation using physics informed attentive neural processes for target tracking

This paper presents a novel approach aimed at enhancing Machine Learning (ML) algorithms for object recognition. Improvements in ML model performance are often attributed to both the quality of data and tuning of the model. However, determining which factor in object appearance is responsible for the improvement can be challenging. In contrast to a large number of works that are devoted to model improvements, we are focusing on properties in data which could help to isolate the impact of intrinsic object characteristics (such as shape) on the performance of ML models. The project employs three sources of data: collected data from aerial platforms, synthetic images generated by rendering CAD models, and lab-acquired images of 3D printed scale models from exactly the same CAD models. The project involves collecting various factors, such as pose, articulation, light direction, background color and shape, scale, brightness, contrast, color, pattern, shape/geometry, and reflectivity/propagation/diffusion (physics). The goal is not to evaluate the model's absolute performance, but to analyze changes in performance for general ML models or ways to improve the effectiveness of the dataset for training. By training a large ensemble of pre-trained neural networks with diverse multi-source datasets and testing on real-world collected images, we analyze performance changes and identify strategies to improve dataset effectiveness for training.
An initial large ensemble of neural networks trained on target images (>200,000 chips) with various multi-source datasets and tested on collected real objects (>50,000 tests). Our results demonstrate that even with limited real-world imagery, multi-source datasets significantly improve object recognition performance in untuned ML models. This approach, which enables adjusting recognition to object shape rather than overall appearance, highlights the critical role of diverse data sources in enhancing ML object recognition capabilities.

Machine Learning Enhancement Using Data Sets Variety

Real world problems encounter distribution drift, requiring models to be adaptable through time. However, many existing architectures remain black-box in nature, limiting trust and usability. In this paper, we address continuous distribution drift in both classification and regression settings by enforcing a Gaussian Process over the weights of a neural network across time. The architecture is optimized with second order optimization methods, employing multiple Hessian approximations. These approximations serve as precision matrices for multivariate normal distributions over layer weights, enabling sampling based-confidence intervals for each layer. Furthermore, we provide a basis function interpolation framework that models weight trajectories between temporal knots, substantially reducing computational cost while maintaining comparable predictive performance. Our framework applies to arbitrary neural network architectures, and we demonstrate its effectiveness on benchmark datasets, achieving performance competitive with state of the art methods.

A Bayesian Approach to Drift-Aware Parameter Modeling

Power system faults leading to outages and equipment failure are detrimental to ships and naval bases. While there are data recorders being installed in several key areas, these require an engineer to collect the raw data, process it, and analyze it to determine the cause of the fault. A Large Language Model (LLM) will be trained on MIL-STD-1399 (Interface Standard for Shipboard Systems) specifications. This standard defines the power quality requirements for ship power systems at different voltage levels. The LLM will utilize tools to interact with processed data and provides an explanation of the power system faults based on the MIL-STD-1399 requirement being violated. This would allow end users who do not have a background in data processing to be able to locally analyze all the data being recorded, saving months of lead time for an engineer to just get the raw data. With the nature of where these data recorders are being installed, the LLM will need to operate on the edge without internet access. Therefore, the LLM needs to be lightweight enough to run on a small form factor computing device like the NVIDIA Jetson Thor. The Thor device will be connected to the storage used by the data collection system and host a pipeline for data processing. The LLM will then use tools to query a database of processed data to check for MIL-STD 1399 violations and generate plots of the violations if needed. This system will allow for significantly faster data analysis of power system failures that can be accessed anywhere electronics are permitted to operate, and more importantly, in an isolated network environment.

Artificial Intelligence for Data Acquisition System

Abstract: Optimizing communications resources in contested environments imposes a high cognitive load on tactical operators and planners, who must simultaneously consider physical connectivity (Carrier-to-Noise Ratio), operational stealth (Low Probability of Detection), doctrinal compliance (EMCON), and emergent situations. While Language Models (LMs) offer promising semantic inference capabilities for decision support, their stochastic nature often leads to "Constraint Collapse"—the failure to adhere to rigid physical laws and safety protocols in high-stakes single-step decision spaces. We propose a decision support system that considers high-fidelity physics-based simulations, EMCON, commander’s intent, and ad-hoc situations prompted by the user.

We developed a high-fidelity simulation modeling ground vehicles traversing mountainous terrain, generating a dataset of 13 discrete antenna pattern and power configurations. We evaluated these options using a suite of LMs ranging from 8B parameters to GPT-4o, benchmarking them against a deterministic rule-based engine and human expert decisions. We emphasize the utility of small LMs, as contested environments often necessitate deployment on resource-constrained edge devices.

Our results indicate that while LMs face known challenges, their performance in tactical environments is highly context-dependent. Specifically, we find that off-the-shelf models struggle significantly with numerical reasoning and order bias, when compared to general benchmarks. However, we demonstrate that large LMs can achieve performance comparable to human experts, and crucially, that small LMs can match this performance when supported by a neuro-symbolic architecture that incorporates rule-guided reasoning and verifiers.

2B Models Outperform 72B: Neuro-Symbolic Decision Support for Tactical Comms

Advances in generative AI, large language models (LLMs), and multi-agent systems are reshaping the defense landscape, creating a new opportunity for the U.S. Navy to rapidly accelerate the development of scenario-learning technologies that can adapt to emerging situations, expose hidden biases, and support high-consequence decision-making. However, most naval AI programs face a bottleneck: the accreditation and deployment of full-scale, multimodal AI architectures at high classification levels (IL6 and above) often requires lengthy preparation, extensive data governance design, and strict infrastructure controls. This presentation proposes a complementary strategic approach—one that delivers meaningful capabilities early while paving the way for a full envisioned solution.

The method centers on rapid prototyping using Python and AutoGen, executed initially at IL4 for demonstration, yet engineered for immediate portability into other IL6+ environments. This rapid-prototyping approach is not intended to represent an entire AI solution architecture, but rather to embody one essential slice of it: the cognitive, interactive, human-AI collaboration layer that enables adaptive scenario learning, counterfactual analysis, and explainable decision support. This human-AI interface is where operators, analysts, and planners will most directly encounter the system—making it an ideal starting point for experimentation, evaluation, and early capability delivery.

References
Black, S. E. (2024). Mastering the digital art of war: Developing intelligent combat simulation agents for wargaming using hierarchical reinforcement learning. Naval Postgraduate School. 
Black, S. E., & Darken, C. (2023). Scaling intelligent agents in combat simulations for wargaming. Naval Postgraduate School. 
Chen, Y., & Chu, S. (2024). Large language models in wargaming: Methodology, application, and robustness. 
Han, W., Zhang, B., Wang, Q., Luo, J., Ran, W., & Xu, Y. (2019). A multi-agent based intelligent training system for unmanned surface vehicles. Applied Sciences, 9(6), 1089. 
Vasankari, L. (2023). Multi-agent reinforcement learning for littoral naval warfare. Aalto University. 
Weller, D., Meltschack, M., & Schwindling, D. (2024). Leveraging large language models for enhanced wargaming in multi-domain operations. NATO STO.


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