United States

Biofilms form when ship hulls encounter naturally occurring microbial communities in marine waters. As these biofilms mature, their structure and microbiome evolve into a complex, self-sustaining microenvironment capable of supporting larger organisms such as barnacles and mussels. Environmental conditions (e.g., salinity, temperature, and water flow) strongly influence both the microscopic and macroscopic characteristics of developing biofilms. Because of this, biofilm composition can serve as a valuable indicator of geolocation. 

Raman microscopy provides a powerful means of examining biofilm microstructure by capturing vibrational signatures of molecular constituents. This technique yields non-destructive spectral fingerprints that are sensitive to spatial and developmental variations within biofilms, enabling precise identification. Traditional machine-learning approaches perform well on Raman data, allowing classification based on species composition, biofilm age, and responses to chemical or pharmacological stimuli. 

In this work, we present a method for determining environmental origin by distinguishing freshwater-derived biofilms from those grown in marine conditions. Although certain Raman spectral differences caused by salinity are known, the effectiveness of deep learning in detecting these subtle biochemical shifts remains largely unexplored. To investigate this, biofilms are being cultivated in both freshwater and seawater systems, with a target of 1,000 samples per condition. Each sample will be imaged, averaged to improve signal-to-noise ratio, and minimally preprocessed using baseline correction, smoothing, normalization, and cosmic-ray removal. The resulting spectra will be analyzed using a shallow 1D convolutional neural network to perform binary classification between freshwater and saltwater origins. Outcomes from this study will advance our broader goal of geolocating vessels based on biofilm signatures, with future plans to incorporate additional ecological influences on biofilm development. 



NAML 2026

Classifying Freshwater and Marine Biofilms Using Raman Spectroscopy

biofilms

spectroscopy

deep learning

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### Welcome to the 10th annual workshop on Naval Applications of Machine Learning!

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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. 



This work covers the continued development of a concentration estimator for a contaminant plume generated by an unknown source in an underwater domain. The plant includes a stationary or mobile source, and the resulting plume is described using the 3D advection-diffusion equation. An autonomous mobile sensor moves through the plume and uses a process-state hybrid estimator to make real-time predictions of the plume from measurements of the concentration. The plant and estimator are discretized using a Non-Overlapping Domain Decomposition Explicit Finite Volume Method (NODDE-FVM) with constant time 4th order Runge-Kutta integration. Additionally, a novel method for measuring the gradient of a scalar field with a single mobile sensor is presented. This method is utilized by the mobile sensor to make estimations of the gradient of the plume without any information required from other sensors that may be present in the domain. This gradient evaluation method is based on a Taylor expansion of five or more scalar field measurements taken by the sensor along a 3D trajectory. The gradient results depend on both the conditioning of the resulting matrix equation and the error resulting from a Taylor series approximation of the field. Ongoing development of this work involves parameter estimation of the plume, focusing on source strength/release rate and localization in real time. This parameter estimation will follow similar work in implementing a neural-network framework to solve inverse problems for PDEs. In particular, we will utilize a physics-informed foundation to train a network on a general set of advective-diffusive scalar fields. This estimation scheme will be applied in tandem with the previously developed concentration estimator to complete a system that can estimate in real time the size, strength, and source location of a plume of contaminants in an underwater domain.

Real-Time Underwater Plume Estimation with a Mobile Sensor

Artificial intelligence models are increasingly integrated into naval systems for navigation, surveillance, decision support etc. These models can be targeted by adversarial attacks that can cause misclassifications. They also must perform under changing or degraded conditions such as lighting variation and changes in environment. In scenarios where AI is relied on for situational awareness and threat detection, the systems must continue to operate and provide credible outputs even in the presence of deliberate disruption.

This presentation describes a real-time defense and monitoring framework that keeps AI models detecting objects and targets under adversarial or degraded conditions while also flagging deliberate attacks. The system combines lightweight runtime analytics that track feature and confidence consistency across frames, anomaly detection that recognizes attack patterns in camera inputs, and analysis that separates adversarial interference from environmental noise. Our NAML 2025 presentation focused on fine-tuning models for improved robustness prior to deployment; this year’s work introduces an at-runtime defense that allows models to detect attacks in real-time.

Our team developed this work to meet the evaluation objectives of the Robust Artificial Intelligence Test Event (RAITE), a red-versus-blue exercise led by NSWC Crane that assesses AI robustness against cyber and physical threats. RAITE is scheduled for March 7-10, and internal evaluations aligned with its scenarios show that the system sustains object detection performance and identifies ongoing interference.

Our talk will share lessons learned in detecting and mitigating attacks on computer vision models, showing how to achieve real-time resilience without fine-tuning or major model changes. We will also discuss applications for shipboard surveillance, autonomous sensing, and expeditionary intelligence, surveillance, and reconnaissance (ISR) systems. All content is unclassified.

Layer-Wise Runtime Detection of Adversarial Attacks in Operational AI Systems

Automatic Target Recognition (ATR) tasks increasingly require multi-class identification of unmanned aerial vehicles (UAVs). However, existing computer vision training datasets contain only a few different types of airframes and are limited in geometric, lighting, and sensor diversity. Furthermore, most existing datasets provide only a single class label. As a result, when a new or custom UAV model must be identified — one that existing datasets do not contain — one must generate training datasets containing those models. Real-world data collection is typically prohibitive in terms of cost and effort. 
To address this, we present SYGARD (Synthetic Data Generator for Automatic Target Recognition, Identification, and Detection), a synthetic-data tool that enables fine-grained UAV classification and identification by ingesting a 3D model of any UAV and generating large volumes of labeled, photorealistic, geospatially grounded imagery under diverse environmental and sensor conditions. SYGARD integrates Unreal Engine 5 rendering, AirSim-based dynamics, Cesium geospatial environments, and automated ground truth generation within a modular workflow for mission design, camera configuration, multi-agent simulation, and real-to-sim scenario reconstruction to produce synthetic data that reflects realistic deployment conditions.
Using these capabilities, we validate SYGARD on an end-to-end identification task involving a custom fixed-wing UAV platform and a quadcopter: synthetic data generated from their 3D models is used to train a YOLO-based classifier that reliably distinguishes the custom airframe in real flight footage without any real training images. In addition, we show that integrating SYGARD data with real imagery through hybrid training improves cross-dataset UAV detection. SYGARD offers a practical and scalable foundation for future-ready ATR systems.

SYGARD: Synthetic Data Generator for Automatic Target Recognition, Identification, and Detection

Large Reasoning Models (LRMs) demonstrate strong performance on complex reasoning tasks, yet they often suffer from overthinking—producing excessively long chain-of-thought (CoT) traces that increase inference cost and may degrade accuracy. Our analysis reveals a clear anti-correlation between reasoning length and accuracy, where across multiple stochastic decodes, the short reasoning paths consistently achieve the highest correctness, while longer ones accumulate errors and repetitions. These short optimal reasoning paths can be found ideally through full enumeration of the reasoning space. However, the tree-structured reasoning space grows exponentially with sequence length, rendering exhaustive exploration infeasible. To address this, we propose DTS, a model-agnostic decoding framework that sketches the reasoning space by selectively branching at high-entropy tokens and applies early stopping to select the shortest completed reasoning path. This approach approximates the optimal solution that enhances both efficiency and accuracy, without requiring additional training or supervision. Experiments on AIME2024 and AIME2025 datasets with DeepSeek-R1-Distill-Qwen-7B and 1.5B show that DTS improves accuracy by up to 8%, reduces average reasoning length by 23%, and decreases repetition frequency by 12%, demonstrating DTS’s ability for scalable and efficient LRM reasoning.

DTS: Enhancing Large Reasoning Models via Decoding Tree Sketching

Subject: 

Creation of a Multidimensional Multimodal Anomaly Detection with Interpretability (MADI) model using in-flight sensor data to augment fault prediction and fleet health monitoring predictive maintenance modeling efforts for the F-15E aircraft. 



Abstract:  

This work presents an end-to-end MADI prototype developed to enhance anomaly detection capabilities using F-15E Flight Data. Flight data was ingested and transformed within a specialized preprocessing pipeline designed to stabilize raw flight data, align disparate recordings, and support a repeatable anomaly detection framework. To address the absence of labeled fault data in operational settings, synthetic anomalies were generated using a uniform negative-sampling procedure, enabling supervised model training without dependence on historical failure events. 

A custom deep neural network was trained and tuned using a reproducible MLOps framework with hyperparameter optimization and automated experiment tracking. The model produces per-timestamp anomaly probabilities and applies integrated-gradients attribution to identify which sensor channels most strongly contribute to each detected anomaly. This approach treats the ensemble state of all sensors simultaneously and enables detection of complex feature-cross anomalies missed by univariate and rule-based algorithms. These outputs are aggregated into sortie-level metrics that summarize anomaly prevalence, severity, and subsystem-level contribution patterns. Results are integrated into a Historical Maintenance Timeline to support alignment with flight activity, maintenance actions, and readiness events. This prototype demonstrates a scalable, explainable, and operationally relevant anomaly detection capability for fighter aircraft sensor data. The framework supports DoD objectives in condition-based maintenance, fleet health monitoring, and data-driven sustainment planning, and establishes a foundation for future enhancements including anomaly-sequence contraction, event correlation, subsystem mapping, and operational-mode-aware anomaly models.

MADI-Based Anomaly Detection to Enhance Predictive Maintenance for the F-15E

The Results-Aligned Performance Improvement & Development (RAPID) training system transforms training content creation by seamlessly blending artificial intelligence with human expertise. This innovative solution enables Naval commands to quickly create and update training materials when operational procedures and Naval policies change, ensuring fleet readiness. Our presentation will demonstrate a clickable prototype showcasing how RAPID can be deployed for critical naval training needs such as shipboard damage control procedures, tactical operations, and maintenance protocols where accuracy and compliance with Navy standards are paramount. Attendees will observe how instructional systems designers (ISDs) configure parameters, upload Navy-specific documentation, and generate draft content that both ISDs and subject matter experts (SMEs) then review and refine. RAPID stands apart from competitors in five key ways: 1) Unlike commercial AI solutions requiring cloud connectivity, RAPID can be rapidly deployed within secure networks, with no external dependencies, making it ideal for fleet-wide deployment. 2) While other systems focus on automating content creation, RAPID’s human-in-the-loop workflow preserves the ISD’s and SME’s expertise while ensuring critical human oversight. 3) The system’s parameter pack assistant allows users to customize and standardize the output logic and format of AI-generated content in compliance with Navy training standards, learning taxonomies, and domain-specific constraints across different learning contexts. 4) RAPID employs a specialized retrieval-augmented generation (RAG) architecture that can index Navy-specific documentation, ensuring all generated training content maintains fidelity to authorized naval sources and terminology. 5) The system's AI validation engine quantitatively measures content against customer-defined standards using proprietary algorithms for coverage (ensuring all objectives are addressed), groundedness (verifying factual accuracy), congruency (confirming alignment between objectives, content, and assessments), and measurability (ensuring outcomes can be properly evaluated).

RAPID Training System

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.

Computer Vision for Accelerating HADR

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.


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