Singapore

In recent years, RF fingerprinting (RFF) has emerged as a promising technology for wireless device authentication. 
However, temporal variations in device load and temperature, along with channel effects, lead to inconsistencies in RFF distributions between training and testing phases. 
As a result, deep learning (DL)-based recognition models often suffer from degraded performance. 
To address this problem, we propose the first test-time-adaptation (TTA) approach to improve the domain generalization ability of RFF recognition models. 
We first analyze the causes of time-varying RFF distribution shifts, such as carrier frequency offset (CFO), and develop a physical impairment-based data augmentation strategy.
Based on this, we further propose a physically information-aware prototype to guide the model for TTA.
Our method requires no model retraining or labeled test samples, and is a lightweight, nonparametric solution.
Finally, our approach is extensively evaluated using mobile phones with the IEEE 802.11 orthogonal frequency division multiplexing (OFDM) system, which demonstrates that our scheme can effectively
improve RFF average recognition performance by about 7.8%.

AAAI 2026

RFF-TTA: Physical Information-Aware Prototype for Temporally Varying RF Fingerprinting Online Test-Time-Adaptation

sensor networks & smart cities

app: internet of things

### Welcome to the 8th 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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* To spread awareness of current machine learning research relevant to Naval applications

* To connect machine learning researchers with experts in military requirements

* To build and strengthen collaborations within the defense research community

Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop.

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Katie Rainey, Naval Information Warfare Center Pacific

Nicole Isoda, Naval Information Warfare Center Pacific 

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NAML 2024

The 8th annual workshop on Naval Applications of Machine Learning 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. The goals of NAML are to spread awareness of current machine learning research relevant to Naval applications, to connect machine learning researchers with experts in military requirements and to build and strengthen collaborations within the defense research community.


technical paper

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.

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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.

My research focuses on making LLM training and serving
widely accessible to academia and smaller organizations by
reducing dependence on proprietary data and heavy compute.
In this talk, I will present a coherent framework that
unifies data curation without labels, zero-data
self-evolution, reward calibration for reliable confidence
estimation, and computation-efficient language model
inference.

Breaking the Resource Monopoly: LLM Post-Training and Serving with Modest Data and Compute

Machine learning models are often used to make predictions about the outcomes of applications to selective programs. Many prospective school or college applicants turn to machine learning models to predict whether they will be admitted to a program, and employers may use algorithmic tools to filter out resumes predicted to have a low probability of being hired when offering interviews for a job opening. However, such decision processes differ substantially from the conventional machine learning setting: decisions are not independent across applicants. Whether a student is admitted depends on the other applicants who apply because admissions decisions are \textit{capacity-constrained}. We formalize how the nature of admission decisions results in a data-generating process which is incompatible with traditional machine learning assumptions. We characterize how selection functions properties affect the difficulty of generalization to applicant pool distribution shifts, introducing two concepts: stability, which measures how many existing decisions can change when a single new applicant is introduced; and variability, which measures the number of unique students whose decisions can change. We demonstrate our theory on admissions data from the New York City high school matching system, showing that machine learning performance degrades as the applicant pool increasingly differs from the training data. Furthermore, there are larger performance drops for schools using decision rules that are less stable and more variable. Our work raises questions about the reliability of predicting individual admissions probabilities.

Capacity Constraints Make Admissions Processes Less Predictable

As artificial intelligence (AI) becomes increasingly
integrated into daily life, there is a critical need for
developing AI literacy across all educational levels.
However, current AI education remains largely confined to
college-level computer science classrooms with limited
access for K-12 learners. We present the AI Scholars
Program, a novel approach that addresses the AI education
gap by preparing college computing students to serve as AI
education ambassadors in their communities and empowering
K-12 teachers to adopt AI education practices in their
classrooms. This experience report presents the curriculum
and its outcomes after one round of refinement. The program
offers structured AI learning through bi-weekly webinars,
resources, and collaborative opportunities to form teams
and conduct community outreach projects. Our program
invited 63 scholars from 30 institutions across the U.S.,
including 51 college students and 12 K-12 teachers. Their
outreach impacted over 230 K-12 learners. We examine
program outcomes for participants and projects through
pre/post surveys measuring computing attitudes and
self-efficacy for teaching AI, scholar interviews, and
outreach project reports. We share lessons learned and
challenges for designing similar programs, highlighting the
importance of involving educators for effective
community-engaged AI education. The program creates a
sustainable pipeline for college students to develop
technical skills and leadership while addressing K-12 AI
education shortages. We contribute insights for scaling AI
literacy and broadening participation in computing.

AI Scholars Program: Scaling AI Literacy Through K-12 Outreach

Diffusion models have advanced fine-grained garment generation, yet balancing controllability, efficiency, and texture fidelity remains challenging. Adapter-based methods often yield incoherent details, while full fine-tuning is computationally expensive and prone to overwriting pretrained priors.
To address these limitations, we propose IMAGGarment+, an efficient diffusion framework for controllable and high-quality garment synthesis. It comprises two key modules designed for efficient and attribute-aware conditioning.
First, we introduce an attribute-wise feature extractor (AFE) that disentangles key garment attributes, silhouette, logo, position, and color, into parallel latent streams. Each stream is optimized independently via LoRA, ensuring minimal parameter overhead while retaining expressive capacity.
Second, we develop an attribute-adaptive attention (AA) module to inject attribute-specific cues into the generative process through a selective, layer-wise injection strategy. Specifically, silhouette and color features are injected into early decoder layers to guide structural and appearance formation, while logo features are propagated across all layers to ensure cross-scale consistency.
Extensive experiments on fine-grained garment benchmarks demonstrate that IMAGGarment+ outperforms state-of-the-art baselines with less than 20\% additional parameters, validating its effectiveness and efficiency.

IMAGGarment+: Efficient Attribute-Wise Diffusion for Garment Generation

Automatically solving table reasoning tasks remains challenging due to three main factors: (1) diverse and hierarchical table structures that hinder comprehension, (2) the heavy reliance on complex logical and numerical reasoning—which makes purely text-based methods prone to hallucinations—and (3) the necessity of multi-step processing to handle intricate tasks involving multiple and lengthy tables.
To address these challenges, we introduce TaREx, a novel framework that unifies table representation, integrates code-driven execution, and supports interactive multi-step reasoning. TaREx employs a reinforcement learning-based training pipeline to optimize its reasoning policy for complex tasks.
Experimental results show that TaREx achieves state-of-the-art performance across a wide range of table reasoning benchmarks, both in-domain and out-of-domain. These include fundamental tasks such as table question answering (TQA) and table fact verification (TFV), as well as advanced tabular data analysis tasks. The results highlight TaREx’s effectiveness and scalability in advancing automated table reasoning.

TaREx: Reinforcement Learning for Code-Driven Table Reasoning

Structured Electronic Health Record (EHR) data stores patient information in relational tables and plays a central role in clinical decision-making. 
Recent advances have explored the use of large language models (LLMs) to process such data, showing promise across various clinical tasks.
However, the absence of standardized evaluation frameworks and clearly defined tasks makes it difficult to systematically assess and compare LLM performance on structured EHR data.
To address these evaluation challenges, we introduce EHRStruct, a benchmark specifically designed to evaluate LLMs on structured EHR tasks.
EHRStruct defines 11 representative tasks spanning diverse clinical needs and includes 2,200 task-specific evaluation samples derived from two widely used EHR datasets.
We use EHRStruct to evaluate 20 advanced and representative LLMs, covering both general and medical models.
We further analyze key factors influencing model performance, including input formats, few-shot generalisation, and finetuning strategies, and compare results with 11 state-of-the-art LLM-based enhancement
methods for structured data reasoning. 
Our results indicate that many structured EHR tasks place high demands on the understanding and reasoning capabilities of LLMs.
In response, we propose SEMaster, a code-augmented method that achieves state-of-the-art performance and offers practical insights to guide future research.

EHRStruct: A Comprehensive Benchmark Framework for Evaluating Large Language Models on Structured Electronic Health Record Tasks

The Open Digital Rights Language (ODRL) is a pivotal standard for automating data rights management. However, the inherent logical complexity of authorization policies, combined with the scarcity of high-quality ``Natural Language-to-ODRL" training datasets, impedes the ability of current methods to efficiently and accurately translate complex rules from natural language into the ODRL format. To address this challenge, this research leverages the potent comprehension and generation capabilities of Large Language Models (LLMs) to achieve both automation and high fidelity in this translation process. We introduce AgentODRL, a multi-agent system based on an Orchestrator-Workers architecture. The architecture consists of specialized Workers, including a Generator for ODRL policy creation, a Decomposer for breaking down complex use cases, and a Rewriter for simplifying nested logical relationships. The Orchestrator agent dynamically coordinates these Workers, assembling an optimal pathway based on the complexity of the input use case. Specifically, we enhance the ODRL Generator by incorporating a validator-based syntax strategy and a semantic reflection mechanism powered by a LoRA-finetuned model, significantly elevating the quality of the generated policies. Extensive experiments were conducted on a newly constructed dataset comprising 770 use cases of varying complexity, all situated within the context of data spaces. The results, evaluated using ODRL syntax and semantic scores, demonstrate that our proposed Orchestrator-Workers system, enhanced with these strategies, achieves superior performance on the ODRL generation task.

AgentODRL: A Large Language Model-based Multi-agent System for ODRL Generation

Stochastic multi-objective optimization (SMOOP) requires ranking multivariate distributions; yet, most empirical studies perform scalarization, which loses information and is unreliable. Based on the optimal transport theory, we introduce the center-outward $q$-dominance relation and prove it implies strong first-order stochastic dominance (FSD). Also, we develop an empirical test procedure based on $q$-dominance, and derive an explicit sample size threshold, $n^*(\delta)$, to control the Type I error.

We verify the usefulness of our approach in two scenarios: (1) as a ranking method in hyperparameter tuning; (2) as a selection method in multi-objective optimization algorithms. For the former, we analyze the final stochastic Pareto sets of seven multi-objective hyperparameter tuners on the YAHPO-MO benchmark tasks with $q$-dominance, which allows us to compare these tuners when the expected hypervolume indicator (HVI, the most common performance metric) of the Pareto sets becomes indistinguishable. For the latter, we replace the mean value-based selection in the NSGA-II algorithm with $q$-dominance, which shows a superior convergence rate on noise-augmented ZDT benchmark problems.

These results establish center-outward $q$-dominance as a principled, tractable foundation for seeking truly stochastically dominant solutions for SMOOPs.

Center-Outward q-Dominance: A Sample-Computable Proxy for Strong Stochastic Dominance in Stochastic Multi-Objective Optimisation

Interpretability methods have recently gained significant attention, particularly in the context of large language models, enabling insights into linguistic representations, error detection, and model behaviors such as hallucinations and repetitions. However, these techniques remain underexplored in automatic speech recognition (ASR), despite their potential to advance both the performance and interpretability of ASR systems. In this work, we adapt and systematically apply established interpretability methods such as logit lens, linear probing, and activation patching, to examine how acoustic and semantic information evolves across layers in ASR systems. Our experiments reveal previously unknown internal dynamics, including specific encoder-decoder interactions responsible for repetition hallucinations and semantic biases encoded deep within acoustic representations. These insights demonstrate the benefits of extending and applying interpretability techniques to speech recognition, opening promising directions for future research on improving model transparency and robustness.

Beyond Transcription: Mechanistic Interpretability in ASR

This article proposes a Learning-from-Demonstration (LfD)
method using probability densities on the workspaces of
robot manipulators. The method, named
PRobabilistically-Informed Motion Primitives (PRIMP),
learns the probability distribution of the end effector
trajectories in the 6-D workspace that includes both
positions and orientations. It is able to adapt to new
situations such as novel via points with uncertainty and a
change of viewing frame. The method itself is
robot-agnostic, in that the learned distribution can be
transferred to another robot with the adaptation to its
workspace density. Workspace-STOMP, a new version of the
existing STOMP motion planner, is also introduced, which
can be used as a postprocess to improve the performance of
PRIMP and any other reachability-based LfD method. The
combination of PRIMP and Workspace-STOMP can further help
the robot avoid novel obstacles that are not present during
the demonstration process. The proposed methods are
evaluated with several sets of benchmark experiments. PRIMP
runs more than five times faster than existing
state-of-the-art methods while generalizing trajectories
more than twice as close to both the demonstrations and
novel desired poses. They are then combined with our lab's
robot imagination method that learns object affordances,
illustrating the applicability to learn tool use through
physical experiments.

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Breaking the Resource Monopoly: LLM Post-Training and Serving with Modest Data and Compute

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Radio Frequency Fingerprinting in Distorted Electromagnetic Environments

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