Singapore

The fusion of Spiking Neural Networks (SNNs) and Transformers holds significant promise for energy-efficient on-device Al, particularly for Synthetic Aperture Radar (SAR) imagery in resource-constrained environments like satellites. However, existing models are not optimized for SAR&#39;s unique statistical properties, such as speckle noise, and suffer from computational inefficiency due to static inference graphs. To address this, we propose SAR-Spikingformer, a lightweight SNN Transformer. Our model introduces two key contributions: 1) a domain-specific data augmentation strategy to enhance robustness against SAR-specific noise and geometric variations, and 2) a dynamic &#39;Block-Halting&#39; mechanism that adapts computational depth based on input difficulty, minimizing inference costs. Our results on the MSTAR and EuroSAT datasets show significant energy reductions of up to 27.76% and 46.53%, respectively, without compromising classification accuracy.

AAAI 2026

Adaptive Spiking Transformer for SAR Image Classification

energy-efficient ai

block-halting

sar image classification

spiking transformer

dynamic inference

speckle noise

early exit

data augmentation

Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop.

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.

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

To build and strengthen collaborations within the defense research community


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NAML 2022

Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop. 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.

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

To build and strengthen collaborations within the defense research community



The fusion of Spiking Neural Networks (SNNs) and Transformers holds significant promise for energy-efficient on-device Al, particularly for Synthetic Aperture Radar (SAR) imagery in resource-constrained environments like satellites. However, existing models are not optimized for SAR's unique statistical properties, such as speckle noise, and suffer from computational inefficiency due to static inference graphs. To address this, we propose SAR-Spikingformer, a lightweight SNN Transformer. Our model introduces two key contributions: 1) a domain-specific data augmentation strategy to enhance robustness against SAR-specific noise and geometric variations, and 2) a dynamic 'Block-Halting' mechanism that adapts computational depth based on input difficulty, minimizing inference costs. Our results on the MSTAR and EuroSAT datasets show significant energy reductions of up to 27.76% and 46.53%, respectively, without compromising classification accuracy.

workshop 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.

Spiking Neural Networks (SNNs) are promising for energy-efficient computing on next generation neuromorphic hardware (Nunes et al. 2022). However, scaling them to large Transformer architectures introduces challenges such as training instability and high computational costs associated with multiple timesteps (Hu et al. 2024), which can offset their efficiency benefits. To address these issues, this paper proposes Spiking Swin-B, a spiking version of the large-scale Swin-B Transformer, integrated with a Reinforcement Learning (RL)-based meta-controller for dynamic execution. To ensure stable training and prevent performance degradation, we preserve the original attention mechanism of Swin-B, enabling a robust ANN-to-SNN conversion. Furthermore, the RL meta-controller learns to automatically optimize the accuracy-energy trade-off by dynamically adjusting timesteps and early-exit stages based on input complexity. 
Experimental results on CIFAR-100 show that the proposed Spiking Swin-B reduces computational energy by approximately 40% compared to its ANN counterpart, with only a marginal accuracy drop of about 1%. This work demonstrates the feasibility of applying learnable, dynamic control to large-scale spiking Transformers, paving a practical path toward adaptive and energy-aware SNN inference.

Reinforcement-Learned Dynamic Execution for Spiking Swin-B

Many recent zero or few-shot approaches in Information Extraction rely on large-scale annotated corpora for pretraining. However, while manually annotated corpora are scarce, the current methods for performing data augmentation or data generation remain limited, especially for complex tasks such as event extraction. To address this challenge, we introduce a simple yet efficient annotation method for real-world data that leverages the capabilities of Large Language Models (LLMs) to both generate annotations and evaluate their quality. Our method defines confidence scores to identify passages of interest and to select the best generated annotations. Based on this approach, we propose Omnivent, a large-scale and general-purpose event dataset designed for both event detection and event argument extraction. To the best of our knowledge, our dataset offers the largest coverage of event types to date, comprising 38,859 distinct event types and 9,981 argument roles. To demonstrate the effectiveness of our dataset, we also introduce GLEE, a bidirectional transformer model to perform zero-shot event detection and argument extraction. We conducted evaluations across 9 benchmarks and 7 domains showing that pretraining on Omnivent yields better zero-shot performance than pretraining on human-annotated data. Notably, despite being 25x smaller, GLEE outperforms leading LLM-based approaches in zero-shot event detection and achieves up to 50\% of the performance of fully supervised models in event argument extraction.

Annotating Pretraining Corpora in Information Extraction: An Unsupervised Confidence-Guided Approach

Multimodal document retrieval systems have shown strong progress in aligning visual and textual content for semantic search. However, most existing approaches remain heavily English-centric, limiting their effectiveness in multilingual contexts. In this work, we present M3DR (Multilingual Multimodal Document Retrieval) a framework designed to bridge this gap across languages, enabling applicability across diverse linguistic and cultural contexts. M3DR leverages synthetic multilingual document data and generalizes across different vision-language architectures and model sizes, enabling robust cross-lingual and cross-modal alignment. Using contrastive training, our model learns unified representations for text and document images that transfer effectively across languages. We validate this capability on 22 typologically diverse languages, demonstrating consistent performance and adaptability across linguistic and script variations. We further introduce a comprehensive benchmark that captures real-world multilingual scenarios, evaluating models under monolingual, multilingual, and mixed-language settings. Extensive experiments show that M3DR achieves substantial improvements over existing baselines, with over 150\% relative gains on cross-lingual retrieval tasks.

M3DR - Towards Universal Multilingual Multimodal Document Retrieval

Retrieval-Augmented Generation (RAG) has facilitated the construction of domain-specific question–answering (Q&A) systems by integrating information retrieval (IR) mechanisms with large language models (LLMs). However, real-world RAG systems frequently suffer from domain drift In automotive voice assistants, novice users unfamiliar with technical terminology often paraphrase such terms, which prevents dense retrievers from bridging the semantic gap and consequently causes retrieval failures. The rapid proliferation of LLMs has expanded their user base, increasing the demand for non-expert users to access domain-specific knowledge in specialized fields such as healthcare and law, thereby underscoring the significance of this issue. This study investigates a practical approach to addressing the challenge of understanding paraphrastic expressions of technical terminology, using a dataset of over 10,000 Q&A pairs derived from vehicle manuals, and proposes continual pre-training of embedding models as an effective solution. Experimental results demonstrate that continual pre-training effectively imbues embedding models with domain knowledge, enabling them to associate paraphrased expressions with their corresponding technical terms. Although continual pre-training has been extensively studied for language models, its application to retrieval embedding models within RAG systems remains largely underexplored. Our findings offer insights into designing retrieval systems that achieve both domain fidelity and linguistic generalization—two essential prerequisites for scaling RAG to real-world applications.

Continual Pre-training of Dense Retrievers for Bridging Paraphrases and Technical Terms in Domain-Specific Retrieval-Augmented Generation

Locating and retrieving objects from scene-level point clouds is a challenging problem with broad applications in robotics and augmented reality. This task is commonly formulated as open-vocabulary 3D instance segmentation. Although recent methods demonstrate strong performance, they depend heavily on SAM and CLIP to generate and classify 3D instance masks from images accompanying the point cloud, leading to substantial computational overhead and slow processing that limit their deployment in real-world settings. Open-YOLO 3D alleviates this issue by using a real-time 2D detector to classify class-agnostic masks produced directly from the point cloud by a pretrained 3D segmenter, eliminating the need for SAM and CLIP and significantly reducing inference time. However, Open-YOLO 3D often fails to generalize to object categories that appear infrequently in the 3D training data. In this paper, we propose a method that generates 3D instance masks for novel objects from RGB images guided by a 2D open-vocabulary detector. Our approach inherits the 2D detector’s ability to recognize novel objects while maintaining efficient classification, enabling fast and accurate retrieval of rare instances from open-ended text queries.

Retrieving Objects from 3D Scenes with Box-Guided Open-Vocabulary Instance Segmentation

The increasing diversity and scale of video data demand retrieval systems capable of multimodal understanding, adaptive reasoning, and domain-specific knowledge integration. This paper presents LLandMark, a modular multi-agent framework for landmark-aware multimodal video retrieval to handle real-world complex queries. The framework features specialized agents that collaborate across four stages: query parsing and planning, landmark reasoning, multimodal retrieval, and reranked answer synthesis. A key component, the Landmark Knowledge Agent, detects cultural or spatial landmarks and reformulates them into descriptive visual prompts, enhancing CLIP-based semantic matching for Vietnamese scenes. To expand capabilities, we introduce an LLM-assisted image-to-image pipeline, where a large language model (Gemini 2.5 Flash) autonomously detects landmarks, generates image search queries, retrieves representative images, and performs CLIP-based visual similarity matching, removing the need for manual image input. In addition, an OCR refinement module leveraging Gemini and LlamaIndex improves Vietnamese text recognition. Experimental results show that LLandMark achieves adaptive, culturally grounded, and explainable retrieval performance. Our code will be released soon.

LLandMark: A Multi-Agent Framework for Landmark-Aware Multimodal Interactive Video Retrieval

Modern generative pre-trained language models excel at open-ended text generation, yet continue to underperform on structure-related tasks such as NER, relation extraction, and semantic role labeling, especially when compared to encoder-only models of similar sizes. While this gap has been attributed to limited structure knowledge, we hypothesize this is also due to the missing connection between the model’s internal representations of linguistic structure and the output space used during supervised fine-tuning. We propose the Structured Language Generation Model (SLGM), a model- and task-agnostic framework that reformulates structured prediction as a classification problem through three components: (1) reinforced input formatting with structural cues, (2) loss design, and (3) format-aware decoding that constrains generation to task-valid outputs. Across 5 tasks and 13 datasets, SLGM substantially improves structure prediction without relying on dataset-specific engineering or additional model parameters. It outperforms baseline fine-tuning on models of the same size, achieves comparable performance to much larger models when used with $<$1B parameter models, and acts as a zero-weight adapter that reproduces the benefits of dataset-specific fine-tuning in low-resource settings.

Structured Language Generation Model: Loss Calibration and Formatted Decoding for Robust Structure Prediction

Optimizing Retrieval-Augmented Generation (RAG) configurations for specific tasks is a complex and resource-intensive challenge. Motivated by this challenge, frameworks for RAG hyper-parameter optimization (HPO) have recently emerged, yet their effectiveness has not been rigorously benchmarked. To fill this gap, we present a comprehensive study involving five HPO algorithms over five datasets from diverse domains, including a newly curated one on real-world product documentation. Our study explores the largest HPO search space considered to date, with three evaluation metrics as optimization targets. Analysis of the results shows that RAG HPO can be done efficiently, either greedily or with random search, and that it significantly boosts RAG performance for all datasets. For greedy HPO approaches, we show that optimizing model selection first is preferable to the common practice of following the RAG pipeline order during optimization.

An Analysis of Hyper-Parameter Optimization Methods for Retrieval Augmented Generation

We present Fisher-LD, a layer-wise knowledge distillation framework for transformer-based recommenders. Fisher-LD uses the Fisher Information Matrix to quantify per-layer importance, allocating supervision where it matters most. On MovieLens-1M, our 6-layer student achieves HR@10=0.978 with 3.3× compression and 3.2× speedup, exceeding the 12-layer teacher (HR@10=0.934) by 4.4 percentage points. Cross-domain validation on Amazon Reviews confirms generalization. Experiments against five baselines show consistent improvements. Our three-phase protocol—Fisher analysis, selective distillation, task fine-tuning—enables effective compression for production LLM-based recommenders. Code will be released.

Fisher-LD: Layer-Wise Knowledge Distillation for LLM Recommenders

jina-reranker-v3 is a 0.6B-parameter multilingual listwise reranker that introduces a novel "last but not late" (LBNL) interaction mechanism. Unlike late interaction models like ColBERT that encode documents separately before multi-vector matching, our approach enables cross-document interactions during encoding by processing queries and all candidate documents simultaneously within shared context windows. We extract contextual embeddings from special tokens at each document's end (the "last" position), but crucially, interactions occur throughout the encoding process ("not late"). The model achieves state-of-the-art BEIR performance with 61.9 nDCG@10 while being significantly smaller than comparable alternatives.

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