Singapore

Transmitting and receiving electromagnetic wave signals reflected back to the ground can detect the structure of sub surface defects. However, the imaging process of ground- penetrating radar (GPR) is highly susceptible to interference from complex underground environments, leading to nonlinear attenuation and noise. This makes it challenging to directly localize and identify defect types from raw reflected radar waveform images. Currently, mainstream methods of manual radar signal gain and filtering heavily rely on expert experience, while common end-to-end generative models are typically designed for optical images. This paper proposes a defect-guided Multi-window Gabor Transform Network (MGT-Net) for GPR B-Scan image reconstruction which achieves automatic gain and defect enhancement of raw GPR signals. Firstly, a Multi-window Gabor Transform Module (MGTM) is designed to effectively represent and extract spatial-frequency features of defects at different locations and of various types. Secondly, a defect guidance network (DG-Net) is constructed to accurately direct the reconstruction of defect areas and enhance the saliency and discriminability of defect features. Additionally, we construct a large-scale GPR B-Scan image dataset (GRD) containing 41,613 images across 7 defect categories. Experimental results show the superior performance of MGT-Net, achieving state-of-the-art (SOTA) SSIM of 81.72% ± 3.5% and PSNR of 30.50 ± 0.442.

AAAI 2026

Multi-Window Gabor Transform Network for Ground Penetrating Radar B-Scan Image Reconstruction

ground penetrating radar

generative adversarial networks (gans) for vision

image reconstruction

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-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
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AAAI 2023

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.

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.

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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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.

Humans easily apply learned skills to different situations,
a flexibility that AI systems still struggle to achieve.
Current AI models are often confined to their training
setup, leading to isolated developments and a narrow scope
of application. This largely restricts the creation of
flexible and general-purpose AI systems. Deep Model Reuse
presents a novel solution. Imagine tapping into a vast
library of pre-trained models, each a master in its
specialized domain. Our approach re-purposes these existing
models, extracting and transforming their knowledge for the
development of novel AI systems. In this talk, we explore
the essential techniques of this transformative process,
highlighting the shift towards versatile and efficient AI
that mirrors human cognition's adaptability.

We introduce three foundational pillars of deep model
reuse: understanding, composing, and refining. First, we
investigate the internal behavior of neural networks—using
language models as explainers and analyzing the
representation space of diffusion models—to uncover how and
what models have learned. Second, we develop methods to
transform and compose models through weight mapping,
knowledge distillation, and model dissection, enabling the
creation of new capabilities by reassembling existing
expertise. Third, we enhance reliability by editing model
behaviors and mitigating biases, ensuring robustness in
complex and dynamic environments.

We demonstrate the power of this paradigm in generative AI,
where model reuse leads to efficient diffusion models free
from spectral bias, improved compositional understanding in
video generation, and the repurposing of 2D/3D models for
3D/4D content creation. By shifting from training from
scratch to intelligently reusing and recombining models, we
move closer to adaptive, scalable, and human-like AI
systems—ushering in a new era of sustainable and general
intelligence.

Deep Model Reuse: Paving the Way for Efficient and Generalizable AI Systems

Indian poetry, known for its linguistic complexity and deep cultural resonance, has a rich and varied heritage spanning thousands of years. However, its layered meanings, cultural allusions, and sophisticated grammatical constructions often pose challenges for comprehension, especially for non-native speakers or readers unfamiliar with its context and language. Despite its cultural significance, existing works on poetry have largely overlooked Indian language poems. In this paper, we propose the $\textbf{Translation and Image Generation (TAI)}$ framework, leveraging Large Language Models (LLMs) and Latent Diffusion Models through appropriate prompt tuning. Our framework supports the United Nations Sustainable Development Goals of Quality Education (SDG 4) and Reduced Inequalities (SDG 10), by enhancing the accessibility of culturally rich Indian-language poetry to a global audience. It includes (1) a translation module that uses an Odds Ratio Preference Alignment Algorithm to accurately translate morphologically rich poetry into English; (2) an image generation module that employs a semantic graph to capture tokens, dependencies, and semantic relationships between metaphors and their meanings, to create visually meaningful representations of Indian poems. Our comprehensive experimental evaluation, including both human and quantitative assessments, demonstrates the superiority of $\textit{TAI}$ Diffusion in poem image generation tasks, outperforming strong baselines. To further address the scarcity of resources for Indian-language poetry, we introduce the $\textbf{Morphologically Rich Indian Language Poems \textit{MorphoVerse} Dataset}$, comprising 1,570 poems across 21 low-resource Indian languages. By addressing the gap in poetry translation and visual comprehension, this work aims to broaden accessibility and enrich the reader’s experience.

Crossing Borders: A Multimodal Challenge for Indian Poetry Translation and Image Generation

Multi-view representation learning, which utilizes multiple channels to improve perceptual accuracy, is recognized for its effectiveness in the analysis of multi-view data. However, deploying these methods in real-world scenarios presents two primary challenges. 1) Lack of Variegation: Multi-view representation techniques commonly observe along a singular axis, i.e., the attribute axis; 2) Insufficient Relationship: Most multi-view feature models lack mechanisms for exploring potential relationships between attribute axis and channel axis. To mitigate these obstacles, we design a Dual Impulse Network framework for multi-view learning (DIN) to train a feature representation. In this framework, a strategy observed along the channel axis and attribute axis simultaneously is introduced, and two different representations are generated by two analogous impulse networks, which are capable of extracting information corresponding to different axes. Furthermore, we incorporate an integration network that analyzes the potential relationship between attribute axis and channel axis to generate two attention matrices. The final two feature representations derived from these attention matrices are aggregated to amplify the expression of internal information. Comprehensive experimental results support the efficacy and superiority of the proposed framework, demonstrating improvements in classification performance compared to state-of-the-art methods.

DIN: Dual Impulse Network for Multi-view Representation Learning

The continuous advancements in life science technology have enabled spatial transcriptome technology to achieve an impressive level of resolution at the single-cell level. This technology has emerged as a crucial method for studying the cellular composition and differentiation states of tissues, investigating cell-cell interactions, and unraveling the molecular mechanisms underlying diseases and developmental processes. A key component in this analysis is the accurate segmentation of cells. However, existing segmentation methods often fail to fully leverage the valuable information provided by spatial transcriptomics, leading to inaccurate cell segmentation. In this study, we introduce SSL-CST, a cell segmentation for single-cell spatial transcriptome method based on self-supervised learning. SSL-CST employs a pre-trained model for foundational contour segmentation. Following the denoising process, it utilizes a self-supervised neural network to correct the cell boundaries to obtain accurate cell boundaries. Through this approach, SSL-CST outperforms other state-of-the-art methods in various tests conducted on multiple datasets. The improved segmentation provided by SSL-CST further enhances the analysis of single-cell spatial expression, providing effective tools for biological discovery.

SSL-CST: Cell Segmentation for Single-Cell Spatial Transcriptome Based on Self-Supervised Learning

Optimizing patent claims is a critical yet challenging task, demanding careful balance between maximizing novelty and preserving legal scope. Manual claim drafting is labor-intensive, costly, and inherently inconsistent, while conventional Large Language Models (LLMs) often lack the structured, iterative reasoning essential for precise claim refinement. To address these challenges, we introduce Tree of Claims (ToC), an innovative framework that redefines claim editing as a guided search problem. ToC synergistically integrates Monte Carlo Tree Search (MCTS) with a collaborative multi-agent system, comprising an LLM-based EditorAgent that proposes contextually grounded edits, and an ExaminerAgent that mimics patent examiner critiques through structured, chain-of-thought analyses of novelty and prior art disclosure. Driven by a carefully designed multi-objective reward function, ToC jointly optimizes novelty, scope retention, and semantic coherence. Experimental evaluation on a benchmark of 1145 claims demonstrates that ToC significantly outperforms standard LLMs in zero-shot and few-shot scenarios, achieving an average composite score improvement of 8%, and up to 9% in certain cases. Extensive experiments, including detailed ablation studies, validate ToC’s efficacy in generating superior, legally robust claim revisions. Overall, ToC establishes a transparent, controllable, and interpretable methodology that effectively bridges advanced LLM reasoning capabilities with strategic MCTS planning for structured patent claim optimization.

ToC: Tree-of-Claims Search with Multi-Agent Language Models

Anti-money laundering (AML) detection is of vital importance in financial risk control. Although Graph Neural Networks (GNN) have yielded promising results, existing motif-based approaches primarily focus on node anomaly detection on simple graphs, which hinders the direct identification of anomalous edges in directed temporal transaction networks. Moreover, consecutive transaction relationships, termed dual-edge motifs, have rarely been considered in previous AML studies. To address these gaps, we propose the D-EMAML framework, which consists of: (1) Fast-Motif-Gen, a GPU-accelerated dual-edge motif graph generator with pruning; (2) D-EMGNN, an attention-enhanced heterogeneous GNN module that reduces motif-type information redundancy; (3) MELP, a label aggregation scheme projecting predictions from the motif graph to the original graph. Extensive experiments on real-world and synthetic datasets demonstrate significant improvements over representative baselines and validate the contribution of each component. To our knowledge, this is the first application of dual-edge motif graphs for GNN-based edge anomaly detection in AML.

Beyond Single Transactions: D-EMAML---Dual-Edge Motif Neural Networks for Enhanced Anti-Money Laundering Detection

This paper presents a novel method, called Deformable
Polygonal Flow Matching (DPFM), for the generation of
polygonal arrangements such as jigsaw puzzles and floor
plans. DPFM is a Flow Matching framework that enables
the generation process to deform, rotate, and translate poly-
gons while decoupling these transformation, allowing to tog-
gle them individually. Able to combine the spatial reason-
ing capabilities of arrangement models with the flexibility
of position-based models, it covers a wide range of appli-
cations within a unified formulation, from noiseless puzzle
solving using rigid alignments to unconstrained floor plans
generation. We represent data using a hierarchical graph com-
posed of a topological subgraph encoding connectivity infor-
mation and semantics (such as room types for floor plans),
and a geometrical subgraph encoding the 1D polygonal loop
of each shape. DPFM also leverages Flow Matching’s arbi-
trary prior distributions for geometric constraints by design-
ing priors with domain knowledge. Rather than starting the
generation process from uninformed distributions, the gener-
ation is constrained through the informed priors since the ini-
tialization stage. The qualitative and quantitative evaluations
of our method, ran on the RPLAN and jigsaw puzzle datasets,
demonstrate strong performance. DPFM outperforms task-
specific methods, becoming the new state-of-the-art for 2D
arrangement generation. As our results show, DPFM is able to
solve novel tasks such as puzzle denoising, where pieces are
reconstructed from noisy versions and arranged into a valid
puzzle in parallel.

Deformable Polygonal Flow Matching with Informed Priors and Hierarchical Graph Constraints

We introduce the Block Rearrangement Problem (BRaP), a challenging component of large warehouse management which involves rearranging storage blocks within dense grids to achieve a target state. We formally define the BRaP as a graph search problem. Building on intuitions from sliding puzzle problems, we propose five search-based solution algorithms, leveraging joint configuration space search, classical planning, multi-agent pathfinding, and expert heuristics. We evaluate the five approaches empirically for plan quality and scalability. Despite the exponential relation between search space size and grid density, our methods demonstrate efficiency in creating rearrangement plans for deeply buried blocks in up to 80x80 grids.

Symbolic Planning and Multi-Agent Path Finding in Extremely Dense Environments with Unassigned Agents

The \textsc{Ride-Sharing Assignment Problem} (AAAI 2018) is a fundamental problem in intelligent transportation systems, urban mobility, and algorithmic decision-making. Given a set of $m$ vehicles with initial locations and $n$ requests ($n \leq mk$), each with a specified origin and destination, the goal is to assign at most $k$ requests to each vehicle and compute corresponding routes that minimize the total travel distance. The algorithmic approach depends on whether $n = mk$ or $n < mk$. In this paper, we present algorithms with provable approximation guarantees for both cases. When $n = mk$, we design a $\min\{\mathcal{O}(\sqrt{k}), \mathcal{O}(\sqrt{\frac{n}{k}})\}$-approximation algorithm, whereas previously the ratio $\mathcal{O}(\sqrt{k})$ was only proved for $k$ being a power of 2. When $n < mk$, we achieve an approximation ratio of $\mathcal{O}(\sqrt{k} \log \max\{n, m\})$, breaking the natural $\mathcal{O}(k)$ barrier. We also conduct experiments to evaluate the empirical performance of our algorithms. The results show that our solutions consistently outperform those produced by the previous existing algorithm.

Improved Algorithms for Trip-Vehicle Assignment in Ride-Sharing

The ability to self-revise is critical for AI agents. To maintain trust and foster positive perceptions, AI systems must correct their mistakes and adapt to users’ changing needs. We present a metacognitive architecture for self-revision in SAMI, an AI social agent deployed in Georgia Tech’s OMSCS program. Over the past ten semesters, SAMI has facilitated social connections for more than 11,000 students. Real-world deployments revealed frequent requests from students to revise the knowledge database, either to correct errors or to update their information. To address this need, we present a self-revision architecture that integrates Knowledge-Based AI (KBAI) and Generative AI (GenAI). The architecture (1) localizes the task requiring revision by introspecting on its self-model, (2) updates the knowledge database, and (3) communicates the revision process back to the user. We evaluate the framework using feedback cases derived from real student data and observed revision needs. This work introduces a novel metacognitive approach to improving explainability through the integration of KBAI and GenAI, with a clear path toward real-world deployment.

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Deep Model Reuse: Paving the Way for Efficient and Generalizable AI Systems

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