Singapore

Classifying non-Hermitian energy spectra under open boundary conditions is an open challenge in physics. This classification is a critical prerequisite for the rational inverse design of systems exhibiting desired dynamics and topological responses. While graph topology has emerged as a promising approach for characterizing these spectra, systematic methods for distilling non-Hermitian spectra into their corresponding graph representations have been lacking. Moreover, the resulting graphs often exhibit complexities that defy manual classification, necessitating machine learning approaches. In this work, we introduce a two-step framework for classifying non-Hermitian spectra based on their graph topologies. The first step employs $\texttt{Poly2Graph}$, an automated, high-performance pipeline that distills non-Hermitian spectra into $\textit{spectral graphs}$ suitable for graph neural networks (GNNs). The second step involves generating a large dataset of these spectral graphs and training a GNN for classification. We propose $\texttt{GnLTransformer}$, a novel architecture featuring dual channels that leverage line graphs to explicitly capture higher-order topological features. $\texttt{GnLTransformer}$ achieves over 99% classification accuracy on our dataset, outperforming standard baselines by 32%. Notably, beyond conventional GNNs, $\texttt{GnLTransformer}$ offers inherent explainability regarding higher-order topology. As a further contribution, we release a new $\textit{multi-graph}$ dataset comprising over 117K spectral graphs.

AAAI 2026

Classifying the Graph Topology of Non-Hermitian Energy Spectra with Graph Transformer

hai: explainable ai (xai) for human understanding

app: natural sciences

ml: graph-based machine learning

**Welcome to EMNLP 2023!**

On behalf of the EMNLP 2023 Organizing Committee, I extend a warm and heartfelt welcome to all of you to the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP). It is with immense pleasure and excitement that we gather here in Singapore, a vibrant hub of innovation and technological advancement.

The conference program is packed with insightful presentations, thought-provoking workshops, and engaging networking opportunities. In addition to the technical sessions, we have also planned several social events that will provide you with the opportunity to connect with your colleagues.

The conference is held in person at the Resorts World Convention Centre in Singapore, and available on-line with the help of Underline.

For those in Singapore, I hope that you will find time to explore the exciting Sentosa Island, Gardens by the Bay, Singapore Botanic Gardens and the many other attractions unique to Singapore.

Of course, we are grateful to our sponsors and partners for their generous support of EMNLP 2023, whose contributions make it possible for us to host this world-class event.

Yuji Matsumoto (RIKEN AIP) 
EMNLP 2023 General Chair

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EMNLP 2023

EMNLP 2023 took place in Singapore from Dec 6th to Dec 10th, 2023.

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.

Machine learning force fields (MLFFs) now rival quantum methods in accuracy, yet their internal logic remains opaque—a critical barrier to both trust and scientific discovery. We introduce **DUAL-X**, a *Dual-Level Explainability Framework* that bridges model reasoning with human understanding. **DUAL-X** unites two complementary perspectives: a **model-centric** level identifying *where* in the atomic structure the model focuses its attention, and a **human-centric** level revealing *what* physically meaningful interactions it prioritizes. Implemented with Grad-CAM for spatial attribution and SHAP-on-SOAP for physical interpretation, **DUAL-X** provides a general, human-in-the-loop paradigm for interpretable scientific AI. Applied to dopant migration—a challenging task for MLFFs—**DUAL-X** reveals that training strategy governs the emergence of chemical intuition. Multi-temperature fine-tuned models exhibit over $10^2\times$ stronger selectivity for Cr–Cr $f$-type ($l=3$) angular correlations than scratch-trained ones, emphasizing complex 3D coordination motifs essential for accuracy. Models with sharper Grad-CAM focus also display coherent SHAP importance for dopant clustering, revealing consistent internal reasoning across scales. By aligning model logic with human physical knowledge, **DUAL-X** transforms opaque predictors into interpretable scientific partners—advancing trustworthy, explainable, and insight-driven AI for materials discovery.

What is Your Force Field Really Learning? Gaining Scientific Intuition with A Dual-Level Explainability Framework

In data-scarce settings, learning accurate and interpretable models for high-stakes medical tabular classification remains a fundamental challenge, as healthcare decisions must be transparent and trustworthy. We propose a novel pipeline for few-shot tabular classification that distills the predictive power of large foundation models into globally interpretable student models. Given a tabular dataset, we generate synthetic data using CTGAN to approximate the underlying distribution. We then train high-capacity teacher models (TabPFN, TabM) on a small number of labeled examples and use them to pseudolabel the synthetic data. Finally, we train student explainer models (i.e., XGBoost, decision trees, Generalized Linear Rule Model (GLRM), and TTnet) on this pseudolabeled synthetic dataset. These student models are globally and exactly interpretable, yielding logical decision rules (e.g., disjunctive normal form) that fully reproduce their predictions. Evaluated across 8 clinical tabular tasks, our distilled models generally outperform baselines trained directly on the few-shot data, with improved ROC AUC scores across few-shot settings. This work demonstrates that foundation models can be effectively leveraged as teachers to produce small, transparent, and high-performing classifiers. Our approach advances the goal of reliable and interpretable machine learning in real-world settings where labeled data is limited.

Leveraging Foundation Models in Healthcare: A Distillation Approach to Interpretable Clinical Prediction

Adjoint-based inverse design yields compact, high-performance nanophotonic devices, but the mapping from pixel-level layouts to optical figures of merit remains hard to interpret. We present a simple pipeline that (i) generates a large set of wavelength demultiplexers (WDMs) with SPINS-B, (ii) records each final 2D layout and its spectral metrics (e.g., transmitted power at 1310 nm and 1550 nm), and (iii) trains a lightweight convolutional surrogate to predict these metrics from layouts, enabling (iv) gradient-based attribution via Integrated Gradients (IG) to highlight specific regions most responsible for performance. On a corpus of sampled WDMs, IG saliency consistently localizes to physically meaningful features (e.g., tapers and splitter hubs), offering design intuition that complements adjoint optimization. Our contribution is an end-to-end, data-driven workflow—SPINS-B dataset, CNN surrogate, and IG analysis—that turns inverse-designed layouts into interpretable attributions without modifying the physics solver or objective, and that can be reused for other photonic components.

Towards Explainable Inverse Design for Photonics via Integrated Gradients

Recent research has extensively studied how large language models manipulate integers in specific arithmetic tasks, and on a more fundamental level, how they represent numeric values. These previous works have found that language model embeddings can be used to reconstruct the original values, however, they do not evaluate whether language models actually model continuous values as continuous. Using expected properties of the embedding space, including linear reconstruction and principal component analysis, we show that language models not only represent numeric spaces as non-continuous but also introduce significant noise. Using models from three major providers (OpenAI, Google Gemini and Voyage AI), we show that while reconstruction is possible with high fidelity ($R^2 \geq 0.95$), principal components only explain a minor share of variation within the embedding space. This indicates that many components within the embedding space are orthogonal to the simple numeric input space. Further, both linear reconstruction and explained variance suffer with increasing decimal precision, despite the ordinal nature of the input space being fundamentally unchanged. The findings of this work therefore have implications for the many areas where embedding models are used, in-particular where high numerical precision, large magnitudes or mixed-sign values are common.

Language Models Do Not Embed Numbers Continuously

Cell phenotype transition refers to the changes in the morphology, function, and surface markers of cells that occur under specific environmental conditions or physiological states, based on their genomic information and external signals. This process plays an important role in development, tissue repair, and responses to external stimuli such as infection or inflammation. Traditional bioinformatics methods for addressing cell type transition often rely on hypothesis-driven models, which may not fully capture the complexity and heterogeneity of the transition processes. In this paper, we introduce DualCPT, a cell phenotype transition and differentiation model based on Markov processes. Specifically, the model consists of a classification branch and a transition branch. The transition branch identifies regulatory genes involved in cell phenotype transition and differentiation. In the classification branch, we evaluate the model’s overall performance on general cell type classification tasks using a comprehensive multi-metric evaluation framework; in the transition branch, we implement a token pruning-based approach for critical locus discovery and enhance information interaction between full-sequence contexts and prioritized regulatory sites via an improved multi-head attention mechanism. Cell phenotype transition tasks are further assessed by uncertainty quantification and confidence calibration. In particular, in gene knockout experiments, we found that knocking out important genes alters the probability of cell phenotype transition and differentiation, and knocking out a certain number of essential genes can terminate specific transition processes. Data, code, and checkpoints are publicly available at https://github.com/Ssupercoder/DualCPT.

DualCPT: Dual-branch Modeling for Cellular Phenotype Transition

Multimodal image fusion aims to integrate complementary information from multiple imaging modalities into a single, informative representation, which is crucial for applications in medical imaging and microscopy. Existing methods often face trade-offs between structural fidelity, edge preservation, and computational efficiency. In this work, we propose LightFusionNet, a lightweight dual-stream network designed to efficiently fuse multimodal images while retaining key structural, textural, and intensity features. The network leverages depthwise separable convolutions to reduce model complexity and incorporates a Predictive Context Attention (PCA) mechanism to selectively emphasize informative regions in the feature maps. Extensive experiments on benchmark medical imaging datasets, including PET-MRI, SPECT-MRI, and CT-MRI, demonstrate that our approach achieves comparable qualitative and quantitative performance compared to state-of-the-art fusion methods, while maintaining low computational cost. The proposed method provides an effective and efficient solution for multimodal image fusion, suitable for both clinical and research applications.

LightFusionNet: Lightweight Dual-Stream Network with Predictive Context Attention for Efficient Medical Image Fusion

Models trained on data from one site often underperform at another due to distribution shift, which can impede reliable deployment for medical imaging tasks. We tackle the problem of multi-center healthcare AI, where models must generalize across hospitals with heterogeneous imaging protocols, scanner hardware, and patient demographics. We propose a principled framework based on optimal transport dataset distances (OTDD) to analyze and guide cross‑site transfer. While OT-based approaches have mostly been applied to benchmark, non-medical datasets, we demonstrate its utility in a multi-center healthcare setting. We (i) build OTDD distance matrices between centers from fixed image embeddings (ResNet‑50, ResNet‑18), (ii) cluster centers using OT distance to reveal structure, and (iii) test whether transfer performance (finetuning on a single source, testing on a target) aligns with OTDD. We find that continuous OTDD distances correlate negatively with target AUROC across source-target pairs indicating that centers close in OTDD yield higher AUROC upon finetuning. Our results support OTDD as a center‑level selector/ranker for cross‑site adaptation in multi‑center healthcare, particularly when evaluated with threshold‑free metrics like AUROC.

Multi-Center Domain Shift in Healthcare AI: Optimal Transport-Based Quantification and Adaptation

In recent years, the challenge of handling out-of-distribution (OOD) data has become central to ensuring the reliability of artificial intelligence deployed in open-world environments. This half-day tutorial at AAAI’26 is designed for students, researchers, and practitioners seeking to advance the machine learning models by providing an integrated overview of OOD detection and OOD generalization. The tutorial will introduce foundational concepts and theoretical principles, survey advanced methodologies across post-hoc scoring, outlier exposure, representation learning, as well as causality-inspired approaches, and highlight challenges together with emerging solutions. Real-world case studies across different domains will demonstrate the critical importance of addressing OOD risks in practice, while discussions will emphasize the distinctions and synergies between detection and generalization as well as open research directions. To participate effectively, attendees are expected to have basic familiarity with linear algebra, probability, and fundamental machine learning concepts, though the content is designed to remain accessible and emphasize intuitive understanding over technical formalism.

Handling Out-of-Distribution Data in the Open World: Principles and Practice for Reliable AI

Although many LLM-based agents have recently been developed, progress on agents that are capable of solving complex, real-world problems is severely limited. One such important and challenging area is that of addressing IT management tasks, including solving IT incidents, which often require extensive human expertise and effort. To enable the development of agents for these tasks, in this lab, we introduce IT-Bench, an open benchmark for IT automation that simulates realistic environments where agents interact with IT systems and multi-modal operational data, including logs, metrics, alerts, and traces. ITBench provides a two- tiered benchmark: a static dataset (ITBench_static) that closely mirrors the live, gym-like environment (ITBench_live). Together, they provide a testbed for benchmarking agentic systems across a host of critical challenges, such as planning and reasoning over massive and heterogeneous IT data, safety, and stochasticity in live IT systems. Through demonstrations, participants will learn all of these challenges. ITBench_static is more beginner-friendly, enabling rapid benchmarking of agents against a smaller set of unique IT-domain challenges. ITBench_live enables more advanced researchers and practitioners familiar with IT systems to develop and test their agents against the full suite of challenging IT problems. We will guide attendees to use both ITBench_static and ITBench_live to develop baseline multi-agent systems and benchmark their performance. No prior IT or site reliability engineering (SRE) experience is required.

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