Singapore

This work investigates the zero-shot forecasting capability of time-series foundation models for Leaf Area Index (LAI) forecasting in agricultural monitoring. Using the HiQ dataset (U.S., 2000-2022), we systematically compare statistical baselines, a fully supervised LSTM, and the Sundial foundation model under multiple evaluation protocols. We find that Sundial, in the zero-shot setting, can outperform a fully trained LSTM provided that the input context window is sufficiently longâ€”specifically, when covering more than one or two full seasonal cycles. This demonstrates, for the first time, that a general-purpose foundation model can surpass specialized supervised models on remote-sensing time series prediction without any task-specific tuning. These results highlight the strong potential of pretrained time-series foundation models to serve as effective plug-and-play forecasters in agricultural and environmental applications.

AAAI 2026

Zero-Shot Transfer Capabilities of the Sundial Foundation Model for Leaf Area Index Forecasting

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.

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.<br><br>

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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.

Speaker anonymization aims to modify the speech signal in
order to protect the identity of a speaker while preserving
the linguistic content. Despite the increasing use of
children's voices in educational applications, such as oral
reading fluency (ORF) assessment, there is little work on
anonymization aspects. In this work, we investigate the
effectiveness of available speaker anonymization methods
drawing from traditional speech-production based approaches
and a neural codec based method. We investigate the
trade-off between privacy protection, measured as the
degree of anonymity, and utility preservation, which in the
current context of ORF assessment, includes the segmental
and suprasegmental features of children’s read speech
utterances. We report objective and subjective evaluations
using two child-speaker datasets: MPS and SpeechOcean. Our
objective evaluation results indicate that the
speech-production based method of vocal tract length
normalization coupled with pitch-transposition achieves the
best balance between privacy and utility. Subjective
listening results indicate that naturalness is achievable
across methods while the neural method fails to preserve
age characteristics, which are more easily controlled by
the speech-production driven methods.

Speaker Anonymization for Children's Oral Reading Assessment

The performance of 3D reconstruction using Neural Radiance Fields (NeRFs) for outdoor phenotyping of plants is strongly influenced by the imaging modality used for data collection. We compare drone, handheld, and 360Â° ground robot datasets collected over soybean and mungbean plots, and evaluate reconstruction quality using 2D metrics PSNR, SSIM, LPIPS, and 3D geometric metrics precision, recall, and F1 score.
Drone imagery produced the highest geometric fidelity, handheld captures achieved the strongest 2D appearance quality, and the 360Â° captures lagged in both metrics due to spherical distortion and motion artifacts. The consistency of the drone-based reconstructions highlights its suitability for field-scale 3D modeling and positions it as a practical foundation for future phenotyping applications.

Drone-based 3D Reconstruction of Plants in Field Conditions Using Neural Radiance Fields (NeRFs)

Self-supervised learning (SSL) is attractive for plant disease detection as it can exploit large collections of unlabeled leaf images, yet most existing SSL methods are built on CNNs or vision transformers that are poorly matched to agricultural imagery. CNN-based SSL struggles to capture disease patterns that evolve continuously along leaf structures, while transformer-based SSL introduce quadratic attention cost from high-resolution patches. To address these limitations, we propose StateSpace-SSL, a linear-time SSL framework that employs a Vision Mamba state-space encoder to model long-range lesion continuity through directional scanning across the leaf surface. A prototype-driven teacherâ€“student objective aligns representations across multiple views, encouraging stable and lesion-aware features from labelled data. Experiments on three publicly available plant disease datasets show that StateSpace-SSL consistently outperforms the CNN- and transformer-based SSL baselines in various evaluation metrics. Qualitative analyses further confirm that it learns compact, lesion-focused feature maps, highlighting the advantage of linear state-space modelling for self-supervised plant disease representation learning.

StateSpace-SSL: Linear-Time Self-supervised Learning for Plant Disease Detection

Accurate crop yield prediction is crucial for global food security and agricultural planning. This study benchmarks modern tabular foundation models and automated machine learning frameworks across three diverse agricultural datasets: (1) soybean yields with 86,101 temporal sequences, (2) global multi-crop data with 28,242 samples across 101 countries, and (3) EU-27 regional crops with 8,656 samples and significant missing data. We evaluate TabPFNv2 (an improved implementation of the TabPFN architecture), AutoGluon, and PyCaret to determine which approach works best under different data conditions. Our results show that model performance is highly context-dependent. AutoGluon achieves superior results on large-scale complete data, PyCaret excels on diverse multi-crop scenarios, while TabPFNv2 demonstrates distinct advantages on datasets with missing values (about a two percentage point gain in $R^2$ on EU-27). These findings challenge assumptions about universally superior approaches and reveal that foundation models offer robust zero-shot prediction, especially valuable for handling incomplete dataâ€”a critical capability for real-world agricultural AI deployment.

Benchmarking Tabular Foundation Models for Agricultural Yield Prediction

Scrap quality directly affects energy use, emissions, and safety in steelmaking. Today, the share of non-metallic inclusions (contamination) is judged visually by inspectors - an approach that is subjective and hazardous due to dust and moving machinery. We present an assistive computer vision pipeline that estimates contamination (per percent) from images captured during railcar unloading and also classifies scrap type. The method formulates contamination assessment as a regression task at the railcar level and leverages sequential data through multi-instance learning (MIL) and multi-task learning (MTL). Best results include MAE 0.27 and R2 0.83 by MIL; and an MTL setup reaches MAE 0.36 with F1 0.79 for scrap class. Also we present the system in near real time within the acceptance workflow: magnet/railcar detection segments temporal layers, a versioned inference service produces railcar-level estimates with confidence scores, and results are reviewed by operators with structured overrides; corrections and uncertain cases feed an active-learning loop for continual improvement. The pipeline reduces subjective variability, improves human safety, and enables integration into acceptance and melt-planning workflows.

From Images to Decisions: Assistive Computer Vision for Non‑Metallic Content Estimation in Scrap Metal

Multimodal Procedural Planning (MPP) facilitates human learning by integrating multiple modalities, such as text and video, to enhance comprehension and execution of procedural tasks. Instructional Videos (IVs) are crucial in MPP as they provide rich visual and auditory cues, making complex procedures more accessible. Large Language Models (LLMs) have promised significant potential for MPP by generating context-aware procedural plans and bridging multimodal information gaps. However, major limitations are the high cost of dataset collection and the under-utilization of the vast number of IVs available on platforms such as YouTube, where procedural content remains largely unstructured. To address this gap, we propose a novel framework, Visually Grounded MPP (VG-MPP), which leverages publicly available raw IVs, thereby eliminating the need for manually curated datasets and enabling the use of a broader range of MPP content. Our method harnesses the zero-shot reasoning capability of LLMs, the video-to-text generation ability of video captioning models, and the text-to-video generation capability of diffusion models, selectively filtering unnecessary information to deliver concise and essential multimodal guidance. Thereby, enabling AI-assisted MPP and skill learning is critical for human-centric manufacturing, where multimodal reasoning can assist humans by adapting to human variability and procedural uncertainty. Comprehensive evaluations demonstrate that VG-MPP provides a substantial advantage over baselines, outperforming existing approaches without depending on any specifically curated dataset.

VG-MPP: Visually Grounded Multimodal Procedural Planning from Unstructured Instructional Videos

Reinforcement learning (RL) has demonstrated great potential in robotic operations. However, its data-intensive nature and reliance on the Markov Decision Process (MDP) assumption limit its practical deployment in real-world scenarios involving complex dynamics and long-term temporal dependencies, such as multi-robot manipulation. Decision Transformers (DTs) have emerged as a promising offline alternative by leveraging causal transformers for sequence modeling in RL tasks. However, their applications to multi-robot manipulations still remain underexplored. To address this gap, we propose a novel framework, Symbolically-Guided Decision Transformer (SGDT), which integrates a neuro-symbolic mechanism with a causal transformer to enable deployable multi-robot collaboration. In the proposed SGDT framework, a neuro-symbolic planner generates a high-level task-oriented plan composed of human-understandable symbolic subgoals. Guided by these subgoals, a goal-conditioned decision transformer (GCDT) performs low-level sequential decision-making for multi-robot manipulation. This hierarchical architecture enables structured, interpretable, and generalizable decision making in complex multi-robot collaborative tasks. We evaluate the performance of SGDT across zero-shot and few-shot scenarios across multiple benchmark tasks analogous to human-centric manufacturing processes. To our knowledge, this is the first work to explore DT-based technology for multi-robot manipulation.

Toward Reliable Multi-Robot Collaboration via a Symbolically-Guided Decision Transformer

Manufacturing planners face complex operational challenges that require seamless collaboration between human expertise and intelligent systems to achieve optimal performance in modern production environments. Traditional approaches to analyzing simulation-based manufacturing data often create barriers between human decision-makers and critical operational insights, limiting effective partnership in manufacturing planning. Our framework establishes a collaborative intelligence system integrating Knowledge Graphs and Large Language Model-based agents to bridge this gap, empowering manufacturing professionals through natural language interfaces for complex operational analysis. The system transforms simulation data into semantically rich representations, enabling planners to interact naturally with operational insights without specialized expertise. A collaborative LLM agent works alongside human decision-makers, employing iterative reasoning that mirrors human analytical thinking while generating precise queries for knowledge extraction and providing transparent validation. This partnership approach to manufacturing bottleneck identification, validated through operational scenarios, demonstrates enhanced performance while maintaining human oversight and decision authority. For operational inquiries, the system achieves near-perfect accuracy through natural language interaction. For investigative scenarios requiring collaborative analysis, we demonstrate the framework's effectiveness in supporting human experts to uncover interconnected operational issues that enhance understanding and decision-making. This work advances collaborative manufacturing by creating intuitive methods for actionable insights, reducing cognitive load while amplifying human analytical capabilities in evolving manufacturing ecosystems.

Intelligent Human-Machine Partnership for Manufacturing: Enhancing Warehouse Planning through Simulation-Driven Knowledge Graphs and LLM Collaboration

Robust object detection in real-world scenarios with out-of-distribution (OOD) shifts is critical for deployment in manufacturing industry, where defect detection systems face limited data, high visual variability, and real-world corruptions (e.g., lighting changes, sensor noise). While image augmentation (IA) has shown promise in improving model robustness, its application often requires deep learning expertise, limiting adoption in industrial settings. To address this, we propose a human-centric pipeline that enables non-expert users to select and fuse IA strategies for fine-tuning object detectors to improve robustness against potential OOD conditions. We conduct a comprehensive benchmark of IA methods (Stylized, AugMix, and PixMix) for enhancing detection robustness on five public OOD datasets (three corruption benchmarks and two natural-shift datasets), evaluating both CNN-based (Faster R-CNN) and Vision Transformer-based (DINO) detectors. Our results show that, on different OOD scenarios, different model and IA strategy may lead to different level of robustness enhancement. Our findings suggest that user-driven selection of models and IA strategies is essential to achieve robust, real-world object detection performance. We also introduce a dataset-agnostic metric, Effectiveness of Robustness Enhancement (ERE), to facilitate cross-dataset comparison. This work provides a practical guidance for deploying robust defect detection in manufacturing applications.

A Human-Centric Augmentation Framework for Robust Object Detection

Human-centric manufacturing requires collaboration between humans and machines, yet digital twins largely ignore human cognitive states. We introduce the adaptive cognitive twin—a lightweight model of worker attention, fatigue, and stress. By fusing EEG, eye-tracking, and heart rate variability with machine learning, our system tracks cognitive states in real time, outperforming state-of-the-art methods in classification accuracy. In real-world factory deployment, it maintained 83.2% fatigue detection accuracy while enabling adaptive robot assistance and safety interventions. We present a complete architecture addressing privacy, interpretability, and scalability, establishing a foundation for human-machine performance optimization.

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Speaker Anonymization for Children's Oral Reading Assessment

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