Singapore

Retrieval-Augmented Generation (RAG) is becoming increasingly essential for Question Answering (QA) in the financial sector, where accurate and contextually grounded insights from complex public disclosures are crucial. However, existing financial RAG systems face two significant challenges: (1) they struggle to process heterogeneous data formats, such as text, tables, and figures; and (2) they encounter difficulties in balancing general-domain applicability with company-specific adaptation. To overcome these challenges, we present VeritasFi, an innovative hybrid RAG framework that incorporates a multi-modal preprocessing pipeline alongside a cutting-edge two-stage training strategy for its re-ranking component. VeritasFi enhances financial QA through three key innovations: (1) A multi-modal preprocessing pipeline that seamlessly transforms heterogeneous data into a coherent, machine-readable format. (2) A tripartite hybrid retrieval engine that operates in parallel, combining deep multi-path retrieval over a semantically indexed document corpus, real-time data acquisition through tool utilization, and an expert-curated memory bank for high-frequency questions, ensuring comprehensive scope, accuracy, and efficiency. (3) A two-stage training strategy for the document re-ranker, which initially constructs a general, domain-specific model using anonymized data, followed by rapid fine-tuning on company-specific data for targeted applications. By integrating our proposed designs, VeritasFi presents a groundbreaking framework that greatly enhances the adaptability and robustness of financial RAG systems, providing a scalable solution for both general-domain and company-specific QA tasks. In additional, VeritasFi has been deployed as an online meeting agent, for which it has served more than 2000 people.

AAAI 2026

VeritasFi: An Adaptable, Multi-tiered RAG Framework for Multi-modal Financial Question Answering

nlp: question answering

nlp: (large) language models

nlp: language grounding & multi-modal nlp

app: other applications

ml: transfer

domain adaptation

multi-task learning

## Welcome to 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics!
This year, the conference is in Mexico City. NAACL was actually already planned for Mexico City in 2021, but due to the pandemic the entire conference was moved online. This year, finally, we get to go! So it is my sincere pleasure to welcome you to Mexico City, whether in person or virtually. Having the conference in Mexico City is a good opportunity to emphasize that NAACL is our flagship conference for ACL members not only in North America but also in Central and South America, even though NAACL has been bearing “North” in its name. At this year’s conference, we have a theme to match, with a theme track on the Languages of Latin America to showcase the linguistic diversity of the region.
 
The opportunity to present at NAACL should not depend on a researcher’s travel budget, or their family status. This is why it is so important to make virtual participation at NAACL as good an experience as possible – but we want to also provide a good experience for in-person participants. As a community, we are still working out the best way to do that. This year at NAACL, we are trying out a big virtual poster session ahead of the conference, with the hope that this will make make for a lively and interactive experience. At the same time, we are reducing virtual oral presentations, which seem to be particularly tricky to make to work well. A big thanks to the NAACL program chairs and to Luciana Benotti for all their ideas and work to improve the virtual experience. And participants, virtual as well as in-person: Please let us know what worked for you and what didn’t, so we can continue to improve hybrid conferences. 
 
I have been lucky to work with many amazing people. Without their insight, dedication and patience, and without the many hours of work they put in, NAACL would not have been possible. A huge thank you to the program chairs Helena Gomez, Kevin Duh, and Steve Bethard – you are the best! 
 
Finally, I would like to thank all authors, invited speakers and panelists, area chairs and reviewers, the volunteers organizing and chairing sessions, and all attendees, in-person and virtual. Thank you for helping us make NAACL 2024 come to life. 
 
Welcome and hope you all enjoy the conference! 
Katrin Erk 
The University of Texas at Austin 
NAACL-2024 General Chair 
*You can read the full Welcome message in the [Conference Handbook (downloadable)](https://drive.google.com/file/d/1H1NvW0VASQjkSCYgw3mr-3l4yYDSxz6B/view?usp=sharing)*

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NAACL 2024

2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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.

Automated Question Generation (AQG) plays a crucial role in enabling scalable assessment and personalized learning, serving as a cornerstone of modern Intelligent Tutoring Systems (ITS). However, existing AQG systems, particularly those built upon Large Language Models (LLMs), face a significant cognitive ceiling in generating high-stakes, exam-ready content for specialized domains like finance (e.g., CFA Exams). They often generate linguistically fluent but cognitively shallow items, failing to ensure difficulty calibration, distractor plausibility, computational correctness, and multi-concept integration. To address these challenges, we propose Fin-Agent-QG, a cognitive-collaborative multi-agent framework that emulates the workflow of professional financial exam committees. Fin-Agent-QG coordinates specialized agents for different cognitive phases of question construction: a Teacher–Critic loop for conceptual and difficulty alignment, a Quantitative Analyst Agent utilizing Code-as-Chain-of-Thought for computational reasoning, and a Knowledge Graph Construction Agent with a Case Strategist for multi-concept integration via a Financial Knowledge Graph. Empirical evaluations by both LLM metrics and human experts demonstrate that Fin-Agent-QG produces exam-ready financial questions with greater cognitive depth, accuracy, and conceptual coherence than traditional AQG systems. This work bridges the gap between automated generation and expert-level financial assessment design.

Fin-Agent-QG: A Collaborative Multi-Agent Framework for Higher-Order Cognitive and Difficulty-Aware Financial Exam Question Generation

We present ArgBoost, a transparent multi-agent forecasting framework for directional cryptocurrency price prediction that integrates structured LLM-based reasoning with interpretable machine learning. ArgBoost classifies next-day VWAP (Volume Weighted Average Price) movements for Ethereum by aggregating outputs from specialized LLM agents operating across distinct data domains. The agent-generated claims are organized into an argumentation graph and analyzed using an Explainable Boosting Machine (EBM) trained on rolling windows. Using daily Ethereum data from January 2024 to April 2025, ArgBoost outperforms Monte Carlo baselines and traditional technical strategies in both predictive and trading performance. The model demonstrates particular strength in tail market conditions, accurately detecting sharp upswings and downturns. Feature importance analysis shows that interaction terms involving coordinated relationships among agents and contextual market conditions jointly enhance predictability, offering a transparent and modular approach to evidence-driven decision support in volatile financial markets.

ArgBoost: Interpretable Multi-Agent Reasoning for Directional Cryptocurrency Price Forecasting

Rice is one of the most widely cultivated crops globally and has been developed into numerous varieties. The quality of rice during cultivation is primarily determined by its cultivar and characteristics. Traditionally, rice classification and quality assessment rely on manual visual inspection, a process that is both time-consuming and prone to errors. However, with advancements in machine vision technology, automating rice classification and quality evaluation based on its cultivar and characteristics has become increasingly feasible, enhancing both accuracy and efficiency. This study proposes a real-time evaluation mechanism for comprehensive rice grain assessment, integrating a one-stage object detection approach, a deep convolutional neural network, and traditional machine learning techniques. The proposed framework enables rice variety identification, grain completeness grading, and grain chalkiness evaluation. The rice grain dataset used in this study comprises approximately 20,000 images from six widely cultivated rice varieties in China. Experimental results demonstrate that the proposed mechanism achieves a mean Average Precision (mAP) of 99.14\% in the object detection task and an accuracy of 97.89\% in the classification task. Furthermore, the framework attains an average accuracy of 97.56\% in grain completeness grading within the same rice variety, contributing to an effective quality evaluation system.

An Overall Real-Time Mechanism for Classification and Quality Evaluation of Rice

Pig counting is an important task in pig marketing and farming. Most of the existing methods use supervised object detection for counting. However, supervised pig counting relies heavily on expensive data labeling, especially in the dense counting scenario. To address this issue, we propose BU-CLIP, which adopts the Contrastive Language Image Pre-training model (CLIP) for unsupervised pig counting. We tailor the image encoder of CLIP and the loss function to make it more suitable for pig counting. Our method replaces CLIP's image encoder with a bilinear model combining ConvNeXt and ResNet50 backbones, while the pooling operation is performed via a multi-head attention module. We reconstruct the loss function by using a multi-modal full-ranking loss, which captures the intrinsic correspondence between text and image. The proposed model is tested on a dense pig counting dataset, and extensive experiments demonstrate that our method outperforms unsupervised state-of-the-art counting methods and achieves almost the same results as supervised state-of-the-art counting methods.

Bilinear Magic Meets CLIP: Unsupervised Learning in Pig Counting Scenario

High-throughput plant phenotyping is an emerging interdisciplinary field of research that lies at the intersection of computer vision, plant science, artificial intelligence, data analytics and visualization, and genomics. To develop novel algorithms that advance image-based high-throughput plant phenotyping research with an aim of precision farming and global food security, the publication of benchmark datasets is indispensable. Thus, this paper introduces ReproPheno, a large-scale, open-source dataset comprising multi-view image sequences of plants spanning their entire life cycle captured using three modalities of cameras, i.e., visible light, fluorescent, and hyperspectral, within the LemnaTec Scanalyzer 3D high-throughput plant phenotyping facility. By enabling comprehensive analysis across multiple sensing modalities and temporal scales, this dataset opens the door to several novel research directions in computer vision, including multimodal co-segmentation, hyperspectral dataset distillation, and 3D model reconstruction of living organisms with growing architectural complexity. Furthermore, the paper presents ReproPhenoNet, a novel algorithm that uses You Only Look Once (YOLO) deep neural-network-based object detector to detect flowers and fruits from visible light and hyperspectral image sequences, respectively. These detections form the foundation for the quantitative computation of reproductive-stage plant phenotypes.

ReproPheno and ReproPhenoNet: A Large-Scale Multimodal Benchmark Dataset and Deep Learning Framework for Reproductive-Stage Plant Phenotyping

Timely detection of plant diseases in greenhouse environments is essential for minimizing crop loss, reducing chemical use, and supporting sustainable production. This paper presents an AI-enabled, reconfigurable edge device designed for leaf-level disease assessment using multimodal sensing and embedded intelligence. A 3D-printed handheld prototype was developed with a custom PCB integrating VIS/NIR spectral sensors, a thermal module, and an RGB camera for noninvasive measurement of physiological stress. A lightweight deep learning model combined with handcrafted spectral indices performs fully on-device analysis to detect early indicators such as abnormal thermal patterns, pigment loss, and reflectance changes linked to infections. The system is built on a low-power Raspberry Pi platform and supports model reconfiguration for rapid adaptation to different crops and disease types. Real-time inference is achieved through a segmentation-based pipeline deployed on both an Android app and an Ubuntu graphical interface. Validation trials demonstrated early disease detection with previously collected images and an average latency of 2.52 seconds on Android and 2.19 seconds on Ubuntu. We are currently calibrating our framework in the greenhouse environment for real-time assessments. A web dashboard provides live visualization of thermal overlays, spectral response, and health scores. By combining custom hardware, multimodal sensing, and on-device AI, the system offers a portable and scalable solution for early disease detection in greenhouse crops.

AI-Enabled Reconfigurable Edge Device for Plant Health Assessment in Greenhouse Environment

Weed detection is a critical task in precision agriculture, as weeds significantly reduce crop yields and increase production costs. This work presents an efficient, low-cost, and low-power UAV-deployable weed detection pipeline that relies solely on consumer grade RGB cameras. We propose novel lightweight U-Net and Attention U-Net architectures optimized for real-time semantic segmentation on an edge device. To enhance segmentation accuracy, we integrate additional RGB derived features. Experiments on the CoFly-WeedDB detection dataset demonstrate that both architectures perform effectively on RGB imagery, with further improvements when incorporating hue and edge detection features. The proposed lightweight U-Net architectures, made more efficient through quantization, achieves IoU scores above 50% and Dice scores exceeding 60% on the CoFly-WeedDB dataset across multiple augmentation levels. These findings highlight the practicality of deploying lightweight deep learning models for precision weed detection in resource-constrained agricultural environments.

On-Edge Weed Detection Using Unmanned Aerial Vehicles

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.

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Fin-Agent-QG: A Collaborative Multi-Agent Framework for Higher-Order Cognitive and Difficulty-Aware Financial Exam Question Generation

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Optimizing LLM Based Retrieval Augmented Generation Pipelines in the Financial Domain

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