Singapore

Financial time series forecasting is both highly significant and challenging. Previous approaches typically standardized time series data before feeding it into forecasting models, which inherently leads to a loss of important information during the encoding process. Moreover, past time series models generally require fixed numbers of variables or lookback window lengths, which further limits the scalability of time series forecasting. In addition, interpretability and uncertainty in forecasting remain underexplored, as these factors directly impact the reliability and practical value of predictions. To address these issues, we first constructed a diverse financial image-text dataset (FVLDB) and developed the Uncertainty-adjusted Group Relative Policy Optimization (UARPO) method to enable the model not only output predictions but also analyze the uncertainty of those predictions. We then launched FinZero, a multi-modal pre-trained model finetuned by UARPO to perform reasoning, prediction, and analytical understanding on the FVLDB financial time series. Extensive experiments validate that FinZero exhibits strong adaptability and scalability. After fine-tuning with UARPO, FinZero achieves an approximate 13.48% improvement in prediction accuracy over GPT-4o in the high-confidence group, demonstrating the effectiveness of reinforcement learning fine-tuning of multi-modal large model for financial time series forecasting tasks. The FVLDB dataset is available at anonymous URL: https://drive.google.com/file/d/1nYUkB9Qf3O3B8byTXvVXg9jGmVL3vxT-/view?usp=share_link.

AAAI 2026

FinZero: Launching Multi-modal Financial Time Series Forecasting with Large Reasoning Model

financial time series

reinforced fine-tuning

uncertainty quantification

reasoning

**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.

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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

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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.

We present CAIA, a benchmark exposing a critical blind spot in AI evaluation: the inability of state-of-the-art models to operate in adversarial, high-stakes environments where misinformation is weaponized and errors are irreversible. While existing benchmarks measure task completion in controlled settings, real-world deployment demands resilience against active deception. Using cryptocurrency markets as a natural laboratory, where $30 billion was lost to exploits in 2024, we evaluate 17 leading models on 178 time-anchored tasks requiring agents to distinguish truth from manipulation, navigate fragmented information landscapes, and make irreversible financial decisions under adversarial pressure. Our results reveal a fundamental capability gap: without tools, even frontier models achieve only 12-28% accuracy on tasks junior analysts routinely handle. Tool augmentation improves performance but plateaus at 67.4% (GPT-5) versus 80% human baseline, despite unlimited access to professional resources. Most critically, we uncover a systematic tool selection catastrophe: models preferentially choose unreliable web search (55.5% of invocations) over authoritative blockchain data, falling for SEO-optimized misinformation and social media manipulation. This behavior persists even when correct answers are directly accessible through specialized tools, suggesting foundational limitations rather than knowledge gaps. The implications extend beyond cryptocurrency to any domain where adversaries actively exploit AI weaknesses, e.g. cybersecurity, content moderation, etc. Our finding that Pass@k metrics mask dangerous trial-and-error behavior challenges fundamental assumptions about autonomous deployment. We release CAIA with contamination controls and continuous updates, establishing adversarial robustness as a necessary condition for trustworthy AI autonomy. The benchmark reveals that current models, despite impressive reasoning scores, remain fundamentally unprepared for environments where intelligence must survive active opposition.

When Hallucination Costs Millions: Benchmarking AI Agents in High-Stakes Adversarial Financial Markets

Large Language Models (LLMs) have shown remarkable capabilities in document processing, but their inability to provide visual grounding without OCR dependencies poses significant challenges in business-critical applications. Current solutions either require model fine-tuning or rely on external OCR services, introducing additional costs, latency, and limitations in handling derived information. This paper presents ViG-LLM, a novel framework that enables closed-box LLMs to generate localization information through a multi-agent system combining U-Net-based layout deconstruction with viewport identification tasks. Evaluated on the FATURA and CORD dataset, our framework achieves perfect accuracy over spatial reasoning tuned LLM like Amazon Nova Pro, while demonstrating superior template-specific consistency. The framework maintains robust performance across LLM architectures while reducing operational costs by 60% compared to Textract-based solutions. In real-world document processing applications, the framework helps retain the high reasoning capabilities of the system in document information extraction tasks while improving explainability, reliability and human interaction for information verification. Through human-in-the-loop learning and closed-box prompt alignment techniques, ViG-LLM provides a robust, adaptable solution for visual grounding tasks in document processing workflows.

ViG-LLM: Enhancing Visual Grounding Capabilities in Closed-Box LLMs for Document Information Extraction without OCR Dependencies

Making use of swarm methods in financial market modeling of liquidity, and techniques from financial analysis in swarm analysis, holds the potential to advance both research areas. In swarm research, the use of game theory methods holds the promise of explaining observed phenomena of collective utility adherence with rational self-interested swarm participants. In financial markets, a better understanding of how independent financial agents may self-organize for the betterment and stability of the marketplace would be a boon for market design researchers. This paper unifies Liquidity Games, where trader payoffs depend on aggregate liquidity within a trade, with Rational Swarms, where decentralized agents use difference rewards to align self-interested learning with global objectives. We offer a theoretical frameworks where we define a swarm of traders whose collective objective is market liquidity provision while maintaining agent independence. Using difference rewards within a Markov team games framework, we show that individual liquidity-maximizing behaviors contribute to overall market liquidity without requiring coordination or collusion. This Financial Swarm model provides a framework for modeling rational, independent agents where they achieve both individual profitability and collective market efficiency in bilateral asset markets.

Multiagent Reinforcement Learning for Liquidity in Bond Markets

Terminal-based coding agents have emerged as powerful reasoning systems, achieving strong performance through iterative code execution and self-verification loops. But can these same capabilities, designed for software engineering, transfer effectively to financial reasoning tasks that demand numerical precision over unstructured data? We present a systematic evaluation of three state-of-the-art coding agents (Codex CLI, Claude Code, Gemini CLI) on four diverse financial QA benchmarks spanning numerical reasoning (FinQA, TAT-QA), document grounding (DocFinQA) and mathematical reasoning (DocMath-Eval). Under oracle context evaluation that isolates reasoning from retrieval, agentic execution consistently outperforms direct inference, with improvements ranging from +2.4% on short tabular tasks to +9.77% on long document reasoning. Within-model comparisons reveal that execution loops provide selective advantages on specific task types, particularly those requiring complex multi-step calculations. Our findings demonstrate that general-purpose coding agents can serve as effective financial reasoning systems without domain-specific fine-tuning, although challenges remain in handling extended symbolic contexts.

Code Execution Improves Financial Reasoning: Benchmarking Modern Agentic Systems on Financial QA

This paper introduces a novel Multi-Agent framework tailored for quantitative tasks routinely performed by portfolio managers and researchers within the asset management industry. Though machine learning and artificial intelligence have been used widely within quantitative finance, there is a scarcity of AI frameworks capable of autonomously performing complex tasks and quantitative analysis especially on structured data. Our framework addresses this gap and facilitates mathematical modeling and data analytics by dynamically generating executable code. The framework comprises of an innovative multi-agent architecture which includes specialized components for reflection, summarization, and financial expertise which coordinate to enhance problem solving abilities. We empirically evaluate the framework on a comprehensive dataset comprising of portfolio management-specific tasks, addressing a critical gap in current research. Our findings demonstrate that the proposed Multi-Agent framework vastly outperforms Single-Agent frameworks, demonstrating its practical utility across various task categories. By using dynamic code generation with the agent’s multi-step reasoning capabilities, we broaden the range of tasks that can be successfully addressed.

A Multi-Agent Framework for Portfolio Management Analytics in Quantitative Finance

Credit risk assessment is essential in the financial sector, but has traditionally depended on costly feature-based models that often fail to utilize all available information in raw credit records. This paper introduces LendNova, the first practical automated end-to-end pipeline for credit risk assessment, designed to utilize all available information in raw credit records by leveraging advanced NLP techniques and language models. LendNova transforms risk modeling by operating directly on raw, jargon-heavy credit bureau text using a language model that learns task-relevant representations without manual feature engineering. By automatically capturing patterns and risk signals embedded in the text, it replaces manual preprocessing steps, reducing costs and improving scalability. Evaluation on real-world data further demonstrates its strong potential in accurate and efficient risk assessment. LendNova establishes a baseline for intelligent credit risk agents, demonstrating the feasibility of language models in this domain. It lays the groundwork for future research toward foundation systems that enable more accurate, adaptable, and automated financial decision-making.

LendNova: Towards Automated Credit Risk Assessment with Language Models

We introduce a generic human-in-the-loop multi-agent examiner framework and demonstrate its application for evaluating small language models (SLMs) in the financial domain. While large language models have shown strong performance in finance, their computational cost motivates the exploration of SLMs as a viable alternative. However, assessing SLMs across a range of financial tasks remains challenging due to the specificity and scarcity of existing benchmarks. To address this gap, we extend the LLM-as-examiner paradigm into a modular system that decomposes evaluation into task-specific agents and estimates the performance of the SLMs relative to large strong models. We apply the proposed approach to estimate the performance of models from the PHI-4 and SMOLLM families on three problems. Extensive experiments show that our method generates more varied and complex evaluation questions than standard benchmarks, revealing nuanced insights into model strengths and weaknesses. The results highlight the value of scalable, agent-assisted evaluation for enhancing the understanding and deployment of small language models in financial domain, while also identifying certain limitations and challenges of our approach.

Multi-Agent Examiner: Evaluating Small Language Models on Financial Tasks

Analyzing financial documents such as 10-K filings, tabular disclosures, and macroeconomic reports demands expert reasoning and extensive time. However, existing Retrieval-Augmented Generation systems often struggle to process hybrid text–table structures or massive scale of financial documents. To address these challenges, we propose Hierarchical Reranker, a RAG framework designed to improve retrieval performance and generative reliability across large-scale financial datasets. The system integrates three key innovations: Pre-Retrieval Optimization, enhancing query clarity and search efficiency through normalization, keyword expansion, and table transformation; Hierarchical Reranker Architecture, improving retrieval precision through a two-stage ranking mechanism; and Long-Context Management, preserving reasoning accuracy through adaptive input partitioning and fusion under extensive contexts. Across multiple benchmarks, including FinQA, FinanceBench, and ConvFinQA, the proposed system achieved an NDCG@20 score of 0.7918 and demonstrated superior factual consistency. Its robustness was further validated by achieving second place in the ACM-ICAIF ’24 FinanceRAG Challenge. This work presents a deployable, domain-optimized RAG pipeline that enhances both the accuracy and scalability of financial reasoning, paving the way for automated audit reporting and quantitative investment analysis.

Hierarchical Reranking for Scalable Financial RAG System

This study investigated LLM–based automation for analyzing non-financial data in corporate credit evaluation. Two systems were developed and compared: a Single-Agent System (SAS), in which one LLM agent infers favorable and adverse repayment signals, and a Popperian Multi-Agent Debate System (PMADS), which structures the dual-perspective analysis as adversarial argumentation under the Karl Popper Debate protocol. Evaluation addressed three fronts: (i) work productivity (task completion time) compared with human experts; (ii) perceived report quality and usability, rated by credit risk professionals for system-generated reports; and (iii) reasoning characteristics quantified via reasoning-tree analysis (breadth, depth, and Reasoning Elaboration Index). Both systems drastically reduced task completion time relative to human experts (baseline ≈ 1,900 s/case; SAS ≈ 11.6 s; PMADS ≈ 92.0 s). Professionals rated SAS re-ports as adequate, while PMADS reports exceeded neutral benchmarks and scored significantly higher in explanatory adequacy, practical applicability, and usability. Reasoning-tree analysis showed PMADS produced deeper, more elaborated structures, whereas SAS yielded single-layered trees. These findings suggest that structured multi-agent debate enhances analytical rigor and perceived usefulness, though at the cost of longer computation time. Overall, the results demonstrate that reasoning-centered automation represents a promising approach for developing useful AI systems in decision-critical financial contexts.

Structured Debate Improves Corporate Credit Reasoning in Financial AI

Despite accounting for 96.1% of all businesses in Malaysia, access to financing remains one of the most persistent challenges faced by Micro, Small, and Medium Enterprises (MSMEs). Newly established or young businesses are often excluded from formal credit markets as traditional underwriting approaches rely heavily on credit bureau data. This study investigates the potential of bank statement data as an alternative data source for credit assessment to promote financial inclusion in emerging markets. Firstly, we propose a cash flow-based underwriting pipeline where we utilise bank statement data for end-to-end data extraction and machine learning credit scoring. Secondly, we introduce a novel dataset of 611 loan applicants from a Malaysian lending institution. Thirdly, we develop and evaluate credit scoring models based on application information and bank transaction-derived features. Empirical results show that the use of such data boosts the performance of all models on our dataset, which can improve credit scoring for new-to-lending MSMEs. Lastly, we intend to release the anonymised bank transaction dataset to facilitate further research on MSMEs financial inclusion within Malaysia's emerging economy.

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Next from AAAI 2026

When Hallucination Costs Millions: Benchmarking AI Agents in High-Stakes Adversarial Financial Markets

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