Singapore

Value decomposition is a central approach in multi-agent reinforcement learning (MARL), enabling centralized training with decentralized execution by factorizing the global value function into local values. To ensure individual-global-max (IGM) consistency, existing methods either enforce monotonicity constraints, which limit expressive power, or adopt softer surrogates at the cost of algorithmic complexity. In this work, we present a dynamical systems analysis of non-monotonic value decomposition, modeling learning dynamics as continuous-time gradient flow. We prove that, under approximately greedy exploration, all zero-loss equilibria violating IGM consistency are unstable saddle points, while only IGM-consistent solutions are stable attractors of the learning dynamics. Extensive experiments on both synthetic matrix games and challenging MARL benchmarks demonstrate that unconstrained, non-monotonic factorization reliably recovers IGM-optimal solutions and consistently outperforms monotonic baselines. Additionally, we investigate the influence of temporal-difference targets and exploration strategies, providing actionable insights for the design of future value-based MARL algorithms.

AAAI 2026

Beyond Monotonicity: Revisiting Factorization Principles in Multi-Agent Q-Learning

multiagent systems

reinforcement learning

We are pleased to announce the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24), which will be held in Vancouver, British Columbia at the Vancouver Convention Centre – West Building from 20-27 February, 2024.

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-24 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.

We expect for AAAI-24 to be an in-person conference – one author of all accepted papers will be expected to present work in person unless there are exceptional circumstances that prevent this.

In order to access the AAAI-24 event page you need to register [here](https://aaai.org/aaai-conference/registration/)

AAAI 2024

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.

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.

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

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

As AI becomes increasingly powerful and ubiquitous, it is disrupting skills and displacing workers. NSF’s National AI Institute for Adult Learning and Online Education (AI-ALOE) posits that AI can be part of the solution to the growing problem if we can use AI for reskilling, upskilling, and workforce development at scale. The long-term vision of AI-ALOE is to develop and use AI technologies to enhance the proficiency of online education for all adult learners, using in-person education as a benchmark. The day-to-day mission of AI-ALOE is to conduct responsible research into AI that is grounded in theories of human cognition and learning and derived from the scientific process of learning engineering. I will describe ongoing research at AI-ALOE.

AI for Reskilling, Upskilling, and Workforce Development

Small Data: A New Paradigm for the Next Generation of AI

Current algorithms for steering LLM behavior are often implemented for specific use cases and tasks. To help provide a more general purpose approach to steering model behavior, IBM has recently developed two toolkits: AI Steerability 360 (AISteer360) and In-Context Explainability 360 (ICX360). This hands-on lab will provide a comprehensive walkthrough of these toolkits.

Participants will first be guided through a conceptual overview of how to steer model behavior across four model control surfaces: input, structural, state, and output steering. Through a series of interactive coding sessions, attendees will implement steering methods on a running example focused on steering a model to produce less toxic outputs. The lab will demonstrate how to construct steering controls for fine-grained model intervention, use cases for specific model tasks, and benchmarks for comparing steering methods on a given use case.

Closing the loop, participants will learn how the ICX360 toolkit can be used to understand why a given (steered) model produced a given output, and how to use these insights to guide refined controls. The session will build progressively from concept to implementation, ensuring participants understand how to create an end-to-end steering workflow.

Prerequisites: Participants should be comfortable with Python programming and have some prior exposure to LLMs and natural language processing (prompting, fine-tuning, etc.). Experience using the Hugging Face transformers package will be beneficial but not required. Participants must bring their own laptop with a Python IDE installed.

Remote compute access will be facilitated through Hugging Face and Google Colab. All setup instructions and access details will be communicated to enrolled participants in advance via the lab’s website.

Learning to Steer Large Language Models

Agentic AI is poised to redefine intelligent decision-making in Industry 4.0 by enabling autonomous systems to perceive, reason, and act over multimodal data sources such as sensor streams, structured knowledge bases, and unstructured maintenance logs. Despite the rapid progress of large language models and multimodal learning, building deployable and trustworthy agentic systems remains challenging due to fragmented data silos, integration complexity, lack of explainability, and limited evaluation protocols.
This tutorial offers a comprehensive, hands-on walkthrough of the full lifecycle of multimodal agentic AI systems—from inception to productization. Participants will begin by learning foundational agent architectures and reasoning strategies, including Plan-Execute, ReAct, Reflexion, RAFA, and hybrid symbolic-neural approaches. Building on these concepts, the tutorial features two interactive lab sessions: (i) addressing integration challenges in smart manufacturing using an open-source multi-agent environment, and (ii) benchmarking agent performance, reasoning, and explainability in an enterprise-scale industrial simulation.
Along the way, participants will engage with state-of-the-art tools and workflows for data integration, trace alignment, and failure diagnosis. They will explore evaluation techniques that combine human feedback, sensor-grounded truth, and emerging LLM-as-a-judge methods to assess reliability and trustworthiness. Additional emphasis will be placed on governance and monitoring frameworks, as well as visualizations for traceability and introspection, enabling participants to design explainable and auditable deployments.
The tutorial leverages two open-source environments AssetOpsBench, a simulation platform with 141 curated industrial scenarios, and SmartPilot, a smart manufacturing multi-agent system for multimodal data integration and reasoning, providing participants with realistic data and experimental workflows. Together, these environments demonstrate how to evaluate agents under diverse operating conditions, such as anomaly detection, fault progression, and post-maintenance recovery.

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