F$^2$Agent: Financial Fusion of Agentic Intelligence for Multimodal Trading

• URL: http://arxiv.org/abs/2608.05668v1
• Date: Thu, 06 Aug 2026 07:05:17 GMT
• Title: F$^2$Agent: Financial Fusion of Agentic Intelligence for Multimodal Trading
• Abstract summary: We propose F$2$Agent, a novel agentic paradigm driven by the Financial Fusion of Agentic Intelligence.<n>F$2$Agent first deploys a hierarchy of specialized agents to comprehensively extract modality-specific signals.<n>It further introduces a modality-aware adaptive fusion mechanism and noise-robust consistency regularization to generate noise-resilient trading signals.
• Score: 24.61485862983447
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With increasingly diverse and heterogeneous information sources, effectively leveraging multimodal data is becoming pivotal for high-quality financial trading. Although recent advancements in Large Language Model (LLM)-based agents have enabled the ingestion of multimodal inputs, existing methods fail to capture nuanced cross-modal dependencies and remain vulnerable to market noise, due to limited multimodal modeling, ineffective fusion mechanisms, and inadequate robustness. To address these challenges, we propose F$^2$Agent, a novel multimodal agentic paradigm driven by the Financial Fusion of Agentic Intelligence. F$^2$Agent first deploys a hierarchy of specialized agents to comprehensively extract modality-specific signals. It further introduces a modality-aware adaptive fusion mechanism coupled with noise-robust consistency regularization to dynamically capture fine-grained inter-modality dependencies and generate noise-resilient trading signals. Extensive experiments on six stocks and cryptocurrency assets demonstrate that F$^2$Agent consistently outperforms 16 competitive baselines across multiple trading metrics, with over 20% relative improvement in annualized return on average. Notably, F$^2$Agent delivers returns of 120.48% on GOOG and 148.41% on TSLA, demonstrating its efficacy and robustness in varying market dynamics.

Related papers

• Poisoning Agentic Alpha: Adversarial Vulnerabilities Across Roles and Architectures in Multi-Agent Trading Systems [103.90831279037094]
  LLM-based multi-agent trading systems are moving rapidly from research prototypes to live deployments that control real assets.<n>A corrupted signal can propagate to the final decision and translate into realized financial loss.<n>We present the first systematic empirical study in the financial domain to characterize how an adversarial signal enters a multi-agent trading system.
  arXiv  Detail & Related papers  (2026-08-25T05:07:37Z)
• MARTI-MARS$^2$: Scaling Multi-Agent Self-Search via Reinforcement Learning for Code Generation [64.2621682259008]
  Multi-Agent Reinforced Training and Inference Framework with Self-Search Scaling (MARTI-MARS2)<n>We propose a Multi-Agent Reinforced Training and Inference Framework with Self-Search Scaling (MARTI-MARS2) to integrate policy learning with multi-agent tree search.<n>We show that MARTI-MARS2 achieves 77.7%, outperforming strong baselines like GPT-5.1 on challenging code generation benchmarks.
  arXiv  Detail & Related papers  (2026-02-08T07:28:44Z)
• Active Asymmetric Multi-Agent Multimodal Learning under Uncertainty [15.933557806106071]
  We propose Active Asymmetric Multi-Agent Multimodal Learning under Uncertainty (A2MAML)<n>A2MAML is a principled approach for uncertainty-aware, modality-level collaboration.<n>Experiments on connected autonomous driving scenarios for collaborative accident detection demonstrate that A2MAML consistently outperforms both single-agent and collaborative baselines.
  arXiv  Detail & Related papers  (2026-02-04T17:01:31Z)
• Why Keep Your Doubts to Yourself? Trading Visual Uncertainties in Multi-Agent Bandit Systems [21.356119126402902]
  We introduce Agora, a framework that reframes coordination as a decentralized market for uncertainty.<n>A market-aware broker, extending Thompson Sampling, initiates collaboration and guides the system toward cost-efficient equilibria.<n>Results establish market-based coordination as a principled and scalable paradigm for building economically viable visual intelligence systems.
  arXiv  Detail & Related papers  (2026-01-26T17:58:53Z)
• Amplifying Prominent Representations in Multimodal Learning via Variational Dirichlet Process [55.91649771370862]
  Dirichlet process (DP) mixture model is a powerful non-parametric method that can amplify the most prominent features.<n>We propose a new DP-driven multimodal learning framework that automatically achieves an optimal balance between prominent intra-modal representation learning and cross-modal alignment.
  arXiv  Detail & Related papers  (2025-10-23T16:53:24Z)
• When Agents Trade: Live Multi-Market Trading Benchmark for LLM Agents [74.55061622246824]
  Agent Market Arena (AMA) is the first lifelong, real-time benchmark for evaluating Large Language Model (LLM)-based trading agents.<n>AMA integrates verified trading data, expert-checked news, and diverse agent architectures within a unified trading framework.<n>It evaluates agents across GPT-4o, GPT-4.1, Claude-3.5-haiku, Claude-sonnet-4, and Gemini-2.0-flash.
  arXiv  Detail & Related papers  (2025-10-13T17:54:09Z)
• Trade in Minutes! Rationality-Driven Agentic System for Quantitative Financial Trading [57.28635022507172]
  TiMi is a rationality-driven multi-agent system that architecturally decouples strategy development from minute-level deployment.<n>We propose a two-tier analytical paradigm from macro patterns to micro customization, layered programming design for trading bot implementation, and closed-loop optimization driven by mathematical reflection.
  arXiv  Detail & Related papers  (2025-10-06T13:08:55Z)
• QuantAgents: Towards Multi-agent Financial System via Simulated Trading [40.636918662488505]
  QuantAgents is a multi-agent system integrating simulated trading.<n> QuantAgents comprises four agents: a simulated trading analyst, a risk control analyst, a market news analyst, and a manager.<n>Our system incentivizes agents to receive feedback on two fronts: performance in real-world markets and predictive accuracy in simulated trading.
  arXiv  Detail & Related papers  (2025-10-06T09:45:57Z)
• TradingAgents: Multi-Agents LLM Financial Trading Framework [4.293484524693143]
  TradingAgents proposes a novel stock trading framework inspired by trading firms.<n>It features LLM-powered agents in specialized roles such as fundamental analysts, sentiment analysts, technical analysts, and traders with varied risk profiles.<n>By simulating a dynamic, collaborative trading environment, this framework aims to improve trading performance.
  arXiv  Detail & Related papers  (2024-12-28T12:54:06Z)
• MacroHFT: Memory Augmented Context-aware Reinforcement Learning On High Frequency Trading [20.3106468936159]
  Reinforcement learning (RL) has become another appealing approach for high-frequency trading (HFT) We propose a novel Memory Augmented Context-aware Reinforcement learning method On HFT, empha.k.a. MacroHFT. We show that MacroHFT can achieve state-of-the-art performance on minute-level trading tasks.
  arXiv  Detail & Related papers  (2024-06-20T17:48:24Z)
• Bi-Bimodal Modality Fusion for Correlation-Controlled Multimodal Sentiment Analysis [96.46952672172021]
  Bi-Bimodal Fusion Network (BBFN) is a novel end-to-end network that performs fusion on pairwise modality representations. Model takes two bimodal pairs as input due to known information imbalance among modalities.
  arXiv  Detail & Related papers  (2021-07-28T23:33:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.