FinTriad: A Multimodal, Multi-Agent System with Adaptive Memory for Tri-Horizon Portfolio Construction

Professional investing requires integrating multimodal information across heterogeneous decision horizons, while experienced traders accumulate market experience that informs subsequent decisions. Although recent work has increasingly sought to replicate this decision-making process with LLM-based agents, existing systems remain limited in multimodal reasoning, portfolio-level decision-making, and, critically, the continual accumulation of experience from realized outcomes. We introduce FinTriad, a multimodal multi-agent framework with adaptive memory for concurrent tri-horizon portfolio construction. FinTriad integrates broadcast financial media, market-wide candidate selection, specialist analysis, and portfolio management across daily, weekly, and monthly horizons. Its verifiable-reward reflexive learning incorporates realized outcomes into temporally layered memory under a verifiable reward, admitting a correction only when the re-decision that produced it is confirmed against the realized market outcome. Across an 18-month evaluation window spanning multiple market regimes, FinTriad demonstrates competitive portfolio performance and robust adaptation to heterogeneous and evolving market conditions.