Are LLMs Good Financial User Simulators? Multi-view Investor Logic Alignment (MILA)

arXiv:2609.15727 · cs.AI, cs.CY, cs.HC · Submitted 2026-09-14 · Read on arXiv

cs.AI, cs.CY, cs.HC

Submitted: 2026-09-14

Updated: 2026-09-21

Comments: The complete version will be open and the paper is under review in AAAI

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Fidelity — 8× in this paper · explained in 2 episodes
• Accuracy — 6× in this paper · explained in 1 episode
• Benchmark — 6× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 4× in this paper · explained in 6 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Consistency — 2× in this paper · explained in 3 episodes
• F1 Score — 2× in this paper · explained in 3 episodes
• Failure Modes — 2× in this paper · explained in 3 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Mean Absolute Error — 1× in this paper
• Root Mean Squared Error — 1× in this paper
• Temporal consistency — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate
• Towards Real-world Human Behavior Simulation: Benchmarking Large Language Models on Long-horizon, Cross-scenario, Heterogeneous Behavior Traces
• DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence
• GLM-5: from Vibe Coding to Agentic Engineering
• ShiJianBench: From Dialogue to Decision for Long-Horizon Evaluation of Investment Advisors
• SurrealDriver: Designing LLM-powered Generative Driver Agent Framework based on Human Drivers' Driving-thinking Data
• Qwen3 Technical Report
• When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environments

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