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This paper presents Fin-Analyst, a hybrid trading agent that combines eight LLM specialists with rule-based signals for Tesla and Bitcoin, achieving top ranking in the FinMMEval 2026 Task 3 benchmark with a +13.51% return on TSLA.
The author replaced an LLM classifier with a simple set of if-statements and found the client preferred the rule-based approach, highlighting the value of simplicity over complex AI.
A developer describes the challenge of building multi-agent AI assistants that fail to handle unexpected situations gracefully, relying on explicit rules that lead to a whack-a-mole problem instead of enabling autonomous reasoning about ambiguity.
A developer built an LLM-powered ticket routing tool, but the support team distrusted the black-box decisions. The client paid to replace the LLM with a simple rules engine, resulting in higher accuracy, lower costs, and greater user trust.
This paper presents a deterministic, rule-based sleep staging method that explicitly implements the American Academy of Sleep Medicine (AASM) scoring rules, providing epoch-level natural language explanations. It achieves 60.5% epoch-level agreement with a majority-vote consensus on 50 polysomnography recordings, offering transparency as a complement to opaque deep learning models.