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A unified benchmark that evaluates representative machine learning, reinforcement learning, LLM-based, and agent-based trading methods in cryptocurrency markets through three progressively more realistic stages through historical backtesting, prospective exchange-based paper trading, and real-money live trading is pres...
This work introduces a Query-Key router that represents the expertise required by each token under the current market context as a low-dimensional query and matches it with learnable expert keys and proposes TradingMoE, a trading-oriented sparse MoE that augments a frozen dense LLM with lightweight residual experts.
This work compares country-continent questions with noun, adjective, and code answers while keeping several fitted measurements distinct across Qwen, Llama, and Gemma to separate early readability, natural strength, causal steering, and later content dependence.
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