The paper systematizes security for LLM agents in agentic commerce into five threat dimensions, identifies 12 cross-layer attack vectors, and proposes a layered defense architecture.
From deep learning to LLMs: A survey of AI in quantitative investment
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QTMRL applies A2C reinforcement learning to a dataset of 23 years of S&P 500 OHLCV data enriched with trend, volatility, and momentum indicators, claiming better profitability and risk control than nine baselines including ARIMA and LSTM.
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SoK: Security of Autonomous LLM Agents in Agentic Commerce
The paper systematizes security for LLM agents in agentic commerce into five threat dimensions, identifies 12 cross-layer attack vectors, and proposes a layered defense architecture.
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QTMRL: An Agent for Quantitative Trading Decision-Making Based on Multi-Indicator Guided Reinforcement Learning
QTMRL applies A2C reinforcement learning to a dataset of 23 years of S&P 500 OHLCV data enriched with trend, volatility, and momentum indicators, claiming better profitability and risk control than nine baselines including ARIMA and LSTM.