GIFT uses LLMs for factor-guided state enhancement, risk-rule reward shaping, and diagnostic refinement in PPO financial RL, then fixes the interface to improve out-of-sample risk-adjusted performance.
Ludvig, and Cagatay Turkay
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
PTMC is a proposed Monte Carlo estimator that generates market-outcome distributions by simulating continuous double-auction interactions among persona-conditioned neural-policy bots whose heterogeneity is drawn from a learned distribution.
FPQC-SAC adds a bounded parameterized quantum circuit to SAC to constrain representations in low-SNR financial environments, reporting 66.89% higher cumulative returns than standard SAC on real portfolio tasks.
citing papers explorer
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GIFT: LLM-Guided State-Reward Interface for Financial Reinforcement Learning
GIFT uses LLMs for factor-guided state enhancement, risk-rule reward shaping, and diagnostic refinement in PPO financial RL, then fixes the interface to improve out-of-sample risk-adjusted performance.
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Persona-Trained Monte Carlo: Estimating Market-Outcome Distributions via Swarms of Persona-Conditioned Neural Policy Bots in a Limit Order Book
PTMC is a proposed Monte Carlo estimator that generates market-outcome distributions by simulating continuous double-auction interactions among persona-conditioned neural-policy bots whose heterogeneity is drawn from a learned distribution.
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Mitigating Bias in Low-SNR Financial Reinforcement Learning via Quantum Representations
FPQC-SAC adds a bounded parameterized quantum circuit to SAC to constrain representations in low-SNR financial environments, reporting 66.89% higher cumulative returns than standard SAC on real portfolio tasks.