QuantEvolver applies reinforcement fine-tuning to evolve an LLM policy for generating executable alpha factor expressions, yielding higher-quality and more complementary factors than prompt-based baselines on market benchmarks.
Investlm: A large langu age model for investment using financial domain instruction tuning
8 Pith papers cite this work. Polarity classification is still indexing.
8
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
roles
background 1polarities
background 1representative citing papers
Moira parameterizes hierarchical RL policies for pair trading with LLMs and adapts them via prompt updates based on trajectory and episode feedback, outperforming baselines on real market data.
AgenticEval is a multi-agent framework that ingests unstructured policies to generate and self-evolve comprehensive safety benchmarks for LLMs, with experiments showing declining safety rates as tests harden.
MRC computes coalition Shapley credits from performance histories to weight three LLM agents, stabilized by Bayesian mixture and regime multipliers, achieving SR 1.51 and 440.1% cumulative return over 1037 days on 13 crypto assets.
A fine-tuned LLM called Perovskite-R1, built from curated perovskite literature and material libraries, proposes precursor additives and designs with some experimental validation showing improved stability and performance.
A systematic review of memory designs, evaluation methods, applications, limitations, and future directions for LLM-based agents.
A survey synthesizing recent LLM research and assessing its applicability to financial data analysis.