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LLM Program Search Fails to Outperform Existing Methods in Options Pricing

By PulseAugur Editorial · [1 sources] ·

A new research paper explores the effectiveness of Large Language Model (LLM) program-search agents in solving complex financial problems, specifically Bermudan options pricing. The study found that while LLMs can generate Python code for these problems, the generated programs did not consistently outperform existing methods like Ridge LSMC, with performance gains often falling within Monte Carlo uncertainty. The research also investigated the impact of different memory and feedback mechanisms, finding no significant benefit from structured memory over score-only memory in the tested pipelines. AI

IMPACT This research suggests current LLM program-search agents may not yet be capable of reliably solving complex, specialized financial problems, indicating a need for further development in agent capabilities and evaluation methods.

RANK_REASON The cluster contains a research paper detailing an audit of LLM program search capabilities on a specific financial problem. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM Program Search Fails to Outperform Existing Methods in Options Pricing

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18 / 100
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The cluster contains a research paper detailing an audit of LLM program search capabilities on a specific financial problem. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Yuhua Qian ·

    Auditing LLM Program Search for Bermudan Stopping with Primal--Dual Certificates

    arXiv:2610.14063v1 Announce Type: cross Abstract: LLM program-search agents are usually judged by the score that drives their search. We study LLM-driven search over Python exercise policies for Bermudan options, an optimal stopping problem where a trusted evaluator owns the simu…

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