arXiv:2606.29251v1 Announce Type: new Abstract: Financial decision-makers face more information than they can directly inspect, making context compression necessary. Yet when large language models (LLMs) compress financial source material, they can alter the investment judgment supported by the original source. We frame this problem as information fidelity: compression loses fidelity when it changes the decision induced by the source. In agentic systems, such losses may recur across intermediate steps and amplify throughout the decision process. Across financial filings and earnings-call trans
The rapid deployment and increasing reliance on large language models (LLMs) for complex analytical tasks such as financial analysis is creating new vulnerabilities and necessitating closer examination of their output fidelity.
This highlights a critical and under-addressed risk in the application of LLMs for high-stakes decision-making, where distorted information can lead to significant financial misjudgments and systemic instability.
The assumption that LLMs provide a reliable, compressed summary of complex information is now being challenged, requiring new methodologies for validating AI-generated insights, especially in financial markets.
- · AI auditing firms
- · Firms leveraging explainable AI (XAI)
- · Developers of robust LLM evaluation metrics
- · Financial institutions with strong human-in-the-loop oversight
- · Financial firms over-reliant on unvalidated LLM analysis
- · LLM providers without robust fidelity controls
- · Investors making decisions solely based on LLM summaries
Financial professionals may become increasingly wary of LLM-generated summaries without explicit validation processes.
New regulations and industry standards will likely emerge to mandate information fidelity and transparency in AI-driven financial analysis.
The development of 'fidelity-preserving' AI models could become a major research and development area, potentially leading to a bifurcation of LLM applications based on risk tolerance.
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Read at arXiv cs.AI