# Fidelity collapses with table size: tool architecture study

Digest AI · Enterprise & Industry · published 2026-11-03T05:00:00Z

Canonical: https://digestai.news/story/fidelity-collapses-with-table-size-tool-architecture-study

## Summary

A study comparing two different ways of delivering data from tools to an AI model found that one approach (BYPASS) is more robust and accurate than the other. The research, conducted on a production travel agent system, shows that as the number of rows in the table increases, the accuracy of responses by the BYPASS method drops significantly. Specifically, when dealing with tables containing 10 or fewer rows, the BYPASS method achieves nearly perfect results (92.3% correct). However, for larger tables (over 74 rows), its performance drops to around 8%. The study suggests that practitioners have unwittingly favored a delivery method that prioritizes correctness over efficiency and scalability.

## Key points

- BYPASS shows higher accuracy with smaller tables
- Accuracy decreases significantly as table size increases
- Costs rise dramatically for BYPASS when dealing with larger tables

## Why it matters

This research highlights the importance of choosing appropriate delivery architectures in AI systems, particularly regarding data scalability and model performance.

## Sources

1. [Fidelity Collapses with Table Size, Not Turn Count: Delivery Architecture in a Deployed Tool-Using Agent](https://arxiv.org/abs/2611.00442) (arXiv cs.CL, 2026-11-03, primary source)

## Cite

Digest AI, "Fidelity collapses with table size: tool architecture study", 3 November 2026, https://digestai.news/story/fidelity-collapses-with-table-size-tool-architecture-study

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