Memory Architectures for Multi-Turn Text-to-SQL: A Benchmark and Empirical Study
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Computer Science > Computation and Language
arXiv:2605.26394 (cs)
[Submitted on 25 May 2026]
Title:Memory Architectures for Multi-Turn Text-to-SQL: A Benchmark and Empirical Study
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Abstract:Multi-turn Text-to-SQL is central to enterprise analytics yet remains predominantly evaluated in single-turn settings. We introduce EnterpriseMem-Bench, a multi-turn Text-to-SQL benchmark of 300 sessions and 1,400 turns built programmatically from three enterprise domains (BIRD financial, SEC EDGAR, Northwind), with deterministic ground truth and per-turn memory-critical annotation. We evaluate five frontier models -- GPT-5 mini, GPT-5.2, Claude Sonnet 4.5, Sonnet 4.6, and Opus 4.6 -- across five memory conditions enabling a three-way ablation isolating working-memory window size, episodic retrieval, and semantic augmentation as independent effects. All Claude models are evaluated with extended thinking enabled to maintain parity with GPT reasoning models. We introduce the Memory Benefit Score (MBS) as a per-turn diagnostic metric. Four findings emerge: (1) stateless multi-turn Text-to-SQL collapses to zero execution accuracy by Turn 3 across all five models, even under reasoning; (2) memory-architecture complexity does not monotonically improve accuracy -- working memory dominates, and additional components produce model- and dataset-dependent effects from +14 to -16 percentage points; (3) Claude Sonnet 4.6 underperforms Sonnet 4.5 by 17-33pp on SEC EDGAR across conditions, a generational regression persisting under reasoning; (4) under reasoning, Claude error distributions become mono-modal -- every non-correct turn is a wrong-result error. We release the benchmark, agent, and evaluation code.
| Comments: | 18 pages, 4 figures, 14 tables; includes appendices with verbatim prompts, example session, and full ablation tables; prepared by the LLM Suite Engineering Team, JP Morgan Chase & Co |
| Subjects: | Computation and Language (cs.CL) |
| ACM classes: | I.2.7; H.3.3; H.2.3 |
| Cite as: | arXiv:2605.26394 [cs.CL] |
| (or arXiv:2605.26394v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.26394
arXiv-issued DOI via DataCite (pending registration)
|
Submission history
From: Ravi Kumar Tummalapenta [view email][v1] Mon, 25 May 2026 23:52:15 UTC (273 KB)
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