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arxiv: 2604.22934 · v1 · submitted 2026-04-24 · 💻 cs.AI · cs.CL

PExA: Parallel Exploration Agent for Complex Text-to-SQL

Tanmay Parekh , Ella Hofmann-Coyle , Shuyi Wang , Sachith Sri Ram Kothur , Srivas Prasad , Yunmo Chen This is my paper

Pith reviewed 2026-05-08 11:51 UTC · model grok-4.3

classification 💻 cs.AI cs.CL
keywords text-to-SQLLLM agentsparallel explorationtest coveragesemantic coverageSQL generationbenchmark evaluation
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The pith

An agent for complex text-to-SQL breaks queries into parallel atomic test cases to achieve semantic coverage before generating the final SQL.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Current LLM agents for turning natural language into SQL struggle with a trade-off where gains in accuracy often increase response time. This paper proposes to treat the generation process like software testing by creating and running multiple simpler atomic SQL statements in parallel. These atomic cases are designed to cover the semantics of the original complex query. The system iterates on improving this coverage until sufficient information is available. Only then does it generate the final SQL, using the results from the test cases to ground its output. This approach reached 70.2 percent execution accuracy on the Spider 2.0 benchmark, setting a new record.

Core claim

We reformulate text-to-SQL generation within the lens of software test coverage where the original query is prepared with a suite of test cases with simpler, atomic SQLs that are executed in parallel and together ensure semantic coverage of the original query. After iterating on test case coverage, the final SQL is generated only when enough information is gathered, leveraging the explored test case SQLs to ground the final generation. Validation on Spider 2.0 shows 70.2% execution accuracy, establishing a new state of the art.

What carries the argument

The reformulation of text-to-SQL as software test coverage using suites of simpler atomic SQL test cases executed in parallel to ensure semantic coverage.

If this is right

  • The parallel execution of atomic test cases allows more efficient information gathering than sequential methods.
  • Grounding final SQL generation on the results of explored test cases improves accuracy for complex queries.
  • The iteration on test case coverage ensures sufficient semantic information before producing the final output.
  • This yields higher execution accuracy on standard benchmarks like Spider 2.0 without proportional latency increases.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The explicit coverage approach could extend to other structured generation tasks such as code synthesis or query languages in different domains.
  • Making semantic coverage measurable may help identify specific components where LLM agents fail on complex inputs.
  • Automatically selecting optimal atomic test cases might reduce the total number of LLM calls required.

Load-bearing premise

That suites of simpler atomic SQL test cases executed in parallel can ensure semantic coverage of the original complex query and that iterating on this coverage gathers enough information to ground accurate final generation.

What would settle it

An experiment on Spider 2.0 showing that the parallel atomic test suite reaches high coverage but the generated final SQL still fails to execute correctly against the database.

Figures

Figures reproduced from arXiv: 2604.22934 by Ella Hofmann-Coyle, Sachith Sri Ram Kothur, Shuyi Wang, Srivas Prasad, Tanmay Parekh, Yunmo Chen.

Figure 1
Figure 1. Figure 1: Illustration of our framework PEXA, comprising three sub-agents – Planner, Test Case Generator, and SQL Proposer – and two tools – SQL Executor and Semantic Verifier. In the black boxes (left), we indicate how we induce parallelism in our framework. We indicate deterministic and agentic decision choices in terms of flow using solid and dotted arrows respectively. Proposer sub-agent’s output before returnin… view at source ↗
Figure 2
Figure 2. Figure 2: Error analysis and categorization on the major view at source ↗
Figure 3
Figure 3. Figure 3: Prompt utilized for PEXA’s Planner 17 view at source ↗
Figure 4
Figure 4. Figure 4: Prompt utilized for PEXA’s Test Case Generator 18 view at source ↗
Figure 5
Figure 5. Figure 5: Prompt utilized for PEXA’s Final SQL Proposer 19 view at source ↗
Figure 6
Figure 6. Figure 6: Prompt utilized for PEXA’s Semantic Verifier 20 view at source ↗
Figure 7
Figure 7. Figure 7: Snapshot of PEXA’s performance compared to other methods as of November 15, 2025. 23 view at source ↗
read the original abstract

LLM-based agents for text-to-SQL often struggle with latency-performance trade-off, where performance improvements come at the cost of latency or vice versa. We reformulate text-to-SQL generation within the lens of software test coverage where the original query is prepared with a suite of test cases with simpler, atomic SQLs that are executed in parallel and together ensure semantic coverage of the original query. After iterating on test case coverage, the final SQL is generated only when enough information is gathered, leveraging the explored test case SQLs to ground the final generation. We validated our framework on a state-of-the-art benchmark for text-to-SQL, Spider 2.0, achieving a new state-of-the-art with 70.2% execution accuracy.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript introduces PExA, a Parallel Exploration Agent for text-to-SQL. It reformulates complex query generation as the parallel execution of simpler atomic SQL test cases designed to ensure semantic coverage of the original query. The framework iterates on coverage until sufficient information is gathered, then generates the final SQL grounded in the explored test cases. It reports a new state-of-the-art execution accuracy of 70.2% on the Spider 2.0 benchmark.

Significance. If the reported result holds under rigorous controls, the work could be significant for LLM-based text-to-SQL agents by offering a structured, coverage-driven approach that may improve both accuracy and latency through parallelism and iteration. The analogy to software test coverage is a fresh framing that could generalize to other agentic generation tasks. The explicit SOTA claim on a standard benchmark provides a clear, falsifiable prediction that future work can test.

major comments (2)
  1. [method description and experimental validation] The central claim rests on the assertion that parallel atomic SQL test cases plus iteration achieve semantic coverage of complex queries (abstract and method description). No quantitative coverage metric, failure-mode analysis, or ablation on coverage quality is supplied, leaving the weakest assumption untested beyond the aggregate 70.2% accuracy figure.
  2. [experimental results] The SOTA claim of 70.2% execution accuracy on Spider 2.0 is presented without any description of baselines, number of runs, error analysis, or how coverage iteration is operationalized and measured. This information is load-bearing for assessing whether the framework actually outperforms prior methods or simply benefits from unstated implementation choices.
minor comments (1)
  1. [abstract and §3] The abstract and method overview use the term 'semantic coverage' without a formal definition or pseudocode for how atomic cases are generated or when iteration stops.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive feedback on our manuscript. We address each of the major comments below and will revise the paper accordingly to incorporate additional details and analyses as suggested.

read point-by-point responses

  1. Referee: [method description and experimental validation] The central claim rests on the assertion that parallel atomic SQL test cases plus iteration achieve semantic coverage of complex queries (abstract and method description). No quantitative coverage metric, failure-mode analysis, or ablation on coverage quality is supplied, leaving the weakest assumption untested beyond the aggregate 70.2% accuracy figure.

    Authors: We agree that the current manuscript would benefit from a more explicit quantitative evaluation of the coverage achieved by the parallel atomic test cases. In the revised version, we will add a coverage metric defined as the fraction of semantic components (tables, columns, conditions) from the original query that are verified through successful execution of the atomic test cases. Additionally, we will include an ablation study removing the iteration step and a failure-mode analysis identifying queries where coverage remains incomplete despite iteration. This will provide stronger evidence for the effectiveness of our approach beyond the final accuracy score. revision: yes

  2. Referee: [experimental results] The SOTA claim of 70.2% execution accuracy on Spider 2.0 is presented without any description of baselines, number of runs, error analysis, or how coverage iteration is operationalized and measured. This information is load-bearing for assessing whether the framework actually outperforms prior methods or simply benefits from unstated implementation choices.

    Authors: We acknowledge that the experimental section in the current draft lacks sufficient detail on the evaluation protocol. The revised manuscript will include: (1) a table comparing against all relevant baselines on Spider 2.0 with their reported accuracies, (2) results averaged over 3 independent runs with standard deviations to account for variability in LLM outputs, (3) a detailed error analysis breaking down failure cases by query complexity and coverage issues, and (4) a precise description of the coverage iteration process, including the stopping criteria (e.g., when coverage exceeds 90% or after a maximum of 5 iterations) and how the explored test cases are used to ground the final SQL generation. These additions will allow readers to better evaluate the validity of the SOTA claim. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper describes an empirical agent framework that reformulates text-to-SQL as parallel execution of atomic SQL test cases for coverage, followed by grounded final generation. No equations, fitted parameters, predictions, or derivation steps are present that reduce by construction to the inputs. Validation is performed directly on the external Spider 2.0 benchmark with a reported execution accuracy result. The approach is self-contained as a new method without load-bearing self-citations, self-definitions, or renamed known results.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract describes the approach at high level only; no free parameters, axioms, or invented entities are specified or required for the central claim.

pith-pipeline@v0.9.0 · 5436 in / 1042 out tokens · 95489 ms · 2026-05-08T11:51:31.773499+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

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    Comparison for Semantic Verification:Compare the back-translated natural language query (from part 1) with the original question. The back-translated query should capture the same intent and meaning as the original question. - If the back-translated query matches the original question, it indicates that the SQL query is semantically correct. - If the back...

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    Return a response: Your final response should be a structured JSON of three fields - "correct", "explanation", and "back_translated_query". The "back_translated_query" should always be the query back-translated from step 1. Based on the comparison in step 2: - If the SQL query is semantically correct: "correct" = true and "explanation" = <Provide a one li...

    work page 2022

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    name" AS

    SQL: SELECT "name" AS "package_name", "version", "project_urls", "upload_time" FROM "PYPI"."PYPI"."DISTRIBUTION_METADATA" WHERE "name" IS NOT NULL LIMIT 100; Execution Output (truncated): package_name,version,project_urls,upload_time sparganothis-vim,0.1.15,[],1720561327687084 sparganothis-vim,0.1.15,[],1720561419667601 safa,0.0.1,[],1720564065701497

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    downloads

    SQL: WITH "downloads" AS ( SELECT "project" AS "name", "file":"version"::string AS "version", MIN("timestamp") AS "first_download_ts" FROM "PYPI"."PYPI"."FILE_DOWNLOADS" GROUP BY "project", "file":"version"::string ) SELECT d."name" AS "package_name", d."version", d."project_urls", d."upload_time" FROM "PYPI"."PYPI"."DISTRIBUTION_METADATA" d JOIN "downloa...

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    name" AS

    SQL: SELECT d."name" AS "package_name", d."version", d."project_urls", d."upload_time" FROM "PYPI"."PYPI"."DISTRIBUTION_METADATA" d JOIN "PYPI"."INFORMATION_SCHEMA"."TABLES" t ON t."TABLE_NAME" = ’DISTRIBU- TION_METADATA’ LIMIT 100; Execution Output (truncated): package_name,version,project_urls,upload_time sparganothis-vim,0.1.15,[],1720561327687084 spar...

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    project" AS

    SQL: SELECT "project" AS "package_name", "file":"version"::string AS "version", NULL AS "project_urls", MIN("timestamp") AS "upload_time" FROM "PYPI"."PYPI"."FILE_DOWNLOADS" GROUP BY "project", "file":"version"::string LIMIT 100; Execution Output (truncated): package_name,version,project_urls,upload_time bs- solevaluator,0.1.1„1649607460000000 django-grap...

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    name" AS

    SQL: SELECT "name" AS "package_name", "version", "project_urls" AS "project_urls", "up- load_time" FROM "PYPI"."PYPI"."DISTRIBUTION_METADATA" LIMIT 10; Execution Output (truncated): package_name,version,project_urls,upload_time sparganothis-vim,0.1.15,[],1720561327687084 sparganothis-vim,0.1.15,[],1720561419667601 safa,0.0.1,[],1720564065701497 24 Table 1...

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    PYPI"."PYPI

    SQL: SELECT COUNT(*) AS cnt_non_empty FROM "PYPI"."PYPI"."DISTRIBUTION_METADATA" WHERE "project_urls" IS NOT NULL AND ARRAY_SIZE("project_urls") > 0; Execution Output (truncated): CNT_NON_EMPTY 42893

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    name" AS

    SQL: SELECT "name" AS "package_name", "version", "project_urls", "upload_time" FROM "PYPI"."PYPI"."DISTRIBUTION_METADATA" WHERE "project_urls" IS NOT NULL AND ARRAY_SIZE("project_urls") > 0 LIMIT 10; Execution Output (truncated): package_name,version,project_urls,upload_time renovosolutions.aws- cdk-aspects-security-group,1.0.214,"[ ""Source, https://gith...

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    PYPI"."PYPI

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    project" AS

    SQL: SELECT "project" AS "package_name", COUNT(*) AS "total_downloads" FROM "PYPI"."PYPI"."FILE_DOWNLOADS" GROUP BY "project" ORDER BY "total_downloads" DESC; Execution Output (truncated): package_name,total_downloads s3transfer,2962234 attrs,1787211 col- orama,1567249

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    name" AS

    SQL: SELECT dm."name" AS "package_name", COUNT(*) AS "total_downloads" FROM "PYPI"."PYPI"."FILE_DOWNLOADS" fd JOIN ( SELECT DISTINCT "name" FROM "PYPI"."PYPI"."DISTRIBUTION_METADATA" ) dm ON fd."project" = dm."name" GROUP BY dm."name" ORDER BY "total_downloads" DESC; Execution Output (truncated): package_name,total_downloads wheel,1481586 tabulate,1478960...

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    name", "version

    SQL: SELECT "name", "version", "project_urls", "upload_time" FROM "PYPI"."PYPI"."DISTRIBUTION_METADATA" WHERE "project_urls"::string ILIKE ’%github.com%’ LIMIT 10; Execution Output (truncated): name,version,project_urls,upload_time track-viz,0.5.0,"[ ""Changelog, https://github.com/JulienMBABD/track-viz/releases"", ""Documentation, https://track- viz.read...

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    name", dm

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    work page

  27. [27]

    name", dm

    SQL: SELECT dm."name", dm."version", dm."project_urls", dm."upload_time" FROM "PYPI"."PYPI"."DISTRIBUTION_METADATA" dm JOIN "PYPI"."INFORMATION_SCHEMA"."TABLES" t ON t."TABLE_NAME" = ’DISTRIBU- TION_METADATA’ AND t."TABLE_SCHEMA" = ’PYPI’ WHERE dm."project_urls"::string ILIKE ’%github.com%’ LIMIT 10; Execution Output (truncated): name,version,project_urls...

    work page

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    name", dm

    SQL: SELECT DISTINCT dm."name", dm."version", dm."project_urls", dm."upload_time" FROM "PYPI"."PYPI"."DISTRIBUTION_METADATA" dm JOIN "PYPI"."PYPI"."FILE_DOWNLOADS" fd ON dm."name" = fd."project" JOIN "PYPI"."INFORMATION_SCHEMA"."TABLES" t ON t."TABLE_NAME" = ’DISTRIBUTION_METADATA’ AND t."TABLE_SCHEMA" = ’PYPI’ WHERE dm."project_urls"::string ILIKE ’%gith...

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    project_urls

    FLATTEN"project_urls" and keep only the elements that reference GitHub.--

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    Remove a trailing ".git" (with optional slash). */ SELECT DISTINCT"package_name", /* step-A: repo root substring */ REGEXP_SUBSTR(LOWER("url_element"),'https?://github\\.com/[^/]+/[^/]+')AS "root_url" FROMflattened ), normalised_urlsAS( SELECT"package_name", -- step-B: drop trailing .git and any trailing slash RTRIM(REGEXP_REPLACE("root_url",'\\.git$','')...

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