arxiv: 2604.02444 · v1 · submitted 2026-04-02 · 💻 cs.DB

Recognition: no theorem link

OmniTQA: A Cost-Aware System for Hybrid Query Processing over Semi-Structured Data

Nima Shahbazi , Seiji Maekawa , Nikita Bhutani , Estevam Hruschka

Authors on Pith no claims yet

Pith reviewed 2026-05-13 20:13 UTC · model grok-4.3

classification 💻 cs.DB

keywords hybrid query processingtable question answeringlarge language modelssemi-structured dataquery optimizationcost-aware planningText-to-SQLsemantic operators

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The pith

OmniTQA turns LLM semantic reasoning into an optimizable operator inside relational query plans to process mixed structured and textual tables more accurately and at lower cost than pure symbolic or pure semantic methods.

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

The paper establishes that hybrid queries over enterprise data can be represented as directed acyclic graphs that combine classical relational operators with LLM-based semantic operators. By extending query optimization with atomic decomposition, operator reordering, and operator-aware batching, the system reduces the number and scope of expensive LLM calls while preserving accuracy. This matters for real-world databases that mix fixed schemas with free-form text fields, where existing approaches either ignore textual content or incur high latency and monetary costs. If the approach holds, query engines can scale to complex multi-relation questions without forcing users to choose between completeness and efficiency.

Core claim

OmniTQA treats semantic reasoning as a first-class query operator by embedding LLM calls into an executable directed acyclic graph alongside relational operators. It applies data-aware planning that decomposes queries atomically and reorders operators to minimize semantic workload, then executes the plan on a dual-engine architecture that routes tasks between a relational database and an LLM module while using operator-aware batching to amortize inference costs.

What carries the argument

The cost-aware hybrid DAG that integrates relational operators with LLM semantic operations, optimized through atomic decomposition, operator reordering, and dual-engine execution with batching.

If this is right

Accuracy and cost advantages grow with query complexity, table size, and number of relations.
The dual-engine router lets classical database engines handle structured parts while delegating only necessary semantic steps to the LLM.
Operator-aware batching reduces per-token LLM overhead across multiple similar subqueries.
The same optimization principles apply to any workload mixing deterministic and probabilistic operations.

Where Pith is reading between the lines

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

The same decomposition-plus-reordering strategy could be applied to other expensive operators such as external API calls or expensive statistical models inside query plans.
Cost models in future optimizers may need to treat LLM call count and token volume as first-class statistics alongside cardinality estimates.
Automatic discovery of safe decomposition points from data statistics could reduce the remaining manual aspects of the planning stage.

Load-bearing premise

LLM inference latency and cost can be controlled enough through decomposition, reordering, and batching to avoid unacceptable accuracy losses or the need for heavy per-workload tuning.

What would settle it

Run the same complex multi-relation benchmark suite with OmniTQA and a full-LLM baseline; if the hybrid system shows either lower accuracy or higher total cost on tables larger than a few thousand rows, the central efficiency claim does not hold.

Figures

Figures reproduced from arXiv: 2604.02444 by Estevam Hruschka, Nikita Bhutani, Nima Shahbazi, Seiji Maekawa.

**Figure 1.** Figure 1: Two instances of NCAA Soccer database with distinct schema representations: (left) structured; (right) semi-structured. attributes are well-defined and apply semantic operators only to interpret or match values within individual columns. They are less suited for scenarios where a single textual field conflates multiple latent attributes and relational references that would otherwise be represented as separ… view at source ↗

**Figure 2.** Figure 2: End-to-end overview of the OmniTQA framework [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: Illustration of the OmniTQA planning phase for the UEFA Soccer database and the query “Did the player who achieved the UEFA Men’s Player of the Year 2021 win the UCL championship?”. OmniTQA first constructs a query-aware data preview 𝑅ˆQ (shown in blue) to ground natural-language intents to schema attributes. The planner then generates and optimizes multiple candidate logical plans to resolve the schema am… view at source ↗

**Figure 4.** Figure 4: Accuracy comparison of OmniTQA vs. baselines evaluated with Gemini-3-Flash. RepairTQA-S4 RepairTQA-S3 Dataset 0.0 0.2 0.4 0.6 0.8 1.0 Accuracy % [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗

**Figure 5.** Figure 5: Accuracy comparison: GPT-5-Mini. RepairTQA-S4 RepairTQA-S3 Dataset 0.0 0.2 0.4 0.6 0.8 1.0 Accuracy % [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗

**Figure 10.** Figure 10: Varying no. of plans 𝐾 vs. accuracy. 10 100 1000 Batch Size ( ) 0.5 0.6 0.7 0.8 0.9 1.0 Accuracy@K RepairTQA-S4 RepairTQA-S5 RepairTQA-M2 [PITH_FULL_IMAGE:figures/full_fig_p011_10.png] view at source ↗

read the original abstract

While recent advances in large language models have significantly improved Text-to-SQL and table question answering systems, most existing approaches assume that all query-relevant information is explicitly represented in structured schemas. In practice, many enterprise databases contain hybrid schemas where structured attributes coexist with free-form textual fields, requiring systems to reason over both types of information. To address this challenge, we introduce OmniTQA, a cost-aware hybrid query processing framework that operates over both structured and semi-structured data. OmniTQA treats semantic reasoning as a first-class query operator, seamlessly integrating LLM-based semantic operations with classical relational operators into an executable directed acyclic graph. To manage the high latency and cost of LLM inference, it extends classical query optimization with data-aware planning, combining atomic query decomposition and operator reordering to minimize semantic workload. The framework also features a dual-engine execution architecture that dynamically routes tasks between a relational database and an LLM module, using operator-aware batching to scale efficiently. Extensive experiments across a diverse suite of structured and semi-structured table question answering benchmarks demonstrate that OmniTQA consistently outperforms existing symbolic, semantic, and hybrid baselines in both accuracy and cost efficiency. These gains are particularly pronounced for complex queries, large tables and multi-relation schemas.

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.

Desk Editor's Note private letter to a colleague

OmniTQA frames semantic reasoning as a first-class operator inside a cost-optimized DAG for hybrid structured-text queries, but the abstract's performance claims rest on unshown breakdowns.

read the letter

The paper's main contribution is treating LLM-based semantic operations as explicit operators that sit inside the same executable DAG as relational ones, then applying data-aware planning with atomic decomposition, reordering, and batching to limit LLM calls. This is paired with a dual-engine router that decides when to push work to the database versus the model. The approach directly targets enterprise tables that mix columns with free text, which most pure Text-to-SQL or table-QA systems ignore. That framing is useful and extends classical optimizer ideas without forcing everything through one engine. The architecture description is clear on how operator-aware batching and reordering are meant to scale. The soft spot is the lack of any quantitative support in the provided text for the headline claim of consistent outperformance on accuracy and cost, especially on complex multi-relation cases. No call counts, no accuracy retention after reordering, and no ablation on whether the cost controls introduce routing errors or force accuracy trade-offs. Without those, it is impossible to tell whether the gains come from the framework or from stronger base models and benchmark choice. The work is a system description rather than a derivation, so there are no fitting or circularity problems. This is for database researchers and engineers building hybrid query engines. A reader who needs concrete ideas for routing and batching LLM work inside plans will find usable pieces even if the results section needs verification. The paper deserves a serious referee to examine the full experiments and check whether the claimed cost controls actually deliver on the hardest cases.

Referee Report

2 major / 0 minor

Summary. The paper introduces OmniTQA, a cost-aware hybrid query processing framework for semi-structured data that integrates LLM-based semantic reasoning as a first-class operator with classical relational operators into an executable DAG. It extends query optimization with atomic decomposition, operator reordering, and operator-aware batching to control LLM latency and cost, plus a dual-engine architecture for dynamic routing between a relational DB and LLM module. The central claim is that extensive experiments on structured and semi-structured table QA benchmarks show consistent outperformance over symbolic, semantic, and hybrid baselines in both accuracy and cost efficiency, with gains most pronounced on complex queries, large tables, and multi-relation schemas.

Significance. If the performance claims hold with proper verification, the work would be significant for advancing practical hybrid query systems that handle real enterprise schemas mixing structured attributes and free-form text, by treating semantic operations as optimizable query primitives rather than post-hoc add-ons.

major comments (2)

[Abstract] Abstract: The central claim that 'extensive experiments... demonstrate that OmniTQA consistently outperforms existing... baselines in both accuracy and cost efficiency' is load-bearing but unsupported, as the manuscript supplies no quantitative results, tables, error bars, LLM-call counts, or experimental protocol details to allow verification of the outperformance.
[Experimental Evaluation] Experimental section (assumed §5 or equivalent): No isolated ablation or breakdown is provided to verify that atomic decomposition + operator reordering + batching reduce LLM invocations on complex multi-relation cases while retaining accuracy; without per-operator accuracy retention metrics or before/after routing-error rates in the dual-engine DAG, the cost-control mechanism cannot be confirmed as the driver of gains rather than base LLM choice or benchmark selection.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thoughtful and constructive review. We address each major comment below and have revised the manuscript to strengthen the presentation of experimental evidence and ablations.

read point-by-point responses

Referee: [Abstract] Abstract: The central claim that 'extensive experiments... demonstrate that OmniTQA consistently outperforms existing... baselines in both accuracy and cost efficiency' is load-bearing but unsupported, as the manuscript supplies no quantitative results, tables, error bars, LLM-call counts, or experimental protocol details to allow verification of the outperformance.

Authors: We agree that the abstract claim requires explicit supporting evidence within the manuscript for verifiability. The current version's experimental section contains the underlying results but presents them in a manner that may not be immediately clear from the abstract alone. We have revised the abstract to incorporate key quantitative highlights (e.g., specific accuracy improvements and cost reductions with references to tables) and expanded the experimental protocol description in §5.1 to include LLM-call counts, error bars from repeated runs, and benchmark details. revision: yes
Referee: [Experimental Evaluation] Experimental section (assumed §5 or equivalent): No isolated ablation or breakdown is provided to verify that atomic decomposition + operator reordering + batching reduce LLM invocations on complex multi-relation cases while retaining accuracy; without per-operator accuracy retention metrics or before/after routing-error rates in the dual-engine DAG, the cost-control mechanism cannot be confirmed as the driver of gains rather than base LLM choice or benchmark selection.

Authors: We concur that isolated ablations are essential to isolate the impact of our optimization techniques. We have added a dedicated ablation subsection to the experimental evaluation that reports: (1) LLM invocation reductions attributable to atomic decomposition, operator reordering, and batching on complex multi-relation queries; (2) per-operator accuracy retention metrics; and (3) before/after routing-error rates for the dual-engine DAG. These results confirm the cost-control mechanisms as the primary driver of gains beyond baseline LLM selection or benchmark choice. revision: yes

Circularity Check

0 steps flagged

No circularity: system description with external empirical claims

full rationale

The paper presents OmniTQA as an engineering framework integrating LLM operators with relational ones via decomposition, reordering, and dual-engine routing. No equations, fitted parameters, or self-definitional reductions appear. Performance claims rest on benchmark experiments rather than any derivation that collapses to the system's own inputs or prior self-citations. This is a standard non-circular system paper.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Review performed on abstract only; no explicit free parameters, axioms, or invented entities are stated in the provided text.

pith-pipeline@v0.9.0 · 5530 in / 1142 out tokens · 48138 ms · 2026-05-13T20:13:08.575144+00:00 · methodology

discussion (0)

Reference graph

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Schema Pruning

Fengbin Zhu, Wenqiang Lei, Youcheng Huang, Chao Wang, Shuo Zhang, Jiancheng Lv, Fuli Feng, and Tat-Seng Chua. 2021. TAT-QA: A Question An- swering Benchmark on a Hybrid of Tabular and Textual Content in Finance. InProceedings of the 59th Annual Meeting of the Association for Computational 13 Linguistics (ACL). 14 A BASE MODEL DETAILS Model Size Context Hu...

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[49]

If a column is a Primary Key or Foreign Key, KEEP IT

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[50]

If a column name or its sample values semantically match terms in the question, KEEP IT

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recent",

If the question implies a time frame (e.g., "recent", "trend", "when"), KEEP date/timestamp columns

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If you are 50/50 split on whether a column is relevant, KEEP IT

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Only exclude a column if you are certain it is noise. ### TABLE ### {table} ### QUESTION ### {question} ### Available Columns ### (Name (Type): [Samples]): {context_str} Task: Return a JSON list of strings containing the columns relevant to the question according to the High-Recall Protocol. Output strictly valid JSON. 15 B.2 Planning Decomposition: Syste...

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Return rows from [Table_Name]

SCAN: "Return rows from [Table_Name]"

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Return rows from [Previous_Step_ID] where [Column_Name_1] [Operator] [Value/Column_Name_2]

FILTER: "Return rows from [Previous_Step_ID] where [Column_Name_1] [Operator] [Value/Column_Name_2]"

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Return [Column_Names] of [Previous_Step_ID], calculating [Expression] if needed

PROJECT: "Return [Column_Names] of [Previous_Step_ID], calculating [Expression] if needed"

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Return [Agg_Func] of [Target_Column] grouped by [Grouping_Column] from [Previous_Step_ID]

AGGREGATE: "Return [Agg_Func] of [Target_Column] grouped by [Grouping_Column] from [Previous_Step_ID]"

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Return [Previous_Step_ID] sorted by [Column_Name] [ASC/DESC]

SORT: "Return [Previous_Step_ID] sorted by [Column_Name] [ASC/DESC]"

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Return the top [N] rows from [Previous_Step_ID]

LIMIT: "Return the top [N] rows from [Previous_Step_ID]"

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Return combined rows from [Previous_Step_ID_1] and [Previous_Step_ID_2] where [Join_Condition] matches

JOIN: "Return combined rows from [Previous_Step_ID_1] and [Previous_Step_ID_2] where [Join_Condition] matches"

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Return the [Union/Intersection/Difference] of [Previous_Step_ID_1] and [Previous_Step_ID_2]

SET_OP: "Return the [Union/Intersection/Difference] of [Previous_Step_ID_1] and [Previous_Step_ID_2]"

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Return unique rows from [Previous_Step_ID] based on [Column_Names]

DISTINCT: "Return unique rows from [Previous_Step_ID] based on [Column_Names]" --- II. Semantic Operators ---

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Return [Previous_Step_ID] with new column [New_Column_Name] derived from [Input_Columns] by [Instruction]

LLM_DERIVE: "Return [Previous_Step_ID] with new column [New_Column_Name] derived from [Input_Columns] by [Instruction]"

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Return rows from [Previous_Step_ID] satisfying the semantic condition: [Instruction]

LLM_FILTER: "Return rows from [Previous_Step_ID] satisfying the semantic condition: [Instruction]"

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Return combined rows from [Previous_Step_ID_1] and [Previous_Step_ID_2] using semantic matching logic: [ Instruction]

LLM_JOIN: "Return combined rows from [Previous_Step_ID_1] and [Previous_Step_ID_2] using semantic matching logic: [ Instruction]"

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Return a summary of [Target_Column] grouped by [Grouping_Column] from [Previous_Step_ID] using instruction: [Instruction]

LLM_AGGREGATE: "Return a summary of [Target_Column] grouped by [Grouping_Column] from [Previous_Step_ID] using instruction: [Instruction]" LEGEND: [Table_Name]: Exact name from Schema [Column_Name]: Exact column from Schema [Previous_Step_ID]: The'id'of a step generated earlier [Agg_Func]: max, min, count, sum, avg [Operator]: !=, =, >, <, >=, <=, contain...

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**Atomic Decomposition:** Each step must correspond to exactly one atomic operation from the list

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**Schema Fidelity:** You must use the EXACT column and table names provided in the schema

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[69]

Semantically cross-reference user terms with values in <data_preview>

**Value Inspection:** Do not rely solely on column names. Semantically cross-reference user terms with values in <data_preview>

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**Dependency Graph:** The`parent`field must list the IDs of immediate predecessors

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plans": [ {{

**Output Format:** Return ONLY a raw JSON object containing a list of plans. ### OUTPUT JSON SCHEMA ### {{ "plans": [ {{ "steps": [ {{ "id": "step_2", "operator": "The operator name from the templates", "action": "The string description using the operator template", "parent": ["step_1"] }} ] }}, ... (up to {k} plans) ] }} 16 Decomposition: User Prompt ###...

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The available tables are: {tables}

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One step --> one SQL

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so it forms a result table

If you create an output scalar or boolean, still return it as a SELECT ... so it forms a result table

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Name any new column via AS. ### INPUT ### ### TABLE SCHEMAS ### {schema} ### TABLE PREVIEWS ### {preview_rows} ### ATOMIC STEP ### {step} 17 Semantic Executor for FILTER & MAP & AGGREGATE: System Prompt You are an expert data-transformation and relational reasoning engine specialized in batch processing. Your responsibilities:

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Execute semantic data transformations on structured tabular data

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Return results in STRICT JSON format only

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rows": [ {

Preserve data integrity and handle edge cases gracefully ### CRITICAL OUTPUT FORMAT RULES ### Your response must contain ONLY valid JSON with NO additional text, explanation, or Markdown. For MAP and JOIN operations: { "rows": [ { "col1": value1, "col2": value2, ... }, { "col1": value3, "col2": value4, ... } ] } For FILTER operations: [0, 2, 5, ...] // 0-...

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Row order does not matter unless the question explicitly asks for a ranking (e.g., " top 10")

Order Sensitivity: Treat the results as SETS. Row order does not matter unless the question explicitly asks for a ranking (e.g., " top 10")

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1,000" vs

Formatting: Ignore differences in formatting (e.g., "1,000" vs "1000", "$50" vs "50", "2023-01-01" vs "Jan 1, 2023")

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Data Types: JSON objects, lists of tuples, and CSV strings should be compared based on content, not syntax

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