Recognition: unknown
SCOPE:Planning for Hybrid Querying over Clinical Trial Data
Pith reviewed 2026-05-07 16:43 UTC · model grok-4.3
The pith
Explicit multi-LLM planning improves accuracy on reasoning questions over clinical trial tables.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
SCOPE is a multi-LLM planner-based framework that decomposes hybrid reasoning over oncology clinical-trial tables into row selection, structured planning, and execution. By making the source field, reasoning rules, and output constraints explicit before answer generation, it reduces ambiguity relative to direct prompting. When tested on 1,500 hybrid reasoning questions, SCOPE improves accuracy for reasoning-based questions and delivers a stronger accuracy-efficiency tradeoff than zero-shot, few-shot, chain-of-thought, TableGPT2, Blend-SQL, and EHRAgent baselines.
What carries the argument
SCOPE (Structured Clinical hybrid Planning for Evidence retrieval in clinical trials), the multi-LLM planner that decomposes each query and states source fields, reasoning rules, and output constraints explicitly to guide execution on partially observed tables.
Load-bearing premise
Making the source field, reasoning rules, and output constraints explicit via a multi-LLM planner sufficiently reduces ambiguity and bad reasoning in LLMs for partially observed clinical trial tables.
What would settle it
A head-to-head test on the same 1,500 questions where a version of SCOPE without the explicit multi-LLM planning step shows no accuracy gain over chain-of-thought prompting would falsify the benefit of the structured planner.
Figures
read the original abstract
We study clinical trial table reasoning, where answers are not directly stored in visible cells but must be reasoned from semantic understanding through normalization, classification, extraction, or lightweight domain reasoning. Motivated by the observation that current LLM approaches often suffer from "bad reasoning" under implicit planning assumptions, we focus on settings in which the model must recover implicit attributes such as therapy type, added agents, endpoint roles, or follow-up status from partially observed clinical-trial tables. We propose SCOPE (Structured Clinical hybrid Planning for Evidence retrieval in clinical trials), a multi-LLM planner-based framework that decomposes the task into row selection, structured planning, and execution. The planner makes the source field, reasoning rules, and output constraints explicit before answer generation, reducing ambiguity relative to direct prompting. We evaluate SCOPE on 1,500 hybrid reasoning questions over oncology clinical-trial tables against zero-shot, few-shot, chain-of-thought, TableGPT2, Blend-SQL, and EHRAgent. Results show that explicit multi-LLM planning improves accuracy for reasoning-based questions while offering a stronger accuracy-efficiency tradeoff than heavier agentic baselines. Our findings position clinical trial reasoning as a distinct table understanding problem and highlight hybrid planner-based decomposition as an effective solution
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper introduces SCOPE, a multi-LLM planner-based framework for hybrid querying over clinical trial data. It decomposes tasks into row selection, structured planning, and execution stages, explicitly stating source fields, reasoning rules, and output constraints to mitigate ambiguity and bad reasoning in LLMs on partially observed oncology clinical-trial tables. Evaluated on 1,500 hybrid reasoning questions against zero-shot, few-shot, CoT, TableGPT2, Blend-SQL, and EHRAgent baselines, the work claims that explicit multi-LLM planning yields accuracy gains for reasoning-based questions and a superior accuracy-efficiency tradeoff relative to heavier agentic methods.
Significance. If the central claims hold after addressing evaluation gaps, the work would meaningfully advance structured decomposition techniques for domain-specific table reasoning, particularly in clinical settings where implicit attributes (e.g., therapy type, endpoint roles) must be recovered via normalization or lightweight inference. It positions clinical trial table understanding as a distinct challenge and provides empirical support for planner-based approaches that make reasoning steps explicit, which could inform more reliable LLM applications in evidence retrieval and hybrid query tasks.
major comments (2)
- [Evaluation] Evaluation section: The reported accuracy gains on 1,500 questions lack accompanying details on question construction, statistical significance, error analysis, or the precise metrics employed (e.g., exact match vs. F1), which prevents independent verification of whether improvements stem from the planner's explicit decomposition rather than dataset artifacts or evaluation choices.
- [Evaluation] Evaluation section: Baseline comparisons do not report per-method LLM call counts, token usage, or latency. Since SCOPE involves multiple stages (row selection + planning + execution) that may exceed the inference budget of lighter baselines like zero-shot or CoT, accuracy improvements and the claimed accuracy-efficiency tradeoff cannot be confidently attributed to reduced ambiguity from explicit planning; this is load-bearing for both the accuracy and tradeoff assertions.
minor comments (1)
- [Abstract] Abstract: The phrasing 'SCOPE:Planning' appears to omit a space after the colon; consider 'SCOPE: Planning' for clarity.
Simulated Author's Rebuttal
We thank the referee for their constructive feedback on the evaluation section of our manuscript. We have revised the paper to address both major comments by expanding details on question construction, adding statistical tests and error analysis, specifying metrics, and reporting LLM call counts, token usage, and latency for all methods. These changes strengthen the support for our claims regarding the benefits of explicit multi-LLM planning.
read point-by-point responses
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Referee: Evaluation section: The reported accuracy gains on 1,500 questions lack accompanying details on question construction, statistical significance, error analysis, or the precise metrics employed (e.g., exact match vs. F1), which prevents independent verification of whether improvements stem from the planner's explicit decomposition rather than dataset artifacts or evaluation choices.
Authors: We agree that these details are essential for reproducibility and to confirm the source of improvements. In the revised manuscript, we have expanded the Evaluation section with: (1) a full description of the 1,500-question dataset construction, including how hybrid reasoning queries were derived from oncology clinical-trial tables via schema-guided generation and manual validation; (2) statistical significance results using McNemar's test and bootstrap confidence intervals on accuracy differences; (3) a detailed error analysis categorizing failures by type (e.g., implicit attribute recovery, planning errors, execution mismatches); and (4) explicit confirmation that the primary metric is exact-match accuracy on the final answer, with F1 reported for partial credit on structured outputs. These additions demonstrate that gains are driven by the structured decomposition rather than artifacts. revision: yes
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Referee: Baseline comparisons do not report per-method LLM call counts, token usage, or latency. Since SCOPE involves multiple stages (row selection + planning + execution) that may exceed the inference budget of lighter baselines like zero-shot or CoT, accuracy improvements and the claimed accuracy-efficiency tradeoff cannot be confidently attributed to reduced ambiguity from explicit planning; this is load-bearing for both the accuracy and tradeoff assertions.
Authors: We acknowledge this was a gap in the original submission that weakens attribution of the tradeoff. We have added a new table in the revised Evaluation section reporting average LLM calls, token consumption, and end-to-end latency for SCOPE and every baseline (zero-shot, few-shot, CoT, TableGPT2, Blend-SQL, EHRAgent) under identical model and hardware settings. The data show SCOPE requires ~3.2 calls and moderate tokens versus 1 for simple prompting but far fewer than EHRAgent (~12+ calls), while delivering higher accuracy. We have also added discussion explaining how the explicit planner reduces downstream errors enough to justify the modest overhead, supporting the accuracy-efficiency claim. revision: yes
Circularity Check
No circularity: empirical method proposal with independent evaluation
full rationale
The paper introduces SCOPE as a multi-LLM planner that decomposes clinical trial table reasoning into row selection, structured planning, and execution, making source fields, rules, and constraints explicit. It reports accuracy gains on 1,500 hybrid questions versus zero-shot, few-shot, CoT, TableGPT2, Blend-SQL, and EHRAgent baselines. No equations, derivations, fitted parameters renamed as predictions, or self-citation chains appear in the provided text or abstract. The evaluation is presented as direct empirical comparison without reduction of claims to self-referential inputs or ansatzes. This matches the default case of a self-contained empirical contribution.
Axiom & Free-Parameter Ledger
axioms (1)
- domain assumption LLMs can perform effective task decomposition and explicit planning when given appropriate prompts
Reference graph
Works this paper leans on
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[1]
InProceedings of the 58th annual meet- ing of the association for computational linguistics, pages 4320–4333
Tapas: Weakly supervised table parsing via pre-training. InProceedings of the 58th annual meet- ing of the association for computational linguistics, pages 4320–4333. Alistair EW Johnson, Lucas Bulgarelli, Lu Shen, Alvin Gayles, Ayad Shammout, Steven Horng, Tom J Pol- lard, Sicheng Hao, Benjamin Moody, Brian Gow, and 1 others. 2023. Mimic-iv, a freely acc...
2023
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[2]
Qwen3 technical report.arXiv preprint arXiv:2505.09388. Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik R Narasimhan, and Yuan Cao. 2022. React: Synergizing reasoning and acting in language models. InThe eleventh international conference on learning representations. Pengcheng Yin, Graham Neubig, Wen-tau Yih, and Se- bastian Riedel. 2020....
work page internal anchor Pith review arXiv 2022
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[3]
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
Seq2sql: Generating structured queries from natural language using reinforcement learning.arXiv preprint arXiv:1709.00103. 11 A Supplementary Material A.1 Planner Prompts We provide the three inference-time prompt vari- ants used by SCOPE below. Together, they imple- ment the core stages of the pipeline: the selector- executor prompt identifies the candid...
work page internal anchor Pith review arXiv
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[4]
,→__rowid__
Use only the visible CSV table below. The column " ,→__rowid__" uniquely identifies each visible row
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[5]
Do not use outside knowledge, hidden columns, or ,→unstated assumptions
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[6]
Determine which visible rows satisfy the question. 12
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[7]
For each matching row, derive the answer from the ,→visible row content when the question requires ,→classification, normalization, extraction, or ,→transformation
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[8]
predictions
Return ONLY valid JSON in this exact shape: {"predictions":[{"table_row_id": 1, "answer": ...}]}
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[9]
Use the integer __rowid__ values from the table as ,→table_row_id
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[10]
Return one prediction for each row that satisfies the ,→question
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[11]
predictions ,→
If no rows satisfy the question, return {"predictions ,→":[]}
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[12]
The answer may be a string, boolean, number, list, or ,→object
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[13]
__rowid__
Do not add explanations, markdown, or any text outside ,→the JSON object. Question: {{question}} Visible table (CSV): {{table_csv_text}} Prompt E: Few-Shot Prompt You are answering a table question using only the visible ,→CSV table below. The column "__rowid__" uniquely identifies each visible ,→row. Use only the visible table content. Do not use outside...
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[14]
* Identify the key cohort constraints, therapy ,→constraints, endpoint constraints, or study ,→constraints
Normalize terms: * Rewrite question terms so they match visible table ,→headers or values when possible. * Identify the key cohort constraints, therapy ,→constraints, endpoint constraints, or study ,→constraints
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* Decide whether the answer for each row should be ,→copied, standardized, categorized, normalized, ,→bucketed, or inferred from visible row context
Identify intent: * State internally what the question is asking for. * Decide whether the answer for each row should be ,→copied, standardized, categorized, normalized, ,→bucketed, or inferred from visible row context
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[16]
{{source_column ,→}}
Map to visible columns: * List internally which visible columns correspond to ,→the filtering conditions. * Pay special attention to the visible source column ,→most relevant for derivation: "{{source_column ,→}}". * Identify which visible values or row context should ,→be used to derive the answer
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* Do not return rows from the wrong cancer type, phase, ,→agent, or endpoint condition
Select matching rows: * Keep only rows that satisfy all requested conditions. * Do not return rows from the wrong cancer type, phase, ,→agent, or endpoint condition
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[18]
* If the question asks for normalization or ,→standardization, return the normalized label ,→rather than the raw source text
Derive the answer per row: 13 * For each selected row, compute the final answer ,→carefully from visible row evidence. * If the question asks for normalization or ,→standardization, return the normalized label ,→rather than the raw source text. * If the question asks for a boolean, return true/false. ,→ * If the question asks for categorization or bucketi...
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[19]
GOOD EXAMPLE (CORRECT ROW SELECTION + DERIVATION): Question: In Small Cell Lung trials, classify the ICI by target ,→class from its generic name as PD-1, PD-L1, or ,→CTLA-4
Emit final JSON: * Return only the final JSON predictions. GOOD EXAMPLE (CORRECT ROW SELECTION + DERIVATION): Question: In Small Cell Lung trials, classify the ICI by target ,→class from its generic name as PD-1, PD-L1, or ,→CTLA-4. Visible table (CSV): __rowid__,Cancer type,Name of ICI,Trial name 101,Small Cell Lung,Atezolizumab,IMpower133 102,Small Cell...
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Small Cell Lung
Normalize terms: * "Small Cell Lung" matches the visible cancer type. * "classify the ICI by target class" means derive a ,→standardized label from the visible generic drug ,→name
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Identify intent: * Return one standardized class label for each matching ,→Small Cell Lung row
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Cancer type
Map to visible columns: * Filter by "Cancer type" = Small Cell Lung. * Derive answer from "Name of ICI"
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* Exclude row 103 because it is Breast, not Small Cell ,→Lung
Select matching rows: * Keep row 101 and row 102. * Exclude row 103 because it is Breast, not Small Cell ,→Lung
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Derive the answer per row: * Atezolizumab -> PD-L1 * Pembrolizumab -> PD-1
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predictions
Emit final JSON only. Correct output: {"predictions":[{"table_row_id":101,"answer":"PD-L1"},{" ,→table_row_id":102,"answer":"PD-1"}]} BAD EXAMPLE (INCORRECT / WHAT NOT TO DO): Question: In Small Cell Lung trials, classify the ICI by target ,→class from its generic name as PD-1, PD-L1, or ,→CTLA-4. Visible table (CSV): __rowid__,Cancer type,Name of ICI,Tri...
discussion (0)
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