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arxiv: 2606.22681 · v1 · pith:6NXN6FQRnew · submitted 2026-06-21 · 💻 cs.CL · cs.AI

Only Ask What You Don't Know: Grounded Delta Planning for Efficient Multi-step RAG

Wei-Chieh Chou , Xuanjun Chen , Jian-Ren Lin , Claire Lin , Hung-yi Lee , Jyh-Shing Roger Jang This is my paper

Pith reviewed 2026-06-26 10:08 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords multi-hop question answeringretrieval-augmented generationplanningdelta planningefficient RAGgap-conditioned promptsHotpotQAMuSiQue
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The pith

GDP-RAG improves multi-hop RAG by grounding plans in a preliminary retrieval pass and then retrieving only the information gaps, reaching 60.63% accuracy at lower cost than prior methods.

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

The paper presents Grounded Delta Planning RAG (GDP-RAG) as a way to reduce error propagation and wasted steps in iterative retrieval-augmented generation for multi-hop questions. It begins with a preliminary retrieval to ground the plan, then uses a prompt that explicitly asks the model to name only the missing information needed. A skeletal trajectory carries evidence from that first pass forward through each subquery. On HotpotQA, 2WikiMultiHopQA, and MuSiQue this produces the highest accuracy among compared systems together with a cost-of-pass 22% below PAR-RAG and 68% below KnowTrace.

Core claim

GDP-RAG is a plan-based framework that targets only the information delta based on three design choices: preliminary retrieval to ground planning before execution, a gap-conditioned planning prompt that asks only for missing information, and a skeletal trajectory that pairs each subquery with a Thought capturing evidence from preliminary retrieval and carrying it through to the final answer. The method focuses computation on unresolved gaps and yields concise, reliable reasoning trajectories.

What carries the argument

Grounded Delta Planning, which combines a preliminary retrieval pass with a gap-conditioned prompt that identifies only missing information before any further retrieval occurs.

If this is right

  • Multi-hop QA can be performed with fewer retrieval rounds while preserving or increasing answer accuracy.
  • Error accumulation across iterative retrieval is reduced when plans are conditioned on already-retrieved evidence.
  • Over-generation of unnecessary reasoning steps is avoided by restricting the planner to explicit information gaps.
  • No compared method simultaneously exceeded GDP-RAG accuracy and undercut its cost-of-pass.

Where Pith is reading between the lines

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

  • The same preliminary-grounding pattern could be tested on iterative tasks outside question answering, such as multi-step code generation or tool use.
  • If the first retrieval pass is weak in a new domain, adding a second cheap initial pass might be needed before planning begins.
  • The skeletal trajectory structure may also limit hallucination by forcing every subquery to reference already-grounded evidence.

Load-bearing premise

The preliminary retrieval pass reliably surfaces enough evidence for the planner to identify true missing information without missing critical context that would appear only in later rounds.

What would settle it

A controlled test set in which the initial retrieval consistently omits at least one fact required to answer the question, with measurement of whether GDP-RAG accuracy then drops below the strongest baseline.

Figures

Figures reproduced from arXiv: 2606.22681 by Claire Lin, Hung-yi Lee, Jian-Ren Lin, Jyh-Shing Roger Jang, Wei-Chieh Chou, Xuanjun Chen.

Figure 1
Figure 1. Figure 1: (A) Direct Planning decomposes queries using only parametric knowledge, produc [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: GDP-RAG framework. The workflow proceeds in three phases: (1) [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Experimental results of the proposed GDPRAG framework. (a) shows the trade-off [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Comparison of planning statistics (a) and reasoning complexity by query hops (b). [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Prompt of Direct Planning [PITH_FULL_IMAGE:figures/full_fig_p015_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Prompt of Grounded Delta Planning. Direct Planning Prompt As shown in [PITH_FULL_IMAGE:figures/full_fig_p015_6.png] view at source ↗
read the original abstract

Multi-hop question answering remains challenging for Retrieval-Augmented Generation (RAG) because existing approaches either propagate errors across iterative retrieval rounds or over-generate reasoning steps, increasing cost without improving accuracy. We propose Grounded Delta Planning RAG (GDP-RAG), a plan-based framework that targets only the information delta based on three simple design choices: (1) preliminary retrieval to ground planning before execution, (2) a gap-conditioned planning prompt that asks only for missing information, and (3) a skeletal trajectory that pairs each subquery with a Thought capturing evidence from preliminary retrieval and carrying it through to the final answer. GDP-RAG focuses computation on unresolved gaps, yielding concise, reliable reasoning trajectories. Extensive experiments on HotpotQA, 2WikiMultiHopQA, and MuSiQue show that GDP-RAG achieves the highest accuracy (60.63%) among all compared systems while maintaining a cost-of-pass of 0.51, 22% lower than PAR-RAG (0.65) and 68% lower than KnowTrace (1.57), with no method achieving both higher accuracy and lower cost.

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 paper proposes Grounded Delta Planning RAG (GDP-RAG), a plan-based framework for multi-hop QA that relies on three design choices: (1) a preliminary retrieval pass to ground planning, (2) a gap-conditioned prompt that requests only missing information, and (3) a skeletal trajectory pairing subqueries with Thoughts from the preliminary pass. Experiments on HotpotQA, 2WikiMultiHopQA, and MuSiQue claim GDP-RAG attains the highest accuracy (60.63%) at the lowest cost-of-pass (0.51), outperforming baselines such as PAR-RAG (0.65) and KnowTrace (1.57) with no method dominating on both metrics.

Significance. If the experimental claims hold under rigorous controls, the work would be significant for demonstrating that targeted delta planning grounded in an initial retrieval can simultaneously improve accuracy and reduce retrieval cost in multi-step RAG, a practical advance over iterative or over-generating baselines.

major comments (2)
  1. [Experiments] Experiments section: the headline accuracy (60.63%) and cost-of-pass (0.51) figures are reported without error bars, dataset split details, number of runs, ablation studies, or statistical significance tests. Because the central claim that GDP-RAG dominates the accuracy-cost frontier rests entirely on these numbers, the absence of these controls makes the result difficult to evaluate.
  2. [Method] Method, design choice (1): the framework assumes a single preliminary retrieval pass will surface sufficient evidence for the gap-conditioned planner to identify all true deltas on multi-hop items. No analysis or failure-case breakdown is provided for HotpotQA-style questions where supporting facts are not co-retrieved in the first round; this assumption is load-bearing for both the accuracy and the reported cost advantage.
minor comments (1)
  1. The abstract and method description would benefit from explicit notation for the skeletal trajectory and cost-of-pass metric to improve reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thoughtful comments, which highlight important areas for strengthening the experimental rigor and methodological analysis. We address each point below and will incorporate revisions to address the concerns.

read point-by-point responses

  1. Referee: [Experiments] Experiments section: the headline accuracy (60.63%) and cost-of-pass (0.51) figures are reported without error bars, dataset split details, number of runs, ablation studies, or statistical significance tests. Because the central claim that GDP-RAG dominates the accuracy-cost frontier rests entirely on these numbers, the absence of these controls makes the result difficult to evaluate.

    Authors: We agree that the current presentation of results would benefit from additional statistical controls. In the revised manuscript we will report results aggregated over multiple runs (with means and standard deviations), explicitly state the dataset splits employed, include ablation studies isolating each of the three design choices, and add statistical significance tests comparing GDP-RAG against the strongest baselines. revision: yes

  2. Referee: [Method] Method, design choice (1): the framework assumes a single preliminary retrieval pass will surface sufficient evidence for the gap-conditioned planner to identify all true deltas on multi-hop items. No analysis or failure-case breakdown is provided for HotpotQA-style questions where supporting facts are not co-retrieved in the first round; this assumption is load-bearing for both the accuracy and the reported cost advantage.

    Authors: The preliminary retrieval step is intended to provide grounding for subsequent planning, and the overall empirical results support its utility. We acknowledge that a dedicated failure-case analysis for instances where supporting facts are not co-retrieved would strengthen the paper. In the revision we will add such an analysis, including quantitative breakdown of retrieval coverage on multi-hop items and qualitative examination of how the gap-conditioned prompt behaves when the initial pass is incomplete. revision: yes

Circularity Check

0 steps flagged

No circularity; performance metrics are direct experimental outcomes with no derivation chain.

full rationale

The paper describes a RAG framework via three explicit design choices and reports empirical accuracy (60.63%) and cost-of-pass (0.51) on HotpotQA, 2WikiMultiHopQA, and MuSiQue. No equations, fitted parameters, self-citations, or uniqueness theorems are invoked to derive results. The reported figures are presented as measured experimental outcomes, not quantities that reduce to the inputs by construction. The preliminary-retrieval assumption is a methodological choice whose sufficiency is evaluated empirically rather than presupposed in a tautological manner.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no explicit free parameters, axioms, or invented entities; all technical detail is deferred to the unavailable full text.

pith-pipeline@v0.9.1-grok · 5748 in / 1093 out tokens · 21040 ms · 2026-06-26T10:08:20.212349+00:00 · methodology

discussion (0)

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Reference graph

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