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REVIEW 3 major objections 6 minor 23 references

Can we Retrieve Everything All at Once? ARM: An Alignment-Oriented LLM-based Retrieval Method

T0 review · 3 major / 6 minor · reviewed 2026-08-09 · deepseek-v4-flash

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Pith's one-line read This paper claims that complex open-domain retrieval can be completed in one large-language-model call by aligning the question with the structure of the data collection, and it reports gains over standard and agentic RAG baselines on…

desk verdict A useful retrieve-all-at-once method for multi-source QA, with a real MIP-connectivity gap and evaluation gaps that need fixing before acceptance. read the letter →

arxiv 2501.18539 v1 pith:RAHHCB7O submitted 2025-01-30 cs.CL cs.AIcs.IR

Peter Baile Chen , Yi Zhang , Michael Cafarella , Dan Roth This is my paper · ORCID
classification cs.CLcs.AIcs.IR
keywords retrieval-augmentedgenerationcomplexopen-domainquestionansweringmulti-tableretrievalconstraineddecodingmixed-integerprogrammingself-verificationbridgingentities
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper argues that the bottleneck for retrieval on complex open-domain questions is not a large language model's (LLM's) ability to decompose a question, but its blindness to how the available data is organized. It proposes ARM, a retrieval method that treats retrieval as one constrained decoding process with three stages: information alignment, structure alignment, and self-verification. The intended payoff is 'retrieve everything at once': one LLM call returns all passages and tables needed to answer the question, including bridging tables that are never explicitly named. On Bird and OTT-QA, ARM reports higher downstream accuracy than standard retrieval-augmented generation (RAG) with query decomposition and agentic RAG, while using fewer LLM calls for retrieval.

What carries the argument

The load-bearing mechanism is a three-stage alignment decoding process in which each decoding step outputs an alignment to data objects instead of a single token. Information alignment uses constrained beam search with a suffix tree over N-grams to force the model's generated keywords to exist in the indexed collection. Structure alignment uses a mixed-integer program with decision variables $b_i$ (object selected) and $c_{ij}$ (objects connected) to maximize $\sum_i R_i b_i + \sum_{i,j} C_{ij} c_{ij}$, where $R_i$ is embedding cosine relevance and $C_{ij}$ is a compatibility score combining embedding similarity and exact-value overlap. The solver's selected objects and their connections are injected as a draft, the LLM verifies the draft, and beam search aggregates selections by weighted voting using token logits.

What would settle it

A reader could test this by building a multi-hop QA collection in which the only route from the found objects to the answer runs through a paraphrased value with no exact token overlap and low embedding similarity; if ARM's perfect recall then falls to dense-retrieval levels, the compatibility assumption is doing the load-bearing work.

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Extended reading notes

Core claim

ARM's central claim is that retrieval for complex questions can be formulated as a generative reasoning process whose intermediate steps are alignments to concrete data objects, rather than free-form text. In information alignment, the LLM extracts keywords and rephrases them through constrained beam decoding over N-grams indexed from the collection, so its queries match the data's actual vocabulary. In structure alignment, a mixed-integer program selects a connected set of objects by maximizing relevance of each object to the question and compatibility among objects, thereby recovering bridging tables and connecting entities. The LLM then self-verifies whether the draft objects cover the question and are connected, and beam search aggregates multiple drafts by confidence-weighted voting. On Bird the method reaches 96.5 recall and 92.7 perfect recall with one LLM call; on OTT-QA it reaches 79.8 recall and 62.5 perfect recall, outperforming the standard RAG and ReAct baselines on downstream tasks.

Load-bearing premise

The claim rests on the assumption that the compatibility score between two objects, computed from embedding similarity plus exact-value overlap, is sufficient to recognize every bridging table or connecting entity needed to answer the question.

Editorial extensions

If this is right

  • Complex multi-source questions can be retrieved in a single LLM call, reducing both latency and the number of LLM calls compared with iterative agentic retrieval.
  • Bridging tables and connecting entities can be recovered even when they are not mentioned in the question, provided they share content with already-found objects.
  • Retrieval quality for multi-object questions should be measured by perfect recall (all required objects retrieved), since a missing object makes the whole answer fail.
  • Because ARM uses the same LLM for generation and self-verification, improvements in the base LLM should directly improve retrieval completeness.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The paper leaves open a boundary: if a required bridge is expressed only through paraphrase or a synonym that shares no exact value and little embedding similarity with base objects, structure alignment may miss it; a paraphrase-heavy collection would test that boundary.
  • The alignment pipeline is modality-agnostic in principle: any object that can be serialized, chunked, and embedded, such as images or knowledge-graph triples, could be plugged into the same information and structure alignment steps.
  • One natural extension is to learn the compatibility score from data instead of hand-combining embedding similarity with Jaccard or overlap coefficients, which could reduce dependence on exact value overlap.
  • ARM's retrieve-all-at-once property is a natural fit for settings like database question answering over private schemas, where iterative search errors are costly and the data organization is fixed and known.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. The paper proposes ARM, an LLM-based retrieval method that aims to retrieve all necessary tables and passages for a complex open-domain question in a single decoding pass. ARM has three components: information alignment, which uses constrained N-gram decoding to rephrase question keywords into corpus-like N-grams; structure alignment, which formulates an integer program to select a set of mutually compatible objects; and self-verification with beam-search aggregation, which lets the LLM choose a final set of objects from multiple alignment drafts. The authors evaluate ARM on Bird and OTT-QA against dense retrieval with and without reranking and query decomposition, and against the agentic ReAct. They report consistent improvements in retrieval recall, perfect recall, F1, and end-to-end accuracy, while using far fewer LLM calls than ReAct. The central claim is that ARM achieves retrieve-all-at-once by aligning the question to the data collection's organization rather than iteratively searching.

Significance. If the method works as described, it is a meaningful step toward efficient retrieval for multi-source questions: a single LLM call replaces iterative agentic search, and the proposed alignment objective jointly considers semantic relevance and object compatibility. The paper also introduces a useful evaluation metric, perfect recall, that directly measures whether all required objects are retrieved. The authors report consistent gains across retrieval and downstream metrics, and the method is clearly described. However, the significance is conditional on correcting a load-bearing flaw in the structure-alignment formulation: the published MIP does not enforce connectedness despite the paper's claim that it does. The evaluation also has gaps around manual dataset filtering and stochastic significance. The idea is novel and potentially useful, but the current manuscript does not fully support its central claims.

major comments (3)
  1. [Section 3.2.2] The MIP formulation does not enforce that the selected objects form a connected structure, contradicting the text that says the solver returns 'a subset of the input search objects that are connected.' The constraints only impose sum_i b_i = k, sum_{i,j} c_ij <= 2(k-1), and 2 c_ij <= b_i + b_j. A feasible solution can set c_ij = 0 for all edges, or can select two disjoint pairs with c_AB = c_CD = 1 and no cross edges. In both cases the graph is disconnected, so the MIP can output an alignment draft with no connections or with multiple isolated components. Because the serialized connections are the only mechanism for discovering bridging objects, and because self-verification can only select objects already present in a draft, a missing bridge cannot be recovered later. This is not a matter of embedding quality: even with a perfect compatibility oracle, the stated optimization problem does not embody the advertised connectedness. Please add a proper connectivity constraint (e.g., a spanning-tree constraint or subtour elimination) and rerun the experiments, or explicitly revise the method description to state that connectivity is only a soft preference enforced by the objective's compatibility term.
  2. [Section 4.1] The paper removes OTT-QA questions with 'incomplete annotations' after manual inspection but never reports how many questions were removed, what fraction of the dev set this represents, or the concrete criteria used. If a substantial number of questions were excluded, the reported OTT-QA numbers are not directly comparable to numbers reported by prior work on the original dev set, and the baseline comparisons in Table 3 could be biased in either direction. Please report the number and proportion of excluded questions, provide the annotation criteria, and, ideally, run a robustness check on the full dev set or on the subset with complete annotations for all methods.
  3. [Sections 4.3 and 4.4] No variance estimates or significance tests are reported for any of the experimental results. ARM and the baselines are all stochastic LLM-based systems (or at least involve nondeterministic decoding and beam search), and the paper does not state the number of seeds or runs. The claimed improvements are not uniformly large (e.g., 2.55 points on Bird execution accuracy averaged across two generation models, 3.7 points on OTT-QA exact match), and manual filtering of OTT-QA further increases the need for statistical robustness. Please report standard deviations or confidence intervals over multiple runs and, where appropriate, paired significance tests for the main comparisons.
minor comments (6)
  1. [Section 3.3] The heading 'Self-Verfication and Aggregation' contains a typo; it should be 'Self-Verification and Aggregation.'
  2. [Section 4.2] The phrase 'with the the percentage of questions' contains a duplicated definite article; it should be 'with the percentage of questions.'
  3. [Appendix A] The sentence 'Passage-passage compatibility is computed by comparing all sentences in a passage with all sentences in the other passage and taking the pair with the the highest compatibility' contains a duplicated 'the.'
  4. [Section 4.1] The data counts are unclear: '3862 objects (3073 passages and 789 tables) for OTT-QA and 1534 questions and 75 tables in the data collection for Bird'—please specify whether these counts are after removing questions, and clarify why 75 tables yield 249515 chunks, as this seems surprisingly large.
  5. [Section 3.2.1] The prompt in Table 6 instructs the model to 'decompose the user question into contiguous, non-overlapping substrings,' but Section 3.2.1 describes extracting keywords; the terminology should be made consistent.
  6. [Section 4.5] The sentence 'First, models might for get information it generated in previous iterations' appears to have a typo; it should likely be 'models might forget information they generated.'

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: ARM's pipeline is an algorithmic search over corpus objects; its evaluations are against external benchmarks and fixed baselines, and no parameter is fitted to the gold retrieval targets.

full rationale

ARM's derivation chain is self-contained rather than circular. Information alignment (Section 3.2.1) constrains LLM decoding with corpus N-grams and scores objects by BM25 plus embedding similarity; this is a search heuristic, not a fit to gold objects. Structure alignment (Section 3.2.2) maximizes a stated a priori objective (Equation 1: R_i b_i plus C_ij c_ij) over candidate objects and expansions; the objective does not encode retrieval targets, and compatibility is defined from embeddings and exact-value overlap, not from the gold set. Self-verification (Section 3.3) selects among objects already present in MIP drafts by constrained decoding and confidence-weighted voting; this restricts the output space but does not make the output equal to an input by definition. The central components are evaluated externally against Bird and OTT-QA dev sets with standard RAG, reranking, query decomposition, and ReAct baselines; no load-bearing claim rests on a fitted parameter derived from the answer labels. The paper does cite prior work by the same authors (Chen et al., 2024c) for the MIP table-retrieval module, but that citation provides supporting prior context for a published, independently evaluable retrieval formulation, not an unverified uniqueness theorem or an input that forces the reported result. A separate concern raised in review is that Constraint 2 of the MIP only bounds the edge count and encourages connectivity rather than enforcing a single connected component; that is an internal-validity and correctness question about whether the optimization matches the prose claim, not circularity. No step in the paper reduces by construction to its own inputs.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The central empirical claim rests on measurement assumptions (embedding similarity and exact-value overlap can detect relevance and object connections) and on evaluation choices (gold annotations are complete and manual exclusions are unbiased). No new entities are introduced. The main free parameters are hyperparameters that shape retrieval but are not fully reported in the paper.

free parameters (4)
  • compatibility weight between semantic and exact-value similarity = unspecified
    Appendix A defines compatibility as a weighted sum of cosine similarity and Jaccard or overlap, but the weights are not reported; they directly affect which objects the MIP solver connects.
  • MIP selected object count k = 5 (implied by top-5 experiments and average retrieved objects of 5.00 or 4.98)
    The solver selects k objects, and k is a user-set hyperparameter that controls the number of retrieved objects and the perfect-recall trade-off.
  • expansion steps l and objects added per expansion step = unspecified (three ways of expansions)
    Section 3.2.2 expands the base object set iteratively but does not specify l or per-step counts; these significantly affect whether bridging objects are included.
  • beam size for constrained decoding = unspecified
    Beam search is used for both N-gram alignment and reasoning-process aggregation, but the beam width is not reported.
assumptions (5)
  • domain assumption Embedding cosine similarity between question and serialized object captures task relevance.
    Relevance Ri in Eq. (1) is cosine similarity; if embeddings miss key semantics, the MIP solver selects the wrong objects.
  • domain assumption Compatibility scores (cosine plus exact-value overlap) identify all joinable columns and connecting entities.
    Appendix A defines this; structure alignment depends on it to add bridging tables and passages.
  • domain assumption Gold object annotations in Bird and OTT-QA dev splits are complete and correct.
    Retrieval metrics compare against gold objects; incompleteness led the authors to manually exclude questions.
  • ad hoc to paper Manual exclusion of OTT-QA questions with incomplete annotations is unbiased.
    Section 4.1 removes questions without reporting the criteria or counts; if excluded questions favor baselines, the conclusions shift.
  • domain assumption Constrained beam decoding with an N-gram suffix tree produces semantically aligned keyword lists.
    Section 3.2.1 relies on N-gram scoring to guide question decomposition; failure here would propagate to all downstream steps.

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Cite this review

Pith. "Pith review of Can we Retrieve Everything All at Once? ARM: An Alignment-Oriented LLM-based Retrieval Method." pith.science (2026). https://pith.science/paper/RAHHCB7O

@misc{pith2026250118539,
  author       = {Pith},
  title        = {Pith review of: Can we Retrieve Everything All at Once? ARM: An Alignment-Oriented LLM-based Retrieval Method},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RAHHCB7O}},
  note         = {Machine review of arXiv:2501.18539}
}
read the original abstract

Real-world open-domain questions can be complicated, particularly when answering them involves information from multiple information sources. LLMs have demonstrated impressive performance in decomposing complex tasks into simpler steps, and previous work has used it for better retrieval in support of complex questions. However, LLM's decomposition of questions is unaware of what data is available and how data is organized, often leading to a sub-optimal retrieval performance. Recent effort in agentic RAG proposes to perform retrieval in an iterative fashion, where a followup query is derived as an action based on previous rounds of retrieval. While this provides one way of interacting with the data collection, agentic RAG's exploration of data is inefficient because successive queries depend on previous results rather than being guided by the organization of available data in the collection. To address this problem, we propose an LLM-based retrieval method -- ARM, that aims to better align the question with the organization of the data collection by exploring relationships among data objects beyond matching the utterance of the query, thus leading to a retrieve-all-at-once solution for complex queries. We evaluated ARM on two datasets, Bird and OTT-QA. On Bird, it outperforms standard RAG with query decomposition by up to 5.2 pt in execution accuracy and agentic RAG (ReAct) by up to 15.9 pt. On OTT-QA, it achieves up to 5.5 pt and 19.3 pt higher F1 match scores compared to these approaches.

Figures

Figures reproduced from arXiv: 2501.18539 by the authors.

Figure 1
Figure 1. A summary of our approach ARM, and a comparison with retrieval in standard RAG, which leverages [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. The average recall and perfect recall of dense [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. The average recall and perfect recall for the [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗

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Reference graph

Works this paper leans on

23 extracted references · 3 canonical work pages

  1. [1]

    Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi. 2023. Self-rag: Learning to retrieve, generate, and critique through self-reflection. arXiv preprint arXiv:2310.11511

    arXiv 2023

  2. [2]

    Jianlv Chen, Shitao Xiao, Peitian Zhang, Kun Luo, Defu Lian, and Zheng Liu. 2024 a . https://arxiv.org/abs/2402.03216 Bge m3-embedding: Multi-lingual, multi-functionality, multi-granularity text embeddings through self-knowledge distillation . Preprint, arXiv:2402.03216

    arXiv 2024

  3. [3]

    Peter Baile Chen, Fabian Wenz, Yi Zhang, Moe Kayali, Nesime Tatbul, Michael Cafarella, C a g atay Demiralp, and Michael Stonebraker. 2024 b . Beaver: An enterprise benchmark for text-to-sql. arXiv preprint arXiv:2409.02038

    arXiv 2024

  4. [4]

    Peter Baile Chen, Yi Zhang, and Dan Roth. 2024 c . Is table retrieval a solved problem? exploring join-aware multi-table retrieval. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2687--2699

    work page 2024

  5. [5]

    Wenhu Chen, Ming-Wei Chang, Eva Schlinger, William Wang, and William W Cohen. 2020. Open question answering over tables and text. arXiv preprint arXiv:2010.10439

    arXiv 2020

  6. [6]

    Palak Jain, Livio Baldini Soares, and Tom Kwiatkowski. 2024. From rag to riches: Retrieval interlaced with sequence generation. arXiv preprint arXiv:2407.00361

    work page Pith review arXiv 2024

  7. [7]

    Harsh Jhamtani, Hao Fang, Patrick Xia, Eran Levy, Jacob Andreas, and Ben Van Durme. 2023. Natural language decomposition and interpretation of complex utterances. arXiv preprint arXiv:2305.08677

    work page Pith review arXiv 2023

  8. [8]

    Tushar Khot, Harsh Trivedi, Matthew Finlayson, Yao Fu, Kyle Richardson, Peter Clark, and Ashish Sabharwal. 2022. Decomposed prompting: A modular approach for solving complex tasks. arXiv preprint arXiv:2210.02406

    arXiv 2022

Show all 23 references
  1. [9]

    Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022. Large language models are zero-shot reasoners. Advances in neural information processing systems, 35:22199--22213

    2022

  2. [10]

    Chaofan Li, Zheng Liu, Shitao Xiao, and Yingxia Shao. 2023. https://arxiv.org/abs/2312.15503 Making large language models a better foundation for dense retrieval . Preprint, arXiv:2312.15503

    2023

  3. [11]

    Jinyang Li, Binyuan Hui, Ge Qu, Jiaxi Yang, Binhua Li, Bowen Li, Bailin Wang, Bowen Qin, Ruiying Geng, Nan Huo, et al. 2024. Can llm already serve as a database interface? a big bench for large-scale database grounded text-to-sqls. Advances in Neural Information Processing Systems, 36

    2024

  4. [12]

    Xianming Li and Jing Li. 2023. Angle-optimized text embeddings. arXiv preprint arXiv:2309.12871

    2023 arXiv

  5. [13]

    Ofir Press, Muru Zhang, Sewon Min, Ludwig Schmidt, Noah A Smith, and Mike Lewis. 2022. Measuring and narrowing the compositionality gap in language models. arXiv preprint arXiv:2210.03350

    2022 arXiv

  6. [14]

    Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. 2024. Reflexion: Language agents with verbal reinforcement learning. Advances in Neural Information Processing Systems, 36

    2024

  7. [15]

    Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal. 2022. Interleaving retrieval with chain-of-thought reasoning for knowledge-intensive multi-step questions. arXiv preprint arXiv:2212.10509

    2022 arXiv

  8. [16]

    Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022. Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems, 35:24824--24837

    2022

  9. [17]

    Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W Cohen, Ruslan Salakhutdinov, and Christopher D Manning. 2018. Hotpotqa: A dataset for diverse, explainable multi-hop question answering. arXiv preprint arXiv:1809.09600

    2018 arXiv

  10. [18]

    Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, and Karthik Narasimhan. 2024. Tree of thoughts: Deliberate problem solving with large language models. Advances in Neural Information Processing Systems, 36

    2024

  11. [19]

    Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. 2022. React: Synergizing reasoning and acting in language models. arXiv preprint arXiv:2210.03629

    2022 arXiv

  12. [20]

    Weinan Zhang, Junwei Liao, Ning Li, and Kounianhua Du. 2024. Agentic information retrieval. arXiv preprint arXiv:2410.09713

    2024 arXiv

  13. [21]

    Denny Zhou, Nathanael Sch \"a rli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Claire Cui, Olivier Bousquet, Quoc Le, et al. 2022. Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625

    2022 arXiv

  14. [22]

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