Toward Structured Knowledge Reasoning: Contrastive Retrieval-Augmented Generation on Experience

Bo Hu; Enjie Liu; Jiawei Gu; Mochi Gao; Ruichao Zhong; Ye Liu; Yuanzhen Xie; Yunzhi Tan; Zang Li; Ziting Xian

REVIEW 3 major objections 4 minor 51 references

Toward Structured Knowledge Reasoning: Contrastive Retrieval-Augmented Generation on Experience

T0 review · 3 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read CoRE, a contrastive retrieval framework with MCTS-built experience memory, lifts structured knowledge reasoning by 3.44% on Text-to-SQL and 4.24% on TableQA.

desk verdict CoRE is a plausible and useful contrastive-RAG framework for structured reasoning, but the unvalidated self-generated reward labels mean the headline gains need a targeted ablation before I would trust the mechanism. read the letter →

arxiv 2506.00842 v2 pith:CQH34LHT submitted 2025-06-01 cs.CL cs.AI

Jiawei Gu , Ziting Xian , Yuanzhen Xie , Ye Liu , Enjie Liu , Ruichao Zhong , Mochi Gao , Yunzhi Tan

show 2 more authors

Bo Hu Zang Li

This is my paper

classification cs.CLcs.AI

keywords contrastiveretrieval-augmentedgenerationexperiencememoryMonteCarloTreeSearchstructuredknowledgereasoningText-to-SQLTableQAin-contextlearningtraining-free

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

Large language models answer plain-text questions well but lose accuracy on structured data such as relational databases and tables, where the model must connect a query to schemas, columns, and rows that are thinly represented in pre-training. CoRE is a training-free framework that treats structured reasoning as a planning problem: Monte Carlo Tree Search decomposes each training question into intermediate question-answer steps, each step is scored by the generator's own correctness check, and the resulting successful and failed trajectories are stored as an Experience Memory. At inference, a retriever finds similar stored cases, re-ranks them by reward, and feeds the best positive and negative cases into a contrastive prompt that tells the model to imitate successes and avoid failures. On Text-to-SQL and TableQA benchmarks, CoRE raises the accuracy of several existing systems by averages of 3.44% and 4.24%, with the largest gains of 17.2% and 8.2% on the most challenging queries. Because the framework needs no retraining and can accept new cases continuously, it addresses a known weak spot of LLMs without altering the underlying model.

What carries the argument

The load-bearing object is the Experience Memory: a set of $\langle q, a, r \rangle$ triples built by Monte Carlo Tree Search, where $q$ is a decomposed sub-question, $a$ is its answer, and $r$ is a reward label obtained by back-propagating the generator's self-evaluation (the answer to 'Is this reasoning step correct?') along successful and failed trajectories. Two mechanisms ride on it: the Retriever, which ranks stored cases by semantic similarity and then merges similarity rank with reward rank to split them into positives and negatives, and the Contrastive Thinker, which turns those examples into an in-context prompt. Together they replace a fixed few-shot demonstration set with a dynamic, contrastive one, and the reward labels are what make the positive/negative split possible.

What would settle it

Run the same CoRE pipeline on a fixed set of queries but replace the reward labels with random draws before re-ranking the retrieved cases; if accuracy does not fall to roughly the level of the baseline with random examples, the reward-based contrastive ordering is not carrying the claimed effect. A stronger version reverses the rewards so high-reward cases are shown as failures and low-reward cases as successes; the method should degrade noticeably if the reward signal is doing the work.

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Extended reading notes

Core claim

The paper's central claim is that the bottleneck in structured knowledge reasoning is the reasoning step itself, and that reasoning can be improved by retrieving experience rather than raw documents. CoRE builds a memory whose entries are decomposed question-answer pairs with reward labels, produced by MCTS rollouts that expand, simulate, and back-propagate LLM-generated sub-questions. When a new query arrives, the framework retrieves the top-k entries by semantic similarity, re-ranks them by reward so high-reward cases become positive examples and low-reward cases become negative examples, and packages both into the prompt through a Contrastive Thinker that instructs the generator to follow the successful cases and avoid the failed ones. The paper reports execution-accuracy gains of 3.5 to 5.9 points on the Bird Text-to-SQL benchmark across baselines and table-reasoning accuracy gains of 1.7 to 6.6 points on WikiTQ and FinQA, with the largest relative improvements concentrated on hard questions.

Load-bearing premise

The method assumes the generator's own yes/no self-evaluation of each reasoning step is accurate enough to give the memory entries trustworthy reward labels and to rank retrieved positives above negatives.

Editorial extensions

If this is right

Any existing Text-to-SQL or table-QA system that relies on fixed few-shot prompts can be upgraded by plugging in CoRE's retriever and memory, so the gains transfer across different base models without fine-tuning.
Because the largest gains appear on the hardest queries, CoRE is most valuable where current systems fail most often, such as multi-join SQL and multi-step table arithmetic.
The paper's ablations show that dynamically retrieved positive and negative examples outperform fixed examples, implying example selection matters more than example count, and that failed attempts help only when paired with a success to imitate.
CoRE supports multi-turn and bootstrapped prompting, so under tight token budgets the contrastive signal can be delivered across turns, and repeated CoRE applications can refine answers when latency permits.
Experience Memory is built once and reused, so the offline MCTS cost is amortized, and retrieval plus prompt construction at inference stays under about 60 ms per query.

Reading between the lines

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

Extension: the same Experience Memory could be reused across tasks that share schemas or domains, giving a new database a cold-start bank of reasoning cases without rebuilding from scratch.
Extension: because each entry carries a per-step reward, the memory is a ready-made curriculum signal that could be used to target weak steps or to create synthetic fine-tuning data for smaller models that cannot run MCTS themselves.
Extension: the contrastive re-ranking mechanism should transfer to other structured formats, such as knowledge graphs or API schemas, wherever a semantic retriever and a scalar quality signal are available.
Extension: a testable design choice is whether the self-evaluation reward can be replaced or augmented by execution feedback on SQL or by answer verification on tables, which would likely make both the memory and the re-ranking more trustworthy.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

CoRE is a genuinely useful addition to the structured-reasoning toolbox: it takes the known ingredients of contrastive ICL and retrieval-based example selection and adds an MCTS-generated experience memory with reward labels, then re-ranks retrieved examples by those labels. The reported gains—3.44 on Bird, 4.24 on WikiTQ/FinQA, with larger jumps on hard questions—are consistent across several baselines, and the paper includes an honest negative result (DAIL-SQL with self-consistency) and a thoughtful ablation showing that positive-negative contrastive prompts beat fixed or positive-only examples. That is a real contribution, and the framework is described clearly enough to reproduce.

The soft spot is the reward label. In §3.1, r2 is the generator's own 'Is this reasoning step correct?' answer, and those labels both select MCTS trajectories and drive the contrastive re-ranking in §3.2. The paper never checks whether r2 or the back-propagated Q-values correlate with actual step correctness. If the model over- or under-rates its own steps, the retrieved positives and negatives are mis-ordered, and the measured gain could be coming mostly from the similarity-based retrieval or from the sheer volume of MCTS-expanded data rather than from the contrastive signal. A randomized-label ablation on Bird dev would settle this. The stress-test note makes this point precisely, and I think it is the right concern.

Two secondary issues. First, no error bars or significance tests anywhere; the magnitude of the 3.44/4.24 averages is uncertain, especially given the variance across baselines. Second, the paper is harder to read than it should be: some tables are garbled (Table 3 and 4 have missing symbols), and there are typos. That is fixable but it hurts the paper.

The limitation section honestly acknowledges dependence on Experience Memory quality and grounding accuracy, and the offline MCTS cost is a real but amortizable investment; 'training-free' is accurate only in the narrow sense of no gradient updates.

Bottom line: the central claim holds up in the sense that the final evaluation uses gold answers, so the gain is not forced by construction, but the internal reward loop needs validation before I would fully trust the contrastive mechanism. The paper deserves a serious referee. I would send it to review, ask for the reward-label ablation, error bars, and a clean revision of the tables. If the label ablation shows the effect survives, this becomes a solid contribution; if not, the pairwise contrastive prompts may still be useful, but the story changes.

Referee Report

3 major / 4 minor

Summary. The paper introduces CoRE, a training-free contrastive retrieval-augmented generation framework for structured knowledge reasoning. CoRE uses Monte Carlo Tree Search with self-evaluation rewards to build an Experience Memory of successful and failed reasoning trajectories, then retrieves and re-ranks these cases as positive and negative exemplars to prompt an LLM. Experiments on Bird (Text-to-SQL), WikiTQ, and FinQA (TableQA) report average gains of 3.44% and 4.24% over baselines, with larger gains on more challenging questions and one explicitly reported negative result (DAIL-SQL with self-consistency).

Significance. If the reported gains hold, CoRE is a simple, plug-and-play method that improves structured knowledge reasoning without fine-tuning, and the public code release supports reproducibility. The paper honestly reports a negative result and includes a systematic ablation design across 0/1/2-shot settings. However, the central mechanism relies on unvalidated self-generated reward labels, and the absence of significance testing leaves the modest average gains indistinguishable from noise. The framework and its qualitative claims are worth considering, but additional validation is needed before the performance advantage can be accepted.

major comments (3)

[§3.1, §3.2, §4.2] The reward labels that control both Experience Memory construction and contrastive re-ranking are self-evaluations produced by the generator itself (r2 = G('Yes' | q_k, prompt) in §3.1, stored as Q-values in §4.2). These labels determine which entries are retrieved and ranked as positive or negative in §3.2, so any bias or miscalibration in self-evaluation propagates directly into the exemplar ordering. The paper reports no validation of r2 or of the resulting Q-values against actual step/answer correctness, and no experiment isolates the contribution of these rewards. Please add a calibration analysis (e.g., correlation between reward labels and success on a held-out sample) and an ablation that replaces the reward labels with random or similarity-only ranks to test whether the reported gains actually depend on the self-evaluation signal.
[Tables 1–4] All results are reported as single point estimates with no error bars, confidence intervals, or significance tests. For example, the WikiTQ/StructGPT gain is 1.7 points; without variance estimates this is not distinguishable from normal run-to-run variation. Given that the headline claims are average gains of 3.44% and 4.24%, please provide standard deviations over repeated runs (or bootstrap confidence intervals) and, where appropriate, paired significance tests across baselines.
[Tables 3 and 4, §4.4] The ablation tables are unreadable as printed: the symbols denoting fixed examples, positive cases, and negative cases are missing, and the column mapping is unclear (e.g., Table 3's '2-shot |' line shows multiple values without clear column headers). The text claims that contrastive ICL outperforms positive-positive combinations and that 'only using negative examples without pointing out the mistakes would harm performance,' but these claims cannot be verified from the current tables. Please reconstruct the tables with explicit textual column labels (e.g., 'fixed', 'positive', 'negative', 'positive+negative', 'two-round positive+negative') so each value can be mapped to a condition.

minor comments (4)

[Throughout] The model name is misspelled as 'LlaMA-3'; it should be 'Llama-3'.
[References] Lewis et al. 2020a and 2020b refer to the same paper, and Yan et al. 2024a and 2024b also refer to the same work; duplicate entries should be consolidated.
[Abstract and §4.3, Figure 4] The claimed '17.2% and 8.2%' improvements on challenging tasks reference Figure 4, but the figure is not described and the definition of 'challenging' (e.g., Bird difficulty levels or question complexity thresholds) is not stated. Please specify how these percentages are computed.
[§4.2] The statement that the Experience Memory shows 'increased diversity and domain coverage' is not supported by the presented evidence; Figure 3 is a histogram of reward values, not a diversity or coverage metric. Please provide quantitative measures such as example overlap, schema coverage, or distinct reasoning patterns.

Circularity Check

0 steps flagged · score 0.0 of 10

CoRE's headline gains are measured against external gold dev labels; self-generated reward labels affect retrieval quality but do not define the final accuracy, so no circular reduction is exhibited.

full rationale

CoRE is an empirical retrieval-augmented prompting system; its central claim is a measured end-to-end accuracy gain on held-out dev sets (Tables 1–2, §4.1–4.3) with external gold answers (EX on Bird, accuracy on WikiTQ/FinQA). The Experience Memory is built from training data via MCTS (§3.1, §4.2), and retrieval selects contrastive examples by similarity and reward labels; no parameter is fitted to the target dev labels, and no output quantity is defined to equal an input quantity by construction. The reward labels r2 = G('Yes'|q_k, prompt) are self-generated, so retrieval quality depends on an unvalidated calibration of the generator's self-judgment; this is an internal robustness concern—and the appended Limitation section explicitly acknowledges dependence on memory coverage and grounding accuracy—rather than a circular reduction, because the final evaluation is computed against gold labels. Self-citations (Gu et al. 2024a; Xie et al. 2023/2024; Liu et al. 2025) are used as background or component descriptions, not as premises that force the reported gains; no uniqueness theorem or fitted prediction is invoked. Therefore the derivation chain is self-contained against external benchmarks and no circular step is exhibitable.

Assumptions & free parameters 7 free parameters · 5 assumptions · 1 invented entities

The central claim rests on several hand-chosen hyperparameters, a self-generated reward signal, and a set of domain assumptions about MCTS diversity, retrieval similarity, and contrastive ICL. None of these is validated against external benchmarks, and the reward-label reliability in particular is load-bearing.

free parameters (7)

MCTS exploration weight w = 0.5
Chosen by hand to balance exploration and exploitation in UCT selection (Eq. 2, §3.1).
Reward exponent alpha = 0.5
Chosen by hand in fr = r1^alpha * r2^(1-alpha) (§3.1).
Candidate sub-questions d = 4
Number of candidate sub-questions generated per expansion; called K in §4.2.
Roll-outs N = 10
Number of MCTS simulations per training example (§4.2).
Depth limit L = 5
Maximum reasoning depth in the search tree (§4.2).
Sampling temperature = 0.8
Temperature for diverse trajectory generation (§4.2).
Qvalue visit threshold = 3
Minimum node visits for storing Qvalue as the reward label (§4.2).

assumptions (5)

domain assumption The self-evaluation reward r2 = G('Yes'|...) is a valid proxy for reasoning-step correctness.
Invoked in §3.1 Simulation; not validated against human judgments.
domain assumption MCTS with the simplified simulation policy generates diverse, useful reasoning trajectories.
Assumed in §3.1 and Algorithm 1; no comparison to other trajectory-generation methods.
domain assumption Semantic similarity over natural-language descriptions of SQL ASTs is a good retrieval signal.
Assumed in §3.2; no systematic comparison of retrieval variants.
domain assumption Contrastive in-context learning with one positive and one negative example improves structured reasoning.
The core mechanism of §3.3; supported only by the paper's own ablations.
domain assumption The grounding phase (schema linking, evidence selection) is accurate enough for CoRE to be applied on top.
Explicitly excluded from the formalization in §2; the paper's Limitation section admits performance depends on grounding accuracy.

invented entities (1)

Experience Memory
purpose: A stored database of decomposed reasoning steps (q, a, r) with reward labels, used for retrieval of positive and negative examples.
It is a paper-specific data artifact; its quality is asserted through Figure 3 and downstream performance, not independently verified.

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Cite this review

Pith. "Pith review of Toward Structured Knowledge Reasoning: Contrastive Retrieval-Augmented Generation on Experience." pith.science (2026). https://pith.science/paper/CQH34LHT

@misc{pith2026250600842,
  author       = {Pith},
  title        = {Pith review of: Toward Structured Knowledge Reasoning: Contrastive Retrieval-Augmented Generation on Experience},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CQH34LHT}},
  note         = {Machine review of arXiv:2506.00842}
}

read the original abstract

Large language models (LLMs) achieve strong performance on plain text tasks but underperform on structured data like tables and databases. Potential challenges arise from their underexposure during pre-training and rigid text-to-structure transfer mechanisms. Unlike humans who seamlessly apply learned patterns across data modalities, LLMs struggle to infer implicit relationships embedded in tabular formats, especially in the absence of explicit structural guidance. To bridge this cognitive gap, we introduce Contrastive Retrieval-Augmented Generation on Experience (CoRE), a framework that builds experience memory representations and enhances generalization through contrastive In-Context Learning (ICL) to simulate human-like knowledge transfer. Experiments on Text-to-SQL and TableQA show CoRE significantly improves performance, achieving average gains of 3.44% and 4.24%, with up to 17.2% on challenging tasks. Our Monte Carlo Tree Search (MCTS)-generated Experience Memory expands training data 8-9x, enhancing diversity and domain coverage. This training-free and continual method propels LLMs toward structured knowledge expertise.

Figures

Figures reproduced from arXiv: 2506.00842 by the authors.

**Figure 2.** Overall framework of CoRE. Simulation To estimate the expected future rewards (Qvalue), this phase simulates future scenarios of the current node using the world model. Starting from the current node, at each state s, a subquestion is generated according to a roll-out policy, and the world model predicts the next state. This roll-out continues until a terminal state is reached. In CoRE, to simplify the process a… view at source ↗

**Figure 3.** Reward label distribution on Bird, WikiTQ and FinQA 3.3 Contrastive Thinker The Contrastive Thinker is built upon a general LLM that is specifically prompted to generate the necessary text based on the given contrastive context. Upon receiving positive and negative cases from the Retriever, the Contrastive Thinker employs deliberate practice by treating positive cases as past successes and negative cases as past f… view at source ↗

Figures from the paper (4 more)

**Figure 4.** Figure 4: Performance under different difficulty [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]

**Figure 5.** Figure 5: Bootstrapping with CoRE by LlaMA-3-70b. tings—one iteration equals one use of CoRE and Nshot indicates the number of contrastive examples in context. Due to context length limits, structured knowledge E is included only in the first round, with subsequent iterations u…

**Figure 6.** Figure 6: Different Schema for CoRE by LlaMA-3-70b [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]

**Figure 7.** Figure 7: A trajectory of Experience Memory Builder for FinQA. [PITH_FULL_IMAGE:figures/full_fig_p015_7.png]

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Huang, Jie Fu, Xiang Yue, and Wenhu Chen

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Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

[1] [1]

online" 'onlinestring :=

ENTRY address archivePrefix author booktitle chapter edition editor eid eprint eprinttype howpublished institution journal key month note number organization pages publisher school series title type volume year doi pubmed url lastchecked label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block STRING...

[2] [2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

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arXiv 2023

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work page 2021

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work page 2021

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arXiv 2023