Pith. sign in

REVIEW 3 major objections 4 minor 1 cited by

AgenticRAGTracer is a hop-aware benchmark that stores each multi-hop question's atomic sub-chains, so failures can be traced to the exact step where reasoning derails.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

AgenticRAGTracer is a hop-level benchmark showing that top LLMs fail on long multi-hop agentic RAG due to collapsed or over-extended reasoning chains.

T0 review reviewed 2026-08-02 challenge →

load-bearing objection Worth engaging as a resource; the hop-level annotations are new and the pipeline is transparent, but the central diagnosis leans heavily on golden traces that may be artifacts of the generator. the 3 major comments →

arxiv 2602.19127 v2 pith:EQIHOD3X submitted 2026-02-22 cs.CL

AgenticRAGTracer: A Hop-Aware Benchmark for Diagnosing Multi-Step Retrieval Reasoning in Agentic RAG

Qijie You , Wenkai Yu , Wentao Zhang This is my paper
classification cs.CL
keywords agentic RAGmulti-hop reasoninghop-aware benchmarkdiagnostic evaluationretrieval-augmented generationLLM reasoning chainsbenchmark constructiondata synthesis
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The reading

The paper introduces AgenticRAGTracer, a benchmark of 1,305 multi-hop questions in which each question is paired with the ordered chain of atomic questions and answers that define its intended reasoning path. Built mostly by an LLM pipeline with human verification, it is the paper's claim to be the first Agentic RAG benchmark that supports step-level diagnosis rather than only final-answer scoring. On evaluation, even the strongest tested model reaches only 22.6% exact match on the hardest 4-hop inference questions. The paper's central finding is that failures are largely caused by distorted reasoning trajectories—models either collapse their search early or wander into over-extension—indicating an inability to allocate steps in line with the task's logical structure. This gives researchers a diagnostic dimension that end-task accuracy alone cannot provide.

Core claim

The central discovery is that multi-hop failures in agentic RAG are not primarily retrieval failures but step-allocation failures. The paper demonstrates that on its hardest subset, 4-hop inference, the best evaluated model attains just 22.6% exact match, and across all models, correct answers have step counts tightly aligned with the golden hop count while incorrect answers have step counts significantly higher or lower. This pattern is interpreted as evidence that models cannot match their reasoning depth to the task's logical structure: they either collapse the chain prematurely or flail with redundant searches after the needed evidence is in hand. The hop-level decompositions in the data

What carries the argument

The central object is the hop-level golden decomposition: each benchmark question stores the ordered atomic QA pairs that form its intended reasoning chain, together with a topology label (Inference for sequential chains, Comparison for parallel fact-gathering). These decompositions allow evaluators to compare a model's actual trajectory against the gold chain hop by hop. The construction pipeline that produces them uses LLM-generated atomic questions, iterative chaining with strict filtering against leakage and spurious links, and full manual verification with a reported Fleiss kappa of 0.65.

Load-bearing premise

The benchmark's golden hop decompositions—the LLM-generated and human-approved chains—are assumed to be the unique correct logical routes; if they embed generator biases or miss legitimate alternative paths, the collapse-versus-over-extension diagnosis becomes partly an artifact.

What would settle it

Re-annotate a random sample of the dataset's hop decompositions with independent annotators who do not see the stored chains; substantial disagreement (well below the reported kappa of 0.65) would indicate the diagnostic labels are not stable. Alternatively, force a strong model to follow the exact golden chain step by step; if accuracy does not improve over its free-form trajectory, the step-allocation explanation is not supported.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • Multi-hop agentic RAG is substantially harder than existing end-task benchmarks suggest; even top models fall below a quarter exact match on 4-hop inference questions.
  • Hop-aware diagnosis classifies failures into two actionable modes, premature collapse and over-extension, giving agent design a concrete target beyond accuracy.
  • Retrieval breadth per step correlates with success, so strategies that retrieve too little risk losing the reasoning chain early.
  • The automated construction pipeline offers a pathway to scalable hop-annotated benchmarks that do not require fully manual curation.

Where Pith is reading between the lines

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

  • If the step-allocation diagnosis generalizes, training and prompting for agentic RAG should reward matching the task's structural depth—for example, early-stopping rewards when the evidence chain is complete—rather than only final accuracy.
  • The same hop-aware instrumentation could be transferred to other multi-step tool-use or planning tasks, where the bottleneck may likewise be trajectory shape rather than raw retrieval quality.
  • A testable extension of the paper's correlational evidence would be to use the golden hop count as a calibration target, measuring whether models that under- or over-shoot the count are the same ones that fail.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 4 minor

Summary. AgenticRAGTracer introduces a benchmark of 1,305 multi-hop Agentic RAG questions constructed largely by an LLM pipeline (GPT-4o-mini) with three-stage filtering and full human review, and annotated with hop-level golden decomposition traces. The paper evaluates 13 models under a unified Qwen-Agent/ReAct setup and reports strong accuracy degradation with hop count (e.g., GPT-5 at 22.6% EM on 4-hop inference). The central diagnostic claim is that failures are primarily driven by distorted reasoning chains—premature collapse or over-extension—rather than isolated tool errors. The paper also argues that existing benchmarks lack intermediate hop-level questions, knowledge bases, and indices, and positions AgenticRAGTracer as the first hop-aware Agentic RAG benchmark.

Significance. If the benchmark's golden traces are valid and canonical, this is a genuinely useful diagnostic resource: it is transparently constructed, uses a shared corpus/index (FlashRAG), removes overlap with existing benchmarks, and ships detailed prompts, which supports reproducibility. The evaluation is broad (13 models) and the step-level analysis addresses a real gap in Agentic RAG evaluation. The paper's main contributions—an automated construction pipeline with human verification and a hop-aware diagnostic evaluation—are valuable. However, the headline diagnostic conclusion (collapse versus over-extension as the dominant failure mode) depends on treating the released hop decompositions as the only valid logical routes, and this assumption is not independently validated. The absence of a human accuracy baseline also leaves the 'poor performance' claim uncalibrated. These are fixable within the paper's scope, but they are load-bearing for the paper's main claims.

major comments (3)
  1. [§3.3–3.4, Table 2, §4.3] The collapse/over-extension diagnosis treats the golden decompositions as canonical, not merely valid. The human review in §3.4 was a binary Retain/Discard decision with Fleiss κ=0.65, resolved by consensus with the authors; it did not check whether alternative valid decompositions exist. Consequently, Table 2's comparison of Steps-C/Steps-I against the nominal hop number conflates 'deviates from the released trace' with 'reasoning is distorted.' A model that merges two gold hops into one well-formed retrieval or splits one hop into two searches is counted as collapsed/over-extended even if its logic is sound. Please provide an external validity check: e.g., independent annotation of whether each gold trace is the unique valid decomposition, or an oracle-retrieval human/symbolic baseline that reports step counts under the same protocol.
  2. [§4.1–4.2] No human accuracy baseline is reported. The claim that 'even the best LLMs perform poorly' (e.g., GPT-5 at 22.6% EM on 4-hop inference) is impossible to calibrate without knowing how well a competent human, or a human with the same retrieval tool, performs on the same questions. A sample of human EM/F1 on each difficulty subset—especially 4-hop Inference—would make the benchmark's difficulty claim meaningful and would also help validate the golden traces. This is a central omission for a benchmark paper.
  3. [Table 2, §4.4] The step-count evidence is too coarse to support the proposed failure taxonomy. Table 2 reports only aggregate average numbers of tool calls for correct and incorrect answers; this conflates plan length, execution dead-ends, repeated retrievals, and actual subgoal decomposition. The qualitative error analysis in §4.4 is based on a single trace (Figure 4). To make 'collapse versus over-extension' a robust diagnostic claim, the authors should report trajectory-level metrics that compare executed subgoals against gold-hop boundaries (e.g., normalized edit distance to the gold plan, or per-hop oracle scores), or provide a coded sample of failure categories with inter-annotator agreement.
minor comments (4)
  1. [§4.4] The text says 'cascading errors illustrated in Figure 3' but Figure 3 shows average top-k values; the error case is Figure 4.
  2. [Figure 2] The 'Data Statistics' figure appears garbled in the manuscript (long path-like strings), and the domain/topology labels are not readable from the text. Please ensure the figure renders clearly in the final version.
  3. [Appendix C] Several prompt excerpts contain typos and OCR-style artifacts ('promblem', 'Docl', 'Answerl1', 'MWN'), and Table 4 has 'Cheif' and an inconsistent dataset name 'Musique' for MuSiQue. These should be cleaned before publication.
  4. [§4.1] The LLM-judge validation uses only 20 instances per subset and cross-checks against GPT-5 and Grok-4, which share an ecosystem with the generator GPT-4o-mini. This is acceptable as a sanity check, but it would be helpful to state the agreement numbers explicitly and to note this as a limitation.

Circularity Check

0 steps flagged

No significant circularity: the benchmark's diagnostic claims are empirical observations, not quantities forced by construction or by self-citation.

full rationale

AgenticRAGTracer is a benchmark-construction and evaluation paper. It does not fit parameters to data and then rename fitted values as predictions. The central diagnostic claim—that failures are dominated by collapsed or over-extended reasoning chains—is an empirical reading of Table 2 (step counts on correct vs. incorrect responses) and the error trace in Figure 4, not a quantity equal by construction to the benchmark's inputs. The golden hop decompositions are generated by GPT-4o-mini and then human-reviewed; using those labels to define hop counts is a data-quality assumption, not a circular derivation. The paper also reports an external anchor: 'we cross-checked the judge’s verdicts on a random sample (20 instances per subset) against stronger models—specifically GPT-5 and Grok-4, as well as human annotations... The results demonstrate near-perfect agreement.' No load-bearing uniqueness theorem or self-citation chain is invoked; the many citations of the authors' own prior work appear in related-work and data-synthesis context and do not justify the benchmark's conclusions. Accordingly, no circular step meeting the quoted-reduction standard was found. The skeptical concern about whether the LLM-generated golden decompositions are the only valid routes is a correctness/validity risk, not a circularity in the paper's derivation chain.

Axiom & Free-Parameter Ledger

0 free parameters · 4 axioms · 1 invented entities

No numerical parameters are fitted to data: the paper reports measured EM/F1/LLM-judge scores, and procedural choices such as GPT-4o-mini as generator or top-k settings are not fitted constants. The load-bearing assumptions are about LLM and human reliability in producing correct hop-level ground truth.

axioms (4)
  • domain assumption GPT-4o-mini-generated atomic QA pairs and multi-hop merges are faithful enough to serve as ground truth after filtering.
    The whole pipeline in Sections 3.2–3.3 uses LLM generation and LLM verification; no independent formal or human re-derivation of every chain is shown, only a Retain/Discard human review with kappa 0.65.
  • domain assumption The hop-level golden decompositions are complete and unambiguous, so a model failing to follow them is genuinely wrong.
    The diagnostic conclusions in Sections 4.3–4.4 compare model trajectories against these LLM/human-authored traces; if a trace is not the only valid route, the collapse/over-extension verdict can be an artifact.
  • domain assumption Title-based overlap filtering against HotpotQA/2Wiki/MuSiQue plus Wikipedia SPARQL disambiguation fully prevents benchmark leakage.
    Sections 3.1 and 3.4. Title matching cannot exclude entity-level or long-tail content overlap, so the 'no overlap' claim is weaker than stated.
  • domain assumption GPT-4o-mini LLM-as-a-judge scores are reliable despite being in the same model family as the generator.
    Section 4.1. The cross-check is small (20 random samples per subset) and uses GPT-5 and Grok-4 plus humans; it does not remove the underlying LLM-judge circularity.
invented entities (1)
  • Hop-level golden trace annotations no independent evidence
    purpose: Store the intermediate atomic QAs that connect single-hop facts to the final multi-hop question, enabling step-by-step diagnosis.
    These annotations are generated by GPT-4o-mini and accepted through LLM filters plus human review; they are observable only through the released dataset and carry no external falsifiable prediction of their own.

reviewed 2026-08-02 · how reviews work

0 comments
Cite this review

Pith. "Pith review of AgenticRAGTracer: A Hop-Aware Benchmark for Diagnosing Multi-Step Retrieval Reasoning in Agentic RAG." pith.science (2026). https://pith.science/paper/EQIHOD3X

@misc{pith2026260219127,
  author       = {Pith},
  title        = {Pith review of: AgenticRAGTracer: A Hop-Aware Benchmark for Diagnosing Multi-Step Retrieval Reasoning in Agentic RAG},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/EQIHOD3X}},
  note         = {Machine review of arXiv:2602.19127}
}
Share X Bluesky LinkedIn Reddit HN
read the original abstract

With the rapid advancement of agent-based methods in recent years, Agentic RAG has undoubtedly become an important research direction. Multi-hop reasoning, which requires models to engage in deliberate thinking and multi-step interaction, serves as a critical testbed for assessing such capabilities. However, existing benchmarks typically provide only final questions and answers, while lacking the intermediate hop-level questions that gradually connect atomic questions to the final multi-hop query. This limitation prevents researchers from analyzing at which step an agent fails and restricts more fine-grained evaluation of model capabilities. Moreover, most current benchmarks are manually constructed, which is both time-consuming and labor-intensive, while also limiting scalability and generalization. To address these challenges, we introduce AgenticRAGTracer, the first Agentic RAG benchmark that is primarily constructed automatically by large language models and designed to support step-by-step validation. Our benchmark spans multiple domains, contains 1,305 data points, and has no overlap with existing mainstream benchmarks. Extensive experiments demonstrate that even the best large language models perform poorly on our dataset. For instance, GPT-5 attains merely 22.6\% EM accuracy on the hardest portion of our dataset. Hop-aware diagnosis reveals that failures are primarily driven by distorted reasoning chains -- either collapsing prematurely or wandering into over-extension. This highlights a critical inability to allocate steps consistent with the task's logical structure, providing a diagnostic dimension missing in traditional evaluations. We believe our work will facilitate research in Agentic RAG and inspire further meaningful progress in this area. Our code and data are available at https://github.com/YqjMartin/AgenticRAGTracer.

Figures

Figures reproduced from arXiv: 2602.19127 by Qijie You, Wenkai Yu, Wentao Zhang.

Figure 1
Figure 1. Figure 1: Data construction workflow. connections between hops may appear plausible to humans, the provided supporting documents often fail to supply explicit links. in such cases, the model is forced to implicitly rely on human prior knowledge rather than actual retrievable evidence, violating the fundamental premise of RAG systems. Neglect of Intermediate Reasoning. Most benchmarks provide only the final supportin… view at source ↗
Figure 2
Figure 2. Figure 2: Data Statistics 3.2 Atomic Question Generation We synthesized atomic QA pairs from the filtered Wikipedia documents using an LLM, followed by a rigorous quality assurance pipeline. The prompts used for data construction are provided in Ap￾pendix C. First, we applied heuristic filtering to remove malformed responses; crucially, we ex￾cluded answers consisting solely of numbers to preserve semantic context, … view at source ↗
Figure 3
Figure 3. Figure 3: Average top-k values. Closed-source models such as GPT-5, o4-mini, and Grok-4 generally outperform most open-source counterparts, demonstrating their stronger inte￾grated reasoning and retrieval coordination abil￾ities. However, an exception is GPT-4o, which performs unexpectedly poorly on our benchmark. To investigate the underlying causes of this perfor￾mance discrepancy, we conducted a quantitative anal… view at source ↗
Figure 4
Figure 4. Figure 4: Error case study. We present the key information in the figure; additional details can be found in [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Gen atomic qa prompt [PITH_FULL_IMAGE:figures/full_fig_p015_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Merge qa prompt(part1) [PITH_FULL_IMAGE:figures/full_fig_p016_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Merge qa prompt(part2) [PITH_FULL_IMAGE:figures/full_fig_p017_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Merge qa prompt(part3) [PITH_FULL_IMAGE:figures/full_fig_p018_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Merge qa prompt(part4) [PITH_FULL_IMAGE:figures/full_fig_p019_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Merge qa prompt(part5) [PITH_FULL_IMAGE:figures/full_fig_p020_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Inference check prompt(part1) [PITH_FULL_IMAGE:figures/full_fig_p021_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Inference check prompt(part2) [PITH_FULL_IMAGE:figures/full_fig_p022_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Inference check prompt(part3) [PITH_FULL_IMAGE:figures/full_fig_p023_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Comparison check prompt(part1) [PITH_FULL_IMAGE:figures/full_fig_p024_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Comparison check prompt(part2) [PITH_FULL_IMAGE:figures/full_fig_p025_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Refine prompt [PITH_FULL_IMAGE:figures/full_fig_p026_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: More optional answer prompt Please solve the following problem and return result. Ensure responses are as concise as possible, focusing only on key information while omitting redundant details. The problem is: {problem} [PITH_FULL_IMAGE:figures/full_fig_p027_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Reasoning prompt [PITH_FULL_IMAGE:figures/full_fig_p027_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: Reasoning prompt comparison [PITH_FULL_IMAGE:figures/full_fig_p027_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: Singlehop prompt [PITH_FULL_IMAGE:figures/full_fig_p028_20.png] view at source ↗
Figure 21
Figure 21. Figure 21: Multi-hop inference prompt You are an expert at solving problems. Now you need to solve a multi-hop comparison problem. Multi-hop comparison promblem: a question that requires retrieving and comparing information from multiple sources to determinea relative fact. ## For Example: Input: Question1: "When was John Beach born?" Answerl: "January 1, 1812" Supporting Document1: "Major John Beach( January 1, 181… view at source ↗
Figure 22
Figure 22. Figure 22: Multi-hop comparison prompt [PITH_FULL_IMAGE:figures/full_fig_p029_22.png] view at source ↗
Figure 23
Figure 23. Figure 23: LLM judge prompt [PITH_FULL_IMAGE:figures/full_fig_p030_23.png] view at source ↗
Figure 24
Figure 24. Figure 24: Thinking prompt Based on the plan and the search results before (if there is), first analyse what information you have gained and what other information you still need, then Execute ONLY ONE MORE step using the RAG search tool. You should never assume or invent any search results that are not explicitly provided in the context. If there is no search results before, just write 'Currently, no results are av… view at source ↗
Figure 25
Figure 25. Figure 25: Action prompt [PITH_FULL_IMAGE:figures/full_fig_p031_25.png] view at source ↗

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Scaling Multi-Hop Training Data via Graph-Constrained Path Selection

    cs.CL 2026-05 conditional novelty 7.0

    Graph-constrained path selection on contextual keyword centroids decouples path enumeration from teacher verbalization to scale multi-hop QA data from plain text, enabling 4.4x corpus expansion and raising closed-book...

Reference graph

Works this paper leans on

23 extracted references · 2 linked inside Pith · cited by 1 Pith paper

  1. [1]

    Search “Elaine Duke Secretary of Homeland Security start date

  2. [2]

    Search “White House Chief of Staff assumed office on [date from step 1]

  3. [3]

    function_call

    Search “[Name of Chief of Staff from step 2] born 1950 User: Based on the plan and the search results before (if there is), first analyse what information you have gained and what other information you still need, then EXECUTE ONLY ONE MORE step using the RAG search tool. ... Assistant: "function_call": {"name": "RAG_search", "arguments": "{query: Elaine ...

    1950

  4. [4]

    Focus on measurable and database-friendly details

    Relevance and Precision - Avoid abstract questions. Focus on measurable and database-friendly details

  5. [5]

    What awards did Author X receive?

    Answer Uniqueness - The question must be **specific enough** to yield a **unique answer** from the document. - % Avoid under-specified questions that allow **multiple correct answers**. - % "What awards did Author X receive?" (if multiple awards are listed in the document) - v "What award did Author X receive in 2022?" (if only one is given for that year)...

    2022

  6. [6]

    question

    For answers that consist of multiple parts or are comparative in nature, multiple core components and comparative statements should be included. ## Output Format (JSON): For each input QA pair, output the following JSON object: {{ "question": "<original question>", “original_answer": "<original answer>", "refined_answer": "<clean, concise, and direct answ...

    1931

  7. [7]

    A increased and B decreased

    Atomicity - Each QA must be based ona single indivisible fact (no conjunctions). % "A increased and B decreased" ~ must split into two questions

  8. [8]

    Performance has improved

    Verifiability - The answer must include at least one of: - ¥v Numeric value (e.g., 59.0%) - v Time or date (e.g., 2025/04/28) - v Unique name/entity (e.g., Humpback65B) - % Reject vague expressions: "Performance has improved"

    2025

  9. [9]

    Global GDP grew by 3.0% in 2023

    Time specificity - Explicitly mark time ranges when containing time-sensitive information - Examples: ¥ "Global GDP grew by 3.0% in 2023" % "Recent GDP growth of 3.0%"

    2023

  10. [13]

    Do not include broader categories, narrower sub-types, or related but distinct concepts

    **Equivalence:** Each alternative expression must refer to *exactly the same entity or information* as the original input. Do not include broader categories, narrower sub-types, or related but distinct concepts

  11. [14]

    * Common **abbreviations, acronyms, or initialisms**

    **Scope of Variation:** Focus on: * Different **formatting conventions** (e.g., dates, numbers, units). * Common **abbreviations, acronyms, or initialisms**. * Well-known **aliases, nicknames, or shorter forms** in common usage. * Synonyms or rephrasing should *only* be included if they are direct, commonly accepted equivalents

  12. [15]

    Avoid creating variations that are only valid in very niche or obscure contexts

    **Context-Agnosticism:** Unless the input itself implies a specific context, generate general-purpose variations. Avoid creating variations that are only valid in very niche or obscure contexts

  13. [16]

    **Inclusion of Original:** Always include the original input as the first item in the generated list

  14. [17]

    1977-01-26

    **Format:** Output the variations as a JSON list of strings. **Examples:** Input: 1977-01-26 Output: ["1977-01-26", "1977 01 26", "1977.01.26", "January 26, 1977", "26 Jan 1977", "Jan 26, 1977"] Input: United Nations Output: ["United Nations", "U.N.", "UN"] Input: 3.14159 Output: ["3.14159", "mn", "pi", "PI"] Input: Doctor of Philosophy Output: ["Doctor o...

    1977

  15. [19]

    2009 UCLA Bruins 27-yard field goal early in the final period sixth career game-winner

    Search RAG for “2009 UCLA Bruins 27-yard field goal early in the final period sixth career game-winner” to identify the player and the specific game

    2009

  16. [20]

    [Player Name] first NFL game opponent

    Search RAG for “[Player Name] first NFL game opponent” to determine which professional football team he first faced

  17. [21]

    [Opponent Team Name] home city

    Search RAG for “[Opponent Team Name] home city” to find the city where that professional team is based

  18. [22]

    [City Name] metropolitan area

    Search RAG for “[City Name] metropolitan area” to get the official metropolitan area designation

  19. [23]

    [Metropolitan Area Name] forms the core of

    Search RAG for “[Metropolitan Area Name] forms the core of” to identify the broader trading area that metro area forms the core of. User: Based on the plan and the search results before (if there is), first analyse what information you have gained and what other information you still need, then EXECUTE ONLY ONE MORE step using the RAG search tool. ... Ass...

    2009

  20. [2002]

    hack/Legend of the Twilight

    The series is currently being serialized in Japan and has been collected into twenty-five tankobon volumes. In North America, Digital Manga Publishing has released only three volumes, with the last graphic novel released in November 2004. The series is currently on hiatus but Digital Manga Question: Which of the following was critiqued as one of the worst...

    2004

  21. [2024]

    a number of sermons on the Epistle of the Romans

    Morehopqa: More than multi-hop reasoning. Preprint, arXiv:2406.13397. Chengyu Shen, Zhen Hao Wong, Runming He, Hao Liang, Meiyi Qiang, Zimo Meng, Zhengyang Zhao, Bohan Zeng, Zhengzhou Zhu, Bin Cui, et al. 2025. Let’s verify math questions step by step.arXiv preprint arXiv:2505.13903. Aditi Singh, Abul Ehtesham, Saket Kumar, and Tala Ta- laei Khoei. 2025. ...

    Pith/arXiv arXiv 2025

  22. [2025]

    Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jin- liu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, Meng Wang, and Haofen Wang

    Data-centric perspectives on agentic retrieval- augmented generation: A survey.Authorea Preprints. Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jin- liu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, Meng Wang, and Haofen Wang. 2024a. Retrieval-augmented genera- tion for large language models: A survey.Preprint, arXiv:2312.10997. Yunfan Gao, Yun Xiong, Meng Wang, ...

    Pith/arXiv arXiv 2025

  23. [9474]

    Hao Liang, Xiaochen Ma, Zhou Liu, Zhen Hao Wong, Zhengyang Zhao, Zimo Meng, Runming He, Chengyu Shen, Qifeng Cai, Zhaoyang Han, et al

    Curran Associates, Inc. Hao Liang, Xiaochen Ma, Zhou Liu, Zhen Hao Wong, Zhengyang Zhao, Zimo Meng, Runming He, Chengyu Shen, Qifeng Cai, Zhaoyang Han, et al. 2025a. Dataflow: An llm-driven framework for unified data preparation and workflow automa- tion in the era of data-centric ai.arXiv preprint arXiv:2512.16676. Hao Liang, Linzhuang Sun, Jingxuan Wei,...

    arXiv 2024

This paper was first reviewed by deepseek-v4-flash on August 2, 2026.