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REVIEW 3 major objections 5 minor 1 cited by

ReasoningFlow: Semantic Structure of Complex Reasoning Traces

T0 review · 3 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read REASONINGFLOW parses large-model reasoning traces into labeled directed acyclic graphs, so planning, verification, and backtracking become machine-detectable subgraph patterns.

desk verdict A plausible annotation schema for reasoning traces that is clearly described but lacks reproducibility evidence and experimental validation; worth reviewing but not yet convincing. read the letter →

arxiv 2506.02532 v1 pith:PPU4KRC4 submitted 2025-06-03 cs.CL

Jinu Lee , Sagnik Mukherjee , Dilek Hakkani-Tur , Julia Hockenmaier This is my paper
classification cs.CL
keywords REASONINGFLOWreasoningtraceslargemodelsdirectedacyclicgraphannotationschemasubgraphpatternmatchingchain-of-thoughtself-verification
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 tries to show that the open-ended reasoning traces produced by large reasoning models have an underlying semantic structure that can be made explicit and machine-readable. REASONINGFLOW labels each step by its role and connects steps into a directed acyclic graph, so behaviors like planning, verification, backtracking, and proof by contradiction show up as graph patterns. A reader should care because this graph-level view promises more precise evaluation of reasoning steps and a path to compressing or pruning long traces, without relying on surface keyword matching. The authors support the idea with 30 manually annotated traces from math, chemistry, and physics, and a logic-based engine that queries these graph patterns.

What carries the argument

The central object is the labeled directed acyclic graph over trace segments. It is defined by a fixed vocabulary of 9 node labels and 14 edge labels (grouped as Planning, Reasoning, Evaluation) together with two edge-drawing axioms: predecessors must supply every antecedent and symbol definition the successor depends on, and coreference-resolving (typically nearest) predecessors are preferred over earlier lexically similar ones. This graph is what carries the argument, because it reduces reasoning behavior to observable topology: each characteristic pattern (verification, backtracking, inductive generalization, proof by contradiction) is a small subgraph, so detection becomes subgraph matching rather than keyword spotting or zero-shot classification.

What would settle it

Find a single reasoning trace from a large reasoning model in which a step's essential function—say, a clause that simultaneously states a plan and delivers a conclusion, or a symbolic definition that is revised by a later node—cannot be expressed without either breaking the left-to-right DAG axiom or forcing a label choice that hides the meaning. Alternatively, run an annotation study where several annotators trained only on the schema annotate the same 30 traces; if the resulting graphs diverge substantially in node boundaries, labels, or edge sets, the schema's core claim that it captures 'the' semantic structure fails.

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Extended reading notes

Core claim

The paper's central claim is that the semantic content of an LRM reasoning trace is captured by a labeled directed acyclic graph whose nodes are contiguous text spans and whose edges connect each step to the earlier steps it uses. The scheme defines nine node roles (Context, Planning, Fact, Reasoning, Restatement, Assumption, Example, Reflection, Conclusion) and fourteen edge labels organized under Planning, Reasoning, and Evaluation. Two axioms govern edge-drawing: a node's predecessors must contain all antecedents and symbol definitions needed to understand or evaluate it, and when multiple lexically similar predecessors exist, the one that resolves coreference is chosen first, otherwise the earliest. With this graph in hand, deductive and inductive reasoning, self-verification, backtracking, and proof by contradiction become subgraph patterns that a query engine can identify mechanically.

Load-bearing premise

The load-bearing premise is that the fixed set of 9 node labels and 14 edge labels, applied with the two edge-drawing axioms, is complete and consistent enough to represent the meaningful structure of any reasoning trace, and that human annotators would apply it the same way; the paper reports 30 manually annotated traces but does not measure inter-annotator agreement.

Editorial extensions

If this is right

  • A reasoning step can be evaluated using only its connected predecessors, which is more tractable and avoids judging planning or reflection nodes as errors.
  • The DAG's ancestor relation tells which nodes are actually needed to derive the final answer, making it a basis for compressing traces for distillation.
  • Reasoning strategies such as verification, backtracking, and proof by contradiction become mechanically detectable subgraph patterns rather than keyword heuristics.
  • During inference, a partially constructed graph could flag unpromising branches and trigger early backtracking, reducing over-length traces.
  • The schema separates reasoning content from planning and metacognitive content, which could make process supervision less noisy for reward models.

Reading between the lines

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

  • Beyond the paper, the same graph could serve as a process-supervision signal: nodes that many other nodes depend on, or that are repeatedly verified, are the ones a correctness check should weight most heavily.
  • The schema's left-to-right DAG axiom is tied to autoregressive generation; non-autoregressive or iterative-editing models would require a relaxation of that axiom, so the annotation scheme may need a graph variant rather than a strict DAG.
  • Because the 30 annotated traces are math and science problems, the observed node/edge distribution is dominated by deduction; applying REASONINGFLOW to creative or multi-hop open-domain traces might reveal richer Example, Assumption, and Plan-Alternative subgraphs.
  • The subgraph query engine outlines a way to build annotated training data at scale: use detected patterns as weak labels to train classifiers that predict reasoning behavior directly from text.
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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 / 5 minor

Summary. The paper introduces ReasoningFlow, a annotation schema for representing the semantic structure of complex reasoning traces generated by large reasoning models (LRMs). The schema segments a trace into nodes with semantic labels (e.g., Planning, Fact, Reasoning, Restatement, Conclusion) and connects them with 14 fine-grained edge labels (e.g., Premise-Conclusion, Frontier-Verify, Support, Refute), forming a directed acyclic graph. The authors manually annotated 30 QwQ-32B-Preview traces from Sky-T1-data and provide two fully worked example graphs. They also present a subgraph-querying engine based on Answer Set Programming, and they argue that the resulting graph structures can support trace-validity evaluation and trace compression. The central claim is that ReasoningFlow captures meaningful, reproducible semantic structure in LRM traces and that distinct reasoning behaviors appear as identifiable subgraph patterns.

Significance. If the schema is reliable and reproducible, it would be a useful advance over existing approaches that analyze LRM traces via keyword matching or LLM-based classification, because it provides explicit relational structure between statements. The paper's strengths are its detailed annotation guidelines (Tables 1 and 2), the inclusion of two complete annotated example graphs (Figures 5 and 6), and a concrete, machine-checkable subgraph-query mechanism using ASP. However, the significance is conditional: the paper provides no evidence that another annotator would produce the same graphs, and the proposed applications (validity evaluation, efficiency) are not experimentally validated. As a schema proposal with illustrative examples, the work is suggestive, but the load-bearing claim of reproducibility is not yet established.

major comments (3)
  1. [Section 3.2] The dataset consists of only 30 traces, all annotated by the authors, with no inter-annotator agreement and no second annotator, so the central claim that ReasoningFlow graphs capture reproducible semantic structure is not supported by evidence. Provide an annotation study with at least two independent annotators on a held-out subset, reporting agreement on node segmentation, node labeling, and edge connections/labels, and discuss disagreements.
  2. [Appendix A] The two edge-drawing axioms are not sufficient to determine a unique graph: Axiom 1 is a necessary condition (predecessors must contain pronoun antecedents and symbol definitions) but does not specify how to choose among multiple predecessor sets, and Axiom 2 uses the word "typically" and gives competing criteria (same-paragraph coreference versus earliest node) that can conflict in long traces with restatements; moreover, segmentation based on "semantic atomicity" is not operationalized. Specify a deterministic procedure or, at minimum, empirically measure annotation variance on ambiguous cases.
  3. [Section 3.1 and Table 1] The text states that there are "8 node classes," but Table 1 enumerates 9 labels including Conclusion, while Figure 4 reports only 7 node categories without Conclusion; this inconsistency must be resolved because the completeness of the label inventory is part of the schema's definition.
minor comments (5)
  1. [Various] There are several typographical and formatting issues, including "tow ards" in Figure 1, irregular spacing in trace labels such as "trace2 3" in Figure 5, and "edge/31" in Appendix B where "edge/3" is presumably intended.
  2. [References] Several references abbreviate author lists with "and 1 others" (e.g., Gu et al., Kim et al., Qu et al., Sui et al., Wang et al., Wei et al.); these should be expanded to full author lists in a camera-ready version.
  3. [Figure 4] The node-label distribution in Figure 4 omits the Conclusion label that appears in Table 1; if Conclusion nodes are merged into another category, this should be stated explicitly in the caption or text.
  4. [Table 1 and Table 2] The distinction between Reflection nodes and Uncertainty edges is not crisply defined; providing an example of a Reflection node with an Uncertainty edge versus a Support/Refute edge would help annotators apply the schema consistently.
  5. [Section 3.2] The paper says the authors "manually annotated" the traces without specifying whether the annotators are also the authors of the paper; please state the annotators' background and whether they were involved in the design of the schema.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the ReasoningFlow schema is stipulated rather than derived, and the reported statistics are descriptive of the authors' annotations rather than predictions that reduce to their inputs.

full rationale

The paper's central contribution is an annotation schema, not a derivation. REASONINGFLOW's node and edge labels are stipulated definitions (Tables 1 and 2), and the directed acyclic graph structure is introduced as an explicit modeling assumption in Appendix A, not as a result derived from data or from prior work. The 'reasoning patterns' in Figures 2, 3, 5, and 6 are defined as subgraph configurations of these labels, so identifying them in the 30 manually annotated traces is descriptive corpus measurement, not a prediction that is forced by construction. The paper makes no fitted-parameter claim and reports no experimental prediction that would need to be checked for equivalence to its inputs. The self-citations (e.g., Mukherjee et al., 2025; Lee and Hockenmaier, 2025; Kim et al., 2025) are used to support background claims and design choices, and they are not invoked as external theorems that uniquely force the schema; independent citations are also present where the DAG choice is justified. The absence of inter-annotator agreement is a reliability and reproducibility concern, not a circularity concern, and the paper's own statements acknowledge the manual nature of the annotations. No circular step can be exhibited with a quote and a specific reduction, so the appropriate finding is no significant circularity.

Assumptions & free parameters 0 free parameters · 4 assumptions · 1 invented entities

The paper introduces a descriptive schema with no fitted numbers. Its validity rests on the adequacy of DAGs for reasoning traces, the exhaustiveness of the chosen label set, and the reliability of manual annotation, none of which are empirically established in the paper.

assumptions (4)
  • domain assumption The semantic structure of LRM reasoning traces is adequately captured by a left-to-right directed acyclic graph.
    Section 3.1 and Appendix A justify DAG via autoregressive generation, citing Ling et al. 2023 and Mukherjee et al. 2025; no empirical comparison of DAG vs tree or full-text representation.
  • ad hoc to paper The 9 node labels and 14 edge labels are sufficient and exhaustive for annotating arbitrary reasoning traces.
    Tables 1 and 2 define labels based on the authors' introspection; no evidence is given that other semantic roles (e.g., 'memory', 'world model', 'uncertainty of the model vs. uncertainty of the content') are covered.
  • domain assumption Annotators can reliably satisfy the two edge-drawing axioms, including the completeness criterion that predecessors include antecedents for all pronouns and symbol definitions.
    Appendix A states this axiom; manual annotation is done by the authors only, with no inter-annotator agreement reported (Section 3.2).
  • ad hoc to paper Subgraph patterns enumerated in Figure 2 (inductive reasoning, verification, backtracking, proof by contradiction) correspond to the cognitive phenomena of interest.
    The patterns are defined by the authors; no external validation or downstream task demonstrates that the detected subgraphs have psychological or computational significance.
invented entities (1)
  • ReasoningFlow schema (node labels and edge labels)
    purpose: To represent the semantic structure of LRM reasoning traces as labeled DAGs and to enable pattern queries.
    The schema is introduced by the authors and is not compared against alternative schemas or validated by downstream tasks; the two worked examples are illustrative, not evidence of criterion validity.

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Cite this review

Pith. "Pith review of ReasoningFlow: Semantic Structure of Complex Reasoning Traces." pith.science (2026). https://pith.science/paper/PPU4KRC4

@misc{pith2026250602532,
  author       = {Pith},
  title        = {Pith review of: ReasoningFlow: Semantic Structure of Complex Reasoning Traces},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PPU4KRC4}},
  note         = {Machine review of arXiv:2506.02532}
}
read the original abstract

Large reasoning models (LRMs) generate complex reasoning traces with planning, reflection, verification, and backtracking. In this work, we introduce ReasoningFlow, a unified schema for analyzing the semantic structures of these complex traces. ReasoningFlow parses traces into directed acyclic graphs, enabling the characterization of distinct reasoning patterns as subgraph structures. This human-interpretable representation offers promising applications in understanding, evaluating, and enhancing the reasoning processes of LRMs.

Figures

Figures reproduced from arXiv: 2506.02532 by the authors.

Figure 1
Figure 1. REASONINGFLOW annotates the semantic structure of a complex reasoning trace. Graph-based structure provides rich contextual information about complex cognitive processes like self-verification, as shown in the example. LRM-generated traces, possibly leading to explain￾able methods for trace validity evaluation and in￾creasing reasoning efficiency. 2 Related works Reasoning traces of LLMs and human-written argu￾menta… view at source ↗
Figure 2
Figure 2. Examples of different reasoning patterns [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Example of subgraph matching. Given a spe [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Edge and node label distributions of REASONINGFLOW. Segments are ordered counterclockwise from the top, matching the item orders in the legend. In edge labels, red, gray, and blue labels fall into Planning, Reasoning, and Evaluation categories, respectively ( [PITH_FU…
Figure 5
Figure 5. Figure 5: An example annotation of REASONINGFLOW, constructed from a physics question (ctx nodes) and the response from QwQ-32B-Preview (trace nodes). Bordered nodes (trace18, 21, 26) together form a verification subgraph, which can be identified by the subgraph matching rule vi…
Figure 6
Figure 6. Figure 6: An example annotation of REASONINGFLOW, constructed from a math question (ctx nodes) and the response from QwQ-32B-Preview (trace nodes). The inductive reasoning and proof by contradiction subgraphs can be identified according to the rule presented on the left side of …

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Forward citations

Cited by 1 Pith paper

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

  1. KisMATH: Do LLMs Have Knowledge of Implicit Structures in Mathematical Reasoning?

    cs.CL 2025-07 conditional novelty 6.0 of 10

    Across 15 open-weight LLMs, answers depend on intermediate math steps in reasoning traces, and graph-aligned reasoning chains receive higher probability than random same-length token sequences.

Reference graph

Works this paper leans on

25 extracted references · 7 canonical work pages · cited by 1 Pith paper

  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...

  3. [3]

    Kanishk Gandhi, Ayush Chakravarthy, Anikait Singh, Nathan Lile, and Noah D Goodman. 2025. Cognitive behaviors that enable self-improving reasoners, or, four habits of highly effective stars. arXiv preprint arXiv:2503.01307

    arXiv 2025

  4. [4]

    Martin Gebser, Roland Kaminski, Benjamin Kaufmann, and Torsten Schaub. 2014. Clingo= asp+ control: Preliminary report. arXiv preprint arXiv:1405.3694

    arXiv 2014

  5. [5]

    Jiawei Gu, Xuhui Jiang, Zhichao Shi, Hexiang Tan, Xuehao Zhai, Chengjin Xu, Wei Li, Yinghan Shen, Shengjie Ma, Honghao Liu, and 1 others. 2024. A survey on llm-as-a-judge. arXiv preprint arXiv:2411.15594

    arXiv 2024

  6. [6]

    Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, and 1 others. 2025. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning. arXiv preprint arXiv:2501.12948

    arXiv 2025

  7. [7]

    Seungone Kim, Ian Wu, Jinu Lee, Xiang Yue, Seongyun Lee, Mingyeong Moon, Kiril Gashteovski, Carolin Lawrence, Julia Hockenmaier, Graham Neubig, and 1 others. 2025. Scaling evaluation-time compute with reasoning models as process evaluators. arXiv preprint arXiv:2503.19877

    arXiv 2025

  8. [8]

    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

Show all 25 references
  1. [9]

    Jinu Lee and Julia Hockenmaier. 2025. Evaluating step-by-step reasoning traces: A survey. arXiv preprint arXiv:2502.12289

    2025

  2. [10]

    Hunter Lightman, Vineet Kosaraju, Yuri Burda, Harrison Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, and Karl Cobbe. 2023. Let's verify step by step. In The Twelfth International Conference on Learning Representations

    2023

  3. [11]

    Zhan Ling, Yunhao Fang, Xuanlin Li, Zhiao Huang, Mingu Lee, Roland Memisevic, and Hao Su. 2023. Deductive verification of chain-of-thought reasoning. Advances in Neural Information Processing Systems, 36:36407--36433

    2023

  4. [12]

    Boyang Liu, Viktor Schlegel, Riza Theresa Batista-Navarro, and Sophia Ananiadou. 2023. Argument mining as a multi-hop generative machine reading comprehension task. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 10846--10858

    2023

  5. [13]

    William C Mann and Sandra A Thompson. 1987. Rhetorical structure theory: A theory of text organization. University of Southern California, Information Sciences Institute Los Angeles

    1987

  6. [14]

    Sara Vera Marjanović, Arkil Patel, Vaibhav Adlakha, Milad Aghajohari, Parishad BehnamGhader, Mehar Bhatia, Aditi Khandelwal, Austin Kraft, Benno Krojer, Xing Han Lù, Nicholas Meade, Dongchan Shin, Amirhossein Kazemnejad, Gaurav Kamath, Marius Mosbach, Karolina Stańczak, and Si...

    2025

  7. [15]

    Sagnik Mukherjee, Abhinav Chinta, Takyoung Kim, Tarun Anoop Sharma, and Dilek Hakkani-T \"u r. 2025. Premise-augmented reasoning chains improve error identification in math reasoning with llms. arXiv preprint arXiv:2502.02362

    2025

  8. [16]

    NovaSky. 2025. https://novasky-ai.github.io/posts/sky-t1 Sky-t1: Train your own o1 preview model within \ 450 . Accessed: 2025-01-09

    2025

  9. [17]

    Xiaoye Qu, Yafu Li, Zhaochen Su, Weigao Sun, Jianhao Yan, Dongrui Liu, Ganqu Cui, Daizong Liu, Shuxian Liang, Junxian He, and 1 others. 2025. A survey of efficient reasoning for large reasoning models: Language, multimodality, and beyond. arXiv preprint arXiv:2503.21614

    2025

  10. [18]

    Qwen. 2024. Qwq: Reflect deeply on the boundaries of the unknown. HuggingFace

    2024

  11. [19]

    Christian Stab and Iryna Gurevych. 2017. Parsing argumentation structures in persuasive essays. Computational Linguistics, 43(3):619--659

    2017

  12. [20]

    Yang Sui, Yu-Neng Chuang, Guanchu Wang, Jiamu Zhang, Tianyi Zhang, Jiayi Yuan, Hongyi Liu, Andrew Wen, Hanjie Chen, Xia Hu, and 1 others. 2025. Stop overthinking: A survey on efficient reasoning for large language models. arXiv preprint arXiv:2503.16419

    2025 arXiv

  13. [21]

    James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal. 2018. Fever: a large-scale dataset for fact extraction and verification. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human La...

    2018

  14. [22]

    Yue Wang, Qiuzhi Liu, Jiahao Xu, Tian Liang, Xingyu Chen, Zhiwei He, Linfeng Song, Dian Yu, Juntao Li, Zhuosheng Zhang, and 1 others. 2025. Thoughts are all over the place: On the underthinking of o1-like llms. arXiv preprint arXiv:2501.18585

    2025 arXiv

  15. [23]

    Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, and 1 others. 2022. Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems, 35:24824--24837

    2022

  16. [24]

    Huihui Xu, Jarom \' r S avelka, and Kevin D Ashley. 2020. Using argument mining for legal text summarization. In Legal Knowledge and Information Systems, pages 184--193. IOS Press

    2020

  17. [25]

    Edward Yeo, Yuxuan Tong, Morry Niu, Graham Neubig, and Xiang Yue. 2025. Demystifying long chain-of-thought reasoning in llms. arXiv preprint arXiv:2502.03373

    2025 arXiv

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