Modeling Hierarchical Thinking in Large Reasoning Models

Ali Nazari; Erfan Shayegani; G M Shahariar; Nael Abu-Ghazaleh

REVIEW 5 major objections 4 minor 15 references

Modeling Hierarchical Thinking in Large Reasoning Models

T0 review · 5 major / 4 minor · reviewed 2026-08-04 · deepseek-v4-flash

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Pith's one-line read A chain-of-thought can be compressed into transitions among six reasoning states, and the topology of those transitions separates successful reasoning chains from failed ones.

desk verdict The body is a careful descriptive CoT-annotation study, but the arXiv abstract promises a Q-value steering method and four-benchmark results that simply do not exist in the manuscript — that mismatch sinks the paper as submitted. read the letter →

arxiv 2510.22437 v2 pith:S4THXWCO submitted 2025-10-25 cs.AI cs.CL

G M Shahariar , Erfan Shayegani , Ali Nazari , Nael Abu-Ghazaleh This is my paper

classification cs.AIcs.CL

keywords finitestatemachinechain-of-thoughtreasoningstatesinterpretabilitylargemodelstransitiontopologyQ-valuesteeringcontrol

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 free-form text of a reasoning model's chain-of-thought can be read as a trajectory through six discrete cognitive states: initialization, deduction, augmentation, uncertainty estimation, backtracking, and final conclusion. If that representation is faithful, reasoning quality stops being a mystery: the pattern of transitions—how a model leaves uncertainty to do more deduction rather than rushing to a conclusion, how it backtracks—can identify effective chains and can be steered. The authors argue that on mathematical problems, strong models show dense deductive progressions with corrective feedback and occasional backtracking, while weaker models finish early; on factual knowledge, they argue, adaptive uncertainty-aware exploration matters more than long derivations. They support this with auto-labeled reasoning traces and with a training-free steering method that improves accuracy on four benchmarks using a fraction of the interventions of token-level baselines.

What carries the argument

The central object is a memoryless Finite State Machine (FSM) whose states are six hand-defined reasoning roles—init, deduce, augment, uncertain, backtrack, closure—and whose transitions are estimated from annotated chain-of-thought spans. The work it does is to compress a free-form reasoning trace into a discrete path, so that dataset-level transition probability matrices and FSM lengths can be compared across models and tasks; the topology, not the text, is then used to identify effective reasoning and to derive Q-values for inference-time steering.

What would settle it

Manually annotate a statistically meaningful sample of CoT traces, or use a second independent labeler, and recompute the transition-topology signatures the paper claims separate strong from weak models. If the signatures—uncertain-state exits into deduction and augmentation, meaningful backtracking, rare immediate uncertain-to-closure transitions—do not reproduce with the independent labels, the FSM analysis is an artifact of the annotation tool. Additionally, rerun Q-Value steering with labels from the independent annotator; if the accuracy gains vanish, the control result depends on the spe

Watch

Extended reading notes

Core claim

The paper's central claim is that large reasoning models, though generating free-form text, move through a small set of discrete functional states, and the sequence of transitions among these states—the topology of a memoryless finite state machine—captures the difference between effective and ineffective reasoning. The six states are initialization (restating or framing the task), deduction (step-by-step inference and calculation), augmentation (recalling facts, planning, testing examples, branching, refining), uncertainty estimation (explicit doubt or confidence checking), backtracking (returning to earlier steps), and final conclusion (closure). The authors claim that dataset-level transi

Load-bearing premise

The whole framework rests on the assumption that the automatic labeler's assignment of each reasoning sentence to one of six fixed states is a correct description of what the model is doing; if the labeler systematically mislabels spans, the transition matrices and steering benefits are artifacts of the labeling tool, not of the models.

Editorial extensions

If this is right

Accuracy can be predicted or flagged from the transition pattern of a chain without solving the underlying problem.
Reasoning can be steered by intervening only at reasoning-state boundaries, so guiding high-level cognitive dynamics is cheaper than token-level control.
The same six-state space appears across models and granularities, providing a common language for comparing reasoning styles and detecting pathological loops.
Overthinking can be mitigated by identifying and pruning redundant state sequences that do not preserve information.

Reading between the lines

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

If this abstraction is faithful, the six-state vocabulary could serve as a reusable 'reasoning grammar' for transferring steering or training policies between tasks and model families—something the paper gestures at but does not demonstrate.
The steering result suggests that activations at state boundaries carry enough signal to bias the next state; a direct test is to ablate exactly which sentence gets steered and measure where the effect originates.
A natural next step is to use the FSM topology itself as a reward signal: sample multiple trajectories, keep those whose transition patterns match a 'healthy' profile, and see whether best-of-n accuracy improves.
Since the paper's own limitations note that FSM metrics describe process rather than correctness, a clean test is whether the strong/weak transition signatures persist after controlling for problem difficulty.

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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

Two things you should know. First, the body of this paper is a straightforward descriptive study: annotate CoT traces with six hand-defined states, produce transition probability matrices, and compare three reasoning models on AIME and GPQA. That part is honestly written, includes the annotation prompts, and the limitations section admits the real weaknesses. Second, the arXiv abstract advertises a Q-value guided steering method with results on four benchmarks and a 25x efficiency gain. That method does not exist in the manuscript. There are no Q-value equations, no steering experiments, no MATH-500 or GSM8k results; Section 5 lists controllability only as a possible future application. The abstract also claims the states "can be captured in the latent state," which Section 2.1 explicitly disavows—the framework models observed text, not internal mechanisms. This is not a minor mismatch; it makes the central claim unsupported.

What is actually new: the specific sentence- and paragraph-level transition statistics for Qwen3-4B, Phi-4-reasoning, and gpt-oss-20b on AIME25 and the first 50 GPQA questions. These measurements are not in the prior CoT-annotation literature, and the dual-granularity comparison is a nice touch. The observation that longer FSM length tracks accuracy on AIME but not on GPQA is a real, if underanalyzed, finding, and a Cohen's kappa of 0.89 on a 10% manual review is reasonable.

Soft spots, in proportion: the datasets are small (30 and 50 questions) with no confidence intervals, so the ranking claims are shaky. More important, the annotation prompt forces "init" and "closure" to appear at most once, labels mid-chain answers as "deduce," and imposes termination, so parts of the observed topology are by construction, not emergent. The "good reasoning uses uncertainty and backtracking" narrative is derived from the same annotated traces used to describe the models; there is no held-out predictive test.

The descriptive study could be salvageable if the abstract were rewritten to match the body and the data were expanded, but as submitted the mismatch is a desk-reject-level flaw. The body shows clear thinking—the authors know what they did and said where the limits are—but the paper's advertised contribution is absent. My verdict: reject. I would not send this to peer review as-is; I would return it for major revision first, or desk-reject and invite a corrected resubmission.

Referee Report

5 major / 4 minor

Summary. The paper proposes a Finite State Machine (FSM) abstraction for Chain-of-Thought reasoning in large reasoning models. Six hand-defined states (init, deduce, augment, uncertain, backtrack, closure) are assigned to CoT segments via GPT-4o-mini annotations, and the authors compute state frequencies, transition probabilities, and FSM length over AIME 25 (30 problems) and the first 50 GPQA Diamond questions, using Qwen3-4B-Thinking, Phi-4-reasoning, and gpt-oss-20b at two reasoning efforts. The body reports descriptive findings about longer traces, uncertainty/backtracking patterns, and task-dependent reasoning styles. The abstract, however, also advertises a Q-Value guided steering method with results on four benchmarks and 25x fewer interventions; no such method or experiments appear in the body.

Significance. If the descriptive claims were convincingly established, the FSM view could be a useful interpretability lens, but its novelty is incremental relative to existing cognitive-labeling and reasoning-graph work. The paper ships reproducible assets — annotation prompts, a code repository, fixed seeds, and a reported inter-annotator kappa of 0.89 — which are strengths. However, the abstract's headline contribution, Q-Value guided steering with surgical efficiency, is entirely absent from the manuscript body, and the descriptive analysis rests on a small, hand-constrained annotation pipeline. As submitted, the paper supports at most a narrow descriptive claim about labeled CoT text, not the advertised control contribution or a latent-state interpretation.

major comments (5)

[Abstract vs. Sections 1–7] The abstract claims a training-free Q-Value guided steering method that achieves large gains with 25x fewer interventions across AIME25, MATH-500, GSM8k, and GPQA Diamond. The body contains no Q-value equations, no steering-vector experiments, no results on MATH-500 or GSM8k, and no efficiency comparison. Section 5 lists controllability only as a future application. This headline contribution is unsupported in the submitted manuscript and cannot be evaluated. The central claim of the paper as stated is therefore not substantiated.
[§2.1 vs. Abstract] The abstract states that the six FSM states and transitions 'can be captured in the latent state of the model.' Section 2.1 explicitly says the framework models behavioral output text, not internal neural mechanisms, and no latent probing is performed anywhere in the paper. This is a direct contradiction between the advertised claim and the actual scope of the work.
[§2.5 and Appendix A] The observed FSM topology is partly enforced by the annotation rules, not emergent. The annotation prompt in Appendix A requires 'init' and 'closure' to appear at most once, instructs the annotator to label mid-chain final-answer sentences as 'deduce,' and Section 2.5 states that termination and invalid transitions are explicitly enforced during annotation. Consequently, the transition matrices and graph topology reflect annotation constraints as well as model behavior. Claims that the topology 'emerges' or that transition patterns reveal model reasoning strategies must separate model-driven transitions from annotator-imposed structure; the paper does not do so.
[§3 and Table 2] The empirical basis is thin for the strength of the conclusions. The analysis uses 30 AIME problems, 50 GPQA questions, three models, and four model configurations, with no confidence intervals, significance tests, or error bars. Several headline distinctions are based on small percentage differences — e.g., 68% vs. 72% GPQA accuracy for Qwen vs. Phi with n=50, and backtracking frequencies of 7% or less. These differences are within plausible sampling noise, so the paper's load-bearing comparisons between 'strong' and 'weak' models are not statistically supported.
[§3, §4, and Limitations] The framework is validated only by a 10% manual review with Cohen's kappa 0.89, which measures labeler agreement on the authors' own taxonomy, not construct validity. The six-state taxonomy is authored by the researchers, and the narrative that effective reasoning uses uncertainty and backtracking is derived from the same annotated traces used to describe the models. There is no held-out predictive test, no alternative-taxonomy comparison, and no evidence that the state assignments capture anything beyond surface textual cues. This circularity undermines the claim that the FSM abstraction identifies 'effective reasoning chains from those that fail.'

minor comments (4)

[§1 and §6] Minor typos and phrasing issues: 'hierarhical' in §6 should be 'hierarchical'; the paper should also clarify whether 'FSM Length (S)' and '(P)' in Table 2 refer to sentence and paragraph granularity in the table caption, not only in the body.
[Figure 1] The state frequencies in Figure 1 appear to sum to well over 100% for several models, presumably because the mean per-state frequency is computed across occurrences rather than as a distribution over states. Please state the normalization explicitly in the caption or text to avoid reader confusion.
[§4(c)–(d)] The narrative in Section 4 cites many specific transition percentages (e.g., '56% from init to augmentation,' '1.6% from deduction to closure') but does not indicate which heatmap in Figure 2 these numbers correspond to or how they were read off the displayed matrices. Adding a pointer or a small table of the exact numeric matrices would improve verifiability.
[§3 Annotation] The annotation prompt instructs 'Do NOT merge sentences,' but the experimental setup later merges consecutive identical self-loops. This is not a contradiction, but it would help to note that self-loop merging is a post-annotation step independent of the annotator's segmentation.

Circularity Check

2 steps flagged · score 5.0 of 10

FSM topology is partly imposed by the annotation prompt rather than discovered; abstract's Q-Value steering and latent-state claims are absent from the body.

self definitional [Section 2.5 (Transition Constraints); Appendix A.1/A.2 annotation prompts]
"Most of these constraints naturally emerge from the model's reasoning behavior. However, termination and invalid transitions are explicitly enforced during annotation. ... 'init' and 'closure' should appear *at most once*. ... If a sentence in the *middle of a chain* expresses a final answer or meta-statement, label it as 'deduce' instead."

The paper presents the FSM topology (closure as terminal sink, init only at the start) as an emergent pattern, and Section 4(e) concludes that all graphs share a 'consistent core structure' centered on the six states. But the annotation protocol forces exactly these properties: at most one init, at most one closure, and mid-chain answer sentences relabeled as deduce. Thus the observed topology is built into the labeling instructions, not independently measured from the CoT text. Findings about 'premature closure' and 'early finish' therefore inherit annotator-imposed constraints rather than describing raw model behavior.
self definitional [Section 4(e); Section 2.3 and Appendix A label definitions]
"we observe that all transition graphs exhibit a consistent core structure that aligns closely with the underlying FSM framework: each graph is centered around the six reasoning states."

This 'discovery' is guaranteed by the annotation setup: every sentence/segment must be assigned exactly one of the six predefined codes (init, deduce, augment, uncertain, backtrack, closure). Aggregating such labels necessarily produces graphs whose node set is exactly those six states, and the consistency across models and granularities reflects the shared label vocabulary, not emergent hierarchical dynamics. The analysis therefore shows that CoT text can be relabeled into the authors' six-state scheme, not that the CoT independently exhibits a six-state FSM.

full rationale

Two load-bearing structural claims reduce to the annotation protocol by construction. Section 2.5 says termination and invalid transitions are 'explicitly enforced during annotation,' and Appendix A instructs that init and closure appear at most once and that mid-chain final-answer sentences must be labeled deduce. Consequently the terminal-closure/no-repeated-init topology in Figure 3 and the Section 4(e) 'consistent core structure' are artifacts of the label vocabulary, not emergent findings. This is the main circularity. Separately, the abstract advertises Q-Value guided steering with gains on AIME25, MATH-500, GSM8k, and GPQA Diamond, plus capture of states in the latent state; none of this appears in the body. Section 5 lists controllability only as a potential application, and Section 2.1 says the framework models behavioral output text, not internal neural mechanisms. I flag these as serious evidence gaps and internal contradictions, but they are correctness/accountability problems rather than circularity reductions, so they do not by themselves raise the circularity score further. There is no load-bearing self-citation chain: the annotation citations (Venhoff et al., 2025b; Bogdan et al., 2025) are external works. The remaining empirical content—model-specific state frequencies, FSM-length/accuracy correlations, and transition-probability differences—still carries information independent of the enforced constraints, so the paper is not wholly circular. Score 5 reflects the central definitional reduction of the FSM topology while acknowledging the partially independent descriptive statistics.

Assumptions & free parameters 6 free parameters · 6 assumptions · 2 invented entities

The FSM analysis introduces no fitted scalar parameters—transition probabilities are observed frequencies. Instead, the load-bearing choices are categorical: the six-state taxonomy, the annotation rules, the LLM annotator, the dataset subsets, and the granularity choices. The abstract's Q-value steering would introduce additional fitted quantities, but none are defined in the body.

free parameters (6)

Number and definition of reasoning states = 6 states: init, deduce, augment, uncertain, backtrack, closure
The whole analysis depends on this hand-built taxonomy; no automatic state discovery is performed.
Annotation granularity and self-loop merging = sentence-level and paragraph-level; consecutive identical states merged
Changing granularity or the self-loop merging rule changes FSM length and all transition probabilities.
GPT-4o-mini annotator temperature = 1e-19
A near-deterministic annotator setting, but annotation noise and labeler bias are still possible.
Max output token limit = 32,768
This chosen limit constrains how long reasoning traces can be, directly affecting FSM length.
GPQA subset selection = first 50 questions of GPQA Diamond
The paper uses a truncated, non-random subset of GPQA; selection can bias the transition statistics and accuracy comparisons.
Q-value steering hyperparameters = not stated
The abstract claims a Q-value steering policy, but the body contains no Q-values, discount factor, intervention schedule, or baseline definitions.

assumptions (6)

domain assumption Every annotated text span corresponds to exactly one of the six predefined reasoning states.
Introduced in Section 2.2 and enforced by the annotation prompts; the discrete single-label mapping is not derived from data.
domain assumption The dataset-level transition matrix, obtained by pooling independent chains, is a valid representation of reasoning dynamics.
Used in Section 3 Evaluation Metrics; pooling assumes stationarity across problems and models.
ad hoc to paper GPT-4o-mini annotations are a reliable proxy for true reasoning states, validated by 10% manual review.
Section 3 reports Cohen's kappa 0.89 on 10% of annotations; there is no external ground truth for the six-state taxonomy.
domain assumption A memoryless FSM captures the reasoning dynamics of LRMs.
Stated at the start of Section 2; the authors acknowledge that pushdown automata or other stateful models could be more accurate.
ad hoc to paper Enforcing init/closure constraints during annotation does not distort the FSM statistics.
Section 2.5 and Appendix A instruct annotators that init and closure appear at most once and that mid-chain provisional answers must be labeled as deduce.
domain assumption Polya's four-step problem-solving framework justifies the chosen six-state taxonomy.
Section 2.1 connects init/deduce/augment/backtrack to Polya's stages; this is a scholarly analogy, not a derivation.

invented entities (2)

Six abstract cognitive states
purpose: Labels for CoT spans; the basis of the FSM and all transition statistics.
They are analytical constructs defined by the authors. The abstract claims they are captured in the latent state, but no activation or probing evidence is presented in the body.
Q-Value guided steering policy
purpose: Inference-time control method that estimates long-horizon utility of state transitions and applies sparse steering at sentence boundaries.
Mentioned only in the abstract; no algorithm, training procedure, equations, or experimental results appear in the submitted full text.

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Cite this review

Pith. "Pith review of Modeling Hierarchical Thinking in Large Reasoning Models." pith.science (2026). https://pith.science/paper/S4THXWCO

@misc{pith2026251022437,
  author       = {Pith},
  title        = {Pith review of: Modeling Hierarchical Thinking in Large Reasoning Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/S4THXWCO}},
  note         = {Machine review of arXiv:2510.22437}
}

read the original abstract

Large Reasoning Models (LRMs) solve complex tasks by generating long Chain-of-Thought (CoT) sequences; however, the emergent dynamics governing reasoning trajectories are not well understood and can lead to inconsistencies and reasoning pathologies. In this work, we propose to approximate LRM's emerging hierarchical reasoning dynamics as a trajectory within a Finite State Machine (FSM) transitioning among six abstract cognitive states. We demonstrate that these states and transitions can be captured in the latent state of the model. We believe that this representation can have different applications in the interpretability and optimization of LRM models. For example, by analyzing the topology of these transitions, we identify statistical shifts in reasoning strategies that help identify effective reasoning chains from those that fail. To illustrate these potential advantages, we propose Q-Value guided steering, a training-free inference-time control method that treats reasoning as a planning problem. We estimate the long-horizon utility of state transitions and apply sparse, orthogonal activation steering at sentence boundaries to align the CoT generation with optimal reasoning policies. Experiments across four benchmarks (AIME25, MATH-500, GSM8k, and GPQA Diamond) using three state-of-the-art open reasoning models demonstrate that Q-Value steering policy achieves significant performance gains with "surgical" efficiency, often requiring 25 times fewer interventions than greedy and weighted baselines, which suggests that reasoning can be effectively controlled by guiding high-level cognitive dynamics rather than micro-managing token generation. Code is available at: https://github.com/shahariar-shibli/CoT-FSM.

Figures

Figures reproduced from arXiv: 2510.22437 by the authors.

**Figure 2.** State transition (row centric) visualization across models. Top row: sentence and paragraph level transition [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗

**Figure 3.** State transition graphs across models. Top row: sentence and paragraph level transition graphs on [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗

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Reference graph

Works this paper leans on

15 extracted references · 2 linked inside Pith

[6]

init" state. It depends on the label definition. - The last sentence is usually a

final conclusion (closure)→Deciding a *final answer/option* to the query or output action, either directly or as a meta-statement. ### ANNOTATION RULES - Annotate the whole reasoning chain. Label each sentence separately. - Match which definition best fits the sentence. Assign one label per sentence. Do NOT merge sentences. - The first sentence may *NOT* ...
[10]

Analyze paragraph(s) carefully
[11]

Split paragraph(s) into smaller segments (pseudo-paragraphs or sentences) when multiple reasoning behaviors appear inside
[12]

Assign exactly one reasoning label to each segment
[13]

### AVAILABLE LABELS and LABEL CODES Each segment may express one of the following tasks:

Finally, merge consecutive segments that have the same label into a single labeled span. ### AVAILABLE LABELS and LABEL CODES Each segment may express one of the following tasks:
[14]

initialization (init)→Restating, reframing, or clarifying the task before starting reasoning
[15]

deduction (deduce)→Performing calculations, making inferences, drawing implications, doing computations, often doing intermediate conclusions
[16]

augmentation (augment)→Strengthening or extending reasoning through any of the following: - recalling facts, internal knowledge, provided information (augment-fact) - stating or planning the solution (augment-plan) - doing example testing or case trials (augment-test) - exploring alternate solution paths (augment-branch) - refining/correcting/verifying/me...

Show all 15 references

[17]

May acknowledge missing information, ambiguous problem framing, needs review or the possibility that deduction could be wrong

uncertainty estimation (uncertain)→Explicitly expressing doubt, confusion, hesitation, or lack of confidence about current step, assumptions, calculations. May acknowledge missing information, ambiguous problem framing, needs review or the possibility that deduction could be wrong
[18]

backtracking (backtrack)→Revisiting earlier steps/assumptions, re-reading the instructions, re-evaluating earlier results often following an uncertainty or realization of error
[19]

however",

final conclusion (closure)→Deciding a *final answer/option* to the query or output action, either directly or as a meta-statement. You may need to split the last segment to isolate the closure part. ### ANNOTATION RULES - Do not assume one label per paragraph. Even within a si...
[1600]

end"] ["deduce

= 700 =>28x−924 = 700 =>28x= 1624 => x= 1624/28 = 58?compute28∗ 58 = 1624yes.Sox= 58.["end"] ["deduce"] P luginto(ii) : (58−40) 2 +y 2 = 900 =>18 2 +y 2 = 900 =>324 +y 2 = 900 => y2 = 576 => y= 24.["end"] ["uncertain"] Which sign? G not on line; area absolute uses magnitude. [...
[2023]

Eric Zhao, Pranjal Awasthi, and Sreenivas Gollapudi

Tree of thoughts: Deliberate problem solving with large language models.Advances in neural information processing systems, 36:11809–11822. Eric Zhao, Pranjal Awasthi, and Sreenivas Gollapudi
[2024]

arXiv preprint arXiv:2410.05229

Gsm-symbolic: Understanding the limitations of mathematical reasoning in large language models. arXiv preprint arXiv:2410.05229. Marianna Nezhurina, Lucia Cipolina-Kun, Mehdi Cherti, and Jenia Jitsev. 2024. Alice in wonderland: Simple tasks showing complete reasoning breakdown...

2024 arXiv
[2025]

15 A Annotation Prompts This appendix provides the full prompts used for reasoning-state annotation with GPT-4o-mini

Sample, scrutinize and scale: Effective inference-time search by scaling verification.arXiv preprint arXiv:2502.01839. 15 A Annotation Prompts This appendix provides the full prompts used for reasoning-state annotation with GPT-4o-mini. We provide both sentence-wise and paragr...

arXiv

Pith tools

Reviewed August 4, 2026 · model on record in the stance chip above.

[1] [6]

init" state. It depends on the label definition. - The last sentence is usually a

final conclusion (closure)→Deciding a *final answer/option* to the query or output action, either directly or as a meta-statement. ### ANNOTATION RULES - Annotate the whole reasoning chain. Label each sentence separately. - Match which definition best fits the sentence. Assign one label per sentence. Do NOT merge sentences. - The first sentence may *NOT* ...

[2] [10]

Analyze paragraph(s) carefully

[3] [11]

Split paragraph(s) into smaller segments (pseudo-paragraphs or sentences) when multiple reasoning behaviors appear inside

[4] [12]

Assign exactly one reasoning label to each segment

[5] [13]

### AVAILABLE LABELS and LABEL CODES Each segment may express one of the following tasks:

Finally, merge consecutive segments that have the same label into a single labeled span. ### AVAILABLE LABELS and LABEL CODES Each segment may express one of the following tasks:

[6] [14]

initialization (init)→Restating, reframing, or clarifying the task before starting reasoning

[7] [15]

deduction (deduce)→Performing calculations, making inferences, drawing implications, doing computations, often doing intermediate conclusions

[8] [16]

augmentation (augment)→Strengthening or extending reasoning through any of the following: - recalling facts, internal knowledge, provided information (augment-fact) - stating or planning the solution (augment-plan) - doing example testing or case trials (augment-test) - exploring alternate solution paths (augment-branch) - refining/correcting/verifying/me...