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REVIEW 4 major objections 5 minor 46 references

Thoughts without Thinking: Reconsidering the Explanatory Value of Chain-of-Thought Reasoning in LLMs through Agentic Pipelines

T0 review · 4 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read Fails: Chain-of-thought reasoning can't explain or improve LLM outputs

desk verdict A well-intentioned workshop paper whose evidence is too confounded to support its claim that CoT doesn't help in agentic pipelines—but the qualitative examples and the system itself are worth a look. read the letter →

arxiv 2505.00875 v1 pith:J5YHNJ2N submitted 2025-05-01 cs.AI

Ramesh Manuvinakurike , Emanuel Moss , Elizabeth Anne Watkins , Saurav Sahay , Giuseppe Raffa , Lama Nachman This is my paper · ORCID
classification cs.AI
keywords chain-of-thoughtexplainableAIagenticpipelineslargelanguagemodelsLLM-as-a-judgehuman-centeredXAItaskguidancequalitativecontentanalysis
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 sets out to test a common assumption in explainable AI: that chain-of-thought (CoT) text—the step-by-step verbalization some LLMs produce before answering—makes an agentic system's outputs both better and more transparent to end users. Working with a perceptive task-guidance system that helps technicians assemble and disassemble toy vehicles, the authors compared three non-reasoning LLMs with three DeepSeek-distilled reasoning variants that emit CoT. They found that the non-reasoning models received higher human and LLM-judge scores for accuracy, comprehensiveness, and helpfulness on task and organizational questions. A qualitative analysis of the CoT text showed it drifting toward familiar but wrong associations (the Einstellung paradigm) and producing verbose, logically flawed rationalizations that obscure rather than explain. The paper concludes that CoT alone neither improves outputs nor provides explainability; it produces explanations without explainability.

What carries the argument

The central object is the agentic pipeline, a fixed-and-dynamic flow of LLM-based agents (perceptors, planners such as lead/query/answer planners, and actors including the RAG module, question answerer, reformulator, and safety agent) with the same LLM deployed in each agent. Within this pipeline, chain-of-thought is treated as the vehicle of explainability under test: the DeepSeek-distilled reasoning models produce a CoT scratchpad before each response, while the Llama and Qwen models do not. The comparison between these two groups, together with human and LLM-as-a-judge scoring of answers and thoughts, is what carries the argument; the qualitative content analysis of CoT tuples supplies the mechanism by which CoT misleads.

What would settle it

Run a within-model ablation on the same agentic pipeline: take one model family (e.g., Qwen) and compare its responses with chain-of-thought prompting enabled versus disabled on the same benchmark questions. If the CoT-enabled version achieves equal or higher human and judge scores, the paper's claim that CoT alone does not improve outputs would be contradicted.

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Extended reading notes

Core claim

The paper's central claim is that in an agentic pipeline, chain-of-thought reasoning text does not deliver the two things it is often credited with: better answer quality and explainability. Quantitatively, the DeepSeek-distilled reasoning models, which generate CoT, scored significantly lower than their non-reasoning counterparts on both task-specific and organizational/social questions, as rated by human reviewers and an LLM judge (Wilcoxon rank-sum tests). The reviewer scores of the CoT thoughts were only weakly correlated with the reviewer scores of the answers, suggesting the thoughts do not steer the model to correct responses. Qualitatively, the CoT text exhibited reasoning that fixated on familiar machine-related vocabulary rather than the task context (Einstellung paradigm), made hasty generalizations from incomplete context, and produced excessive text that raised the burden on any user seeking an explanation. The authors argue that such CoT output amounts to explanations without explainability: it does not improve end users' ability to understand the system or achieve their goals.

Load-bearing premise

The paper assumes that the performance gap between the DeepSeek distilled models and the Llama/Qwen models is caused by chain-of-thought, even though the models differ in family, size, and training method and no model was tested both with and without CoT.

Editorial extensions

If this is right

  • Showing users the raw chain-of-thought text of an agentic system will not, by itself, produce actionable transparency; systems need explanations designed for the user's task.
  • Agentic pipelines that rely on CoT for explainability should be re-examined; the paper's results suggest the traceability of agent calls does not guarantee trustworthy explanations.
  • Benchmarking reasoning models on real participatory task questions can yield different conclusions than standard reasoning benchmarks, so deployment-oriented evaluation should include human-centered scoring.
  • If CoT text can actively mislead, safety and accountability mechanisms should not treat CoT as a faithful record of why an answer was produced.
  • LLM-as-a-judge scores, though weakly correlated with human scores in this study, may still serve as a scalable reference-free indicator for triaging response quality.

Reading between the lines

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

  • A natural extension beyond this paper is a within-model ablation that toggles chain-of-thought on and off for the same model, which would separate the effect of CoT from differences in model family and distillation.
  • The 'explanations without explainability' framing likely extends beyond agentic pipelines: any system that emits fluent rationalizations ungrounded in retrieved evidence may mislead users just as much as it informs them.
  • A testable follow-up would measure end users' actual task performance (time to completion, error rate) with and without CoT text displayed, rather than relying only on expert and judge ratings of answer quality.
  • The weak correlation between thought scores and answer scores could be exploited as an early-warning signal for unreliable answers, though the paper does not draw this conclusion.
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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

4 major / 5 minor

Summary. This paper reports early findings from an agentic task-guidance pipeline evaluated offline on a participatory dataset of task-based and organizational/social questions. Six LLMs (Llama-3-8B, Qwen-7B/14B, and their DeepSeek-R1-distilled counterparts) are deployed as the LLM in each agent of the pipeline. Answers are scored by human reviewers and GPT-4o as a judge on a -1/0/0.5/1 scale. The paper's headline claims are that chain-of-thought reasoning alone does not lead to better outputs and does not offer explainability. The reported results show higher judged scores for the non-reasoning models, weak correlation between thought scores and answer scores for reasoning models, and a qualitative analysis of three prompt-CoT-output tuples suggesting that CoT can be unhelpful or misleading. The authors acknowledge the preliminary nature of the work and defer ablations to future work.

Significance. If the causal claim were established, the paper would be a useful cautionary contribution to HCXAI, challenging the assumption that surfacing chain-of-thought text in agentic systems provides transparency. The participatory dataset built with manufacturing technicians is a genuine strength, as is the use of both human and LLM-based judges and the authors' explicit acknowledgment of limitations. However, as it stands the central causal claim is not identified by the experimental design, and the explainability claim lacks user evidence. The paper is therefore better read as a preliminary observational study than as a demonstration that CoT has no explanatory value.

major comments (4)
  1. [§5-6, Fig. 2] The comparison of 'non-reasoning' Llama/Qwen models against DeepSeek-R1-distilled models is confounded by model family, size, distillation procedure, and decoding behavior; there is no within-model ablation that toggles chain-of-thought on and off, and no condition suppresses the reasoning tokens in the DeepSeek models. The paper's own Limitations section states that ablations of each agent are left to future work. Therefore the abstract's claim that 'CoT reasoning alone does not lead to better outputs' is not identified by the reported experiment. What is supported is the weaker claim that the DeepSeek-distilled model family received lower judged answer scores in this agentic pipeline.
  2. [Abstract and §7 Discussion] The claim that CoT 'does not improve the ability of end users to better understand systems or achieve their goals' is an empirical claim about users, but no user study is reported. The conclusion rests on the authors' qualitative reading of three tuples (Table 1) and on the weak correlation between thought and answer scores, neither of which measures user comprehension or task success. A user study with a condition that shows or hides CoT text is needed to support the explainability half of the headline claim.
  3. [Table 1, tuple C] The chain-of-thought text shown for the input 'Can you communicate with humans in multiple languages?' is identical to the chain-of-thought shown for tuple B ('How do you ensure data privacy and security?'), yet §7's qualitative analysis of tuple C relies on details (e.g., 'Since the context only lists English') that are not present in the displayed CoT. This suggests either a copy-paste error in the table or an unacknowledged model perseveration; either way, the qualitative evidence for the tuple C discussion as presented is not trustworthy and must be corrected or re-analyzed.
  4. [§6 Results] The paper reports weak human-LLM judge agreement (Cohen's kappa = 0.48 for org-soc, 0.28 for task) but Figure 2 plots LLM-as-judge scores alongside human scores and uses them in significance tests. Given the weak agreement, the paper should state whether the main effect (reasoning vs. non-reasoning) holds when using only human scores, and should caution against pooling or relying on LLM judge scores for the central comparison.
minor comments (5)
  1. [§7 Discussion] There is a typo in the word 'susceptiblity' that should be corrected to 'susceptibility'.
  2. [§3 Data] The data counts given as 'N=750/300 (Task), 300 (Org-Soc), 150 (Thoughts)' are unclear; please clarify the exact number of question-answer tuples and thought annotations per condition.
  3. [Fig. 2 caption] The caption refers to the 'middle column in the heatmap Figure 2', but the figure appears to be a bar chart; please clarify what the heatmap is and how it relates to the reported bar values.
  4. [§5 Experiments] The sentence 'the experts assign scores to every answers generated by the agentic flow using 6 different LLMs' is ambiguous; the six LLMs are the systems being evaluated, while scoring is performed by human reviewers and GPT-4o. Please reword to avoid confusion.
  5. [Table 1] The acronym 'MARIE' appears in the example tuples without prior definition; please define the system name when it is first introduced.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the study is an empirical comparison with external judges; the between-model confound is a validity threat, not a circular reduction.

full rationale

The paper does not derive its conclusions from definitions or fitted parameters. The central quantitative claim is that DeepSeek-distilled reasoning models receive lower reviewer scores than non-reasoning models; this is an empirical observation scored by human reviewers and an LLM judge, not a quantity constructed from the claim itself. The weaknesses in the paper are experimental confounds: the reasoning and non-reasoning models differ by family, size, and distillation procedure, with no within-model CoT toggle, so the comparison may not isolate CoT. Similarly, the explainability claim is supported by qualitative analysis of example tuples rather than by direct end-user measurement. These are threats to internal and construct validity, not circularity. No equation in the paper reduces to its inputs, no fitted parameter is renamed as a prediction, and no load-bearing premise is justified solely by a self-citation. The only self-citations (QA-TOOLBOX and the HCXAI position paper) supply dataset provenance and framing, not the paper's conclusion. Accordingly, the circularity score is 0.

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

The paper's conclusions rest on domain assumptions about the proxy task, the evaluation protocol, the causal attribution of performance differences to CoT, and the sufficiency of qualitative examples for claims about user-facing explainability.

assumptions (4)
  • domain assumption The toy assembly/disassembly task is a valid proxy for factory technician task execution.
    The system is tested on toy vehicles instead of real manufacturing, stated in Section 1, and the generalizability of findings depends on this proxy being representative.
  • domain assumption The human and LLM-as-a-judge scoring protocol yields valid measures of accuracy, comprehensiveness, and helpfulness.
    Scores on a -1 to 1 Likert scale are the primary quantitative evidence; the weak inter-rater agreement (kappa 0.28 for task answers) casts doubt on the reliability of this measure.
  • domain assumption Performance differences between reasoning and non-reasoning model families are attributable to chain-of-thought generation rather than other model differences.
    The between-model comparison in Section 6 assumes the CoT mechanism is the causal variable; no ablation isolates it.
  • domain assumption Qualitative analysis of three selected CoT tuples is sufficient to support claims about explainability for end users.
    The paper's explainability conclusion in Section 7 is based on a small number of examples without direct user testing.

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Cite this review

Pith. "Pith review of Thoughts without Thinking: Reconsidering the Explanatory Value of Chain-of-Thought Reasoning in LLMs through Agentic Pipelines." pith.science (2026). https://pith.science/paper/J5YHNJ2N

@misc{pith2026250500875,
  author       = {Pith},
  title        = {Pith review of: Thoughts without Thinking: Reconsidering the Explanatory Value of Chain-of-Thought Reasoning in LLMs through Agentic Pipelines},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/J5YHNJ2N}},
  note         = {Machine review of arXiv:2505.00875}
}
read the original abstract

Agentic pipelines present novel challenges and opportunities for human-centered explainability. The HCXAI community is still grappling with how best to make the inner workings of LLMs transparent in actionable ways. Agentic pipelines consist of multiple LLMs working in cooperation with minimal human control. In this research paper, we present early findings from an agentic pipeline implementation of a perceptive task guidance system. Through quantitative and qualitative analysis, we analyze how Chain-of-Thought (CoT) reasoning, a common vehicle for explainability in LLMs, operates within agentic pipelines. We demonstrate that CoT reasoning alone does not lead to better outputs, nor does it offer explainability, as it tends to produce explanations without explainability, in that they do not improve the ability of end users to better understand systems or achieve their goals.

Figures

Figures reproduced from arXiv: 2505.00875 by the authors.

Figure 1
Figure 1. Shows sample agentic flow depending on the type of input query from the user. Once the question is ingested, the lead planner [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Shows the reviewer scores for the answers as rated by humans and LLM-as-a-judge on Task and Org-Soc questions. We [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. The figure shows our agentic implementation of the perceptive task guidance system. The agents are categorized into [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Shows more examples of dynamic flows in the agentic flow. The examples show the input question from the users in the [PITH_FULL_IMAGE:figures/full_fig_p011_4.png]

Discussion (0). Continue with ORCID to comment.

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