{"id":"f898bdc9-7d48-48a9-875d-cc791bc8ac16","arxiv_id":"2502.03080","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":5.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"IAO, a structured Input-Action-Output prompt template, modestly improves zero-shot LLM accuracy on several reasoning benchmarks and is preferred by human raters for spotting reasoning errors.","lead":"This paper introduces IAO prompting, a template that asks an LLM to break each reasoning step into a subquestion, the input knowledge used, the action taken, and the output produced. Experiments on seven reasoning benchmarks suggest small accuracy gains over zero-shot chain-of-thought, and human raters prefer IAO for spotting errors in wrong answers.","discovery_kind":"extension","skeptic_critique":{"model":"deepseek-v4-flash","headline":"IAO's transparency claim is load-bearing and unsupported: the paper's own StrategyQA case study (Table 5) shows GPT-4 fabricating 'Input' facts (100,000 passengers, 7,610 seats), so the structured trace is not a faithful record of stored-knowledge access.","rationale":"The reader's weakest assumption is exactly the load-bearing one: IAO's structured output must be a faithful trace of internal knowledge access for the transparency and verification claims to hold. I agree, and the paper provides internal evidence that this assumption is insecure. Table 5's GPT-4 IAO trace does not retrieve known values for the StrategyQA question; it generates 'let's say' and 'let's assume' quantities and then compares them. If the model labels invented numbers as Outputs and treats them as inputs to later steps, then the format is not making stored-knowledge flow explicit; it is making plausible-sounding assumptions explicit. That may still help human auditing, which the human evaluation supports, but it is a much weaker and different claim than the abstract's 'transparency in how LLMs leverage their stored knowledge.' The proposed counterfactual test would settle whether the Output fields are causally tied to the Input fields. If the final answer is insensitive to substituted Input values, the trace is post-hoc rationalization rather than a causal record. I do not think this warrants rejecting the empirical accuracy trend or the weaker human-auditability finding; those can stand as conditional. The verdict remains CONDITIONAL, matching the reader, so I mark UNCHANGED. The reproducibility concerns (unpublished prompt template, single GPT-4 run, no significance tests) are also real but secondary to the conceptual problem that the central novelty is unverified and partially contradicted by the authors' own example.","tokens_in":23107,"tokens_out":9479,"duration_ms":85549,"concrete_test":"Run the paper's Table 5 StrategyQA question with the exact IAO template and GPT-4 (gpt-4-1106-preview) for 20 temperature-0 repetitions, and fact-check every numeric value stated in the Input/Action/Output fields against an external source (30th Street Station daily traffic is approximately 120,000; Dorton Arena capacity is approximately 7,610). Then perform a counterfactual intervention: replace the Step 1 and Step 2 Output values with 10,000 and 50,000 in the generated chain and continue generation; a faithful knowledge trace should produce '10,000 < 50,000, so Yes.' If the model instead retains the original answer or ignores the substituted Input values, or if the stated Inputs are invented or vary across runs, the IAO fields are not causally faithful traces of stored-knowledge access, and the transparency claim is unsupported.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The paper's headline contribution is that IAO makes knowledge flow explicit and supports verification. That requires the fields the model writes under Input, Action, and Output to be faithful traces of the knowledge actually retrieved and used. The manuscript never checks this against any external measure, and its own flagship case study cuts against it. In Table 5, GPT-4's IAO trace for the StrategyQA question about 30th Street Station and Dorton Arena places 'Let's say the daily passenger traffic at 30th Street Station is 100,000 people' and 'Let's assume Dorton Arena has a seating capacity of 7,610 people' into the Output fields, with Input fields that state only a need to find or estimate the data. These are not reports of stored knowledge; they are invented placeholders introduced so the comparison can proceed. The paper presents this as a 'transparent and verifiable solution' and as evidence that IAO identifies knowledge gaps, but the gap is not identified and checked; it is filled with unverified assumptions. The claim that IAO 'provides transparency in how LLMs leverage their stored knowledge' is therefore unsupported, even on the authors' own example. The human evaluation only measures which chain readers prefer for spotting mistakes, not whether the chain is causally faithful; the authors cite Turpin et al. (2024) on unfaithful CoT but never address that risk for IAO. Secondary numeric inconsistencies (e.g., text says two-stage GSM8k is 82.3 while Table 6 says 83.9; text says AQuA drops 2.1 while Table 1 shows a 2.1-point gain) further weaken confidence, but the load-bearing problem is the unfaithful trace.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper introduces IAO (Input-Action-Output) prompting, a zero-shot structured template that asks an LLM to decompose each reasoning step into Subquestion, Input, Action, and Output fields. The authors evaluate IAO against zero-shot CoT, Least-to-Most (L2M), and Plan-and-Solve (P&S) on arithmetic, commonsense, symbolic, and logical reasoning datasets using GPT-4 and PALM-2. They report accuracy gains for IAO over zero-shot CoT on most tasks, a two-stage extension, a few-shot variant, an ablation of template fields, and a human evaluation suggesting that IAO chains are easier to inspect for errors. The paper claims that IAO not only improves accuracy but also makes the model's stored-knowledge access and application explicit and verifiable.","tokens_in":23400,"tokens_out":6191,"duration_ms":57580,"significance":"If the accuracy results hold up statistically, IAO is a simple, domain-independent prompting method with modest but consistent gains over zero-shot CoT, and its structured output is plausibly helpful for human audit of wrong answers. The paper's distinctive claimed contribution, however, is the transparency of knowledge flow: that IAO fields reveal and verify how LLMs access stored knowledge. That claim is not established by the evidence, and the paper's own case study undercuts it. The strengths are the breadth of tasks, the inclusion of a human evaluation, the honest Limitations section, and the ablation analysis.","major_comments":[{"comment":"The central transparency claim is unsupported and is contradicted by the paper's own flagship example. In Table 5, GPT-4's IAO trace for the 30th Street Station / Dorton Arena question writes 'Let's say the daily passenger traffic at 30th Street Station is 100,000 people' and 'Let's assume Dorton Arena has a seating capacity of 7,610 people' into the Output fields, while the Input fields only state a need to find or estimate the data. These are invented placeholders, not retrieved stored knowledge, yet the text describes this as a 'transparent and verifiable solution.' The paper never validates the IAO-generated knowledge fields against any external ground truth, and the human evaluation only measures which format annotators prefer for spotting mistakes, not whether the fields are faithful. The risk of unfaithful rationalization is acknowledged via the citation to Turpin et al. (2024) but is never addressed for IAO. To support the transparency claim, the authors must either provide external validation of the knowledge fields (e.g., fact-checking the Input/Output entries against a knowledge base or human annotation) or explicitly reframe the contribution as 'structured reasoning text that humans find easier to audit.'","section":"Case studies (Table 5); Abstract; Discussion"},{"comment":"The reported accuracy advantages are not supported by significance testing. GPT-4 results come from a single run, and PALM-2 results are averages of 3 runs without standard deviations. Several gains are small (e.g., 2.1 p.p. on AQuA in Table 1), so they may be within noise. Moreover, the text contradicts the tables in two places: the PALM-2 subsection states that AQuA accuracy decreased by 2.1 p.p. although Table 1 shows an increase from 61.8 to 63.9, and the two-stage IAO paragraph reports GSM8k accuracy of 82.3% for two-stage IAO while Table 6 shows 83.9%. These inconsistencies must be corrected, and the authors should report standard deviations and run significance tests (or at least confidence intervals) for the accuracy comparisons.","section":"Results (Arithmetic, PALM-2; Two-stage IAO)"},{"comment":"The exact IAO prompt template is never given. The paper describes the four fields (Subquestion, Input, Action, Output) but does not provide the instruction text that elicits this structure from the models, nor the exact prompts used for the zero-shot CoT, L2M, and P&S baselines. Without the precise prompts, the method cannot be reproduced or the comparison fairly evaluated. The appendix examples show outputs but not the prompt that produced them.","section":"IAO Prompting; Experimental Setup"},{"comment":"The human evaluation is underspecified and does not warrant the claim of 'significantly improves.' No details are given on the number of annotators per item, the annotation instructions, whether the order of the two chains was randomized, or inter-annotator agreement. The sample is small (10 correct and 10 wrong questions per dataset for three datasets), and no statistical test is reported for the preference percentages. This matters because the paper uses this evaluation as evidence for its transparency contribution; as reported, the results only indicate a subjective preference, not a measurable improvement in verification accuracy.","section":"Human evaluation; Table 7"}],"minor_comments":[{"comment":"The sentence 'There is a decrease in terms of percentage points (p.p), 2.1%, for the AQuA dataset' is confusing and contradicted by Table 1; the intended meaning appears to be an increase, and the phrasing should be clarified.","section":"Results, PALM-2 paragraph"},{"comment":"In the PALM-2 rows, the CoT chain states Dorton Arena has a seating capacity of 5,000, while the IAO chain in the same table states 10,000; the surrounding text says the model used '5,000 seating capacity.' This inconsistency is not discussed and further shows that the IAO 'Input' fields cannot be taken as reliable reports of stored knowledge.","section":"Table 5"},{"comment":"The aggregation used for the 'Average' in Table 3 is not defined, and Table 8 only reports four of the seven datasets; the authors should specify which tasks and runs contribute to each ablation average.","section":"Ablation studies; Tables 3 and 8"},{"comment":"The BIG-bench collection is cited in the dataset section but is missing from the reference list, and the sentence in the compute-cost section ending 'justify the extra computational expense of two results in improved accuracy' is ungrammatical and should be rewritten.","section":"References; Compute cost"},{"comment":"There are numerous small typos and spacing errors (e.g., 'the answer is(D)' in Table 4, 'Therefore, the answer isNone' in error examples). A careful proofreading pass is needed.","section":"General"}],"recommendation":"major_revision","confidential_remarks":"The transparency/fidelity issue is the main blocker to publication. The accuracy claims are fixable with statistical rigor and consistency corrections, but the paper's title and abstract promise something the experiments do not deliver. I would suggest the authors either add external fact-checking of the IAO fields or substantially rewrite the contribution to focus on structured chains for human error-spotting, which the human evaluation does partially support."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"The empirical core of this paper is modest but real: IAO prompting, with its Subquestion/Input/Action/Output template, edges out zero-shot CoT on most of the tested reasoning benchmarks, and the ablation shows the components matter. The comparisons to L2M and P&S are fair, and the human evaluation, though small, is a sensible way to test whether structured chains help people spot errors. I'd believe the accuracy trend if the runs were more solid—the GPT-4 numbers come from a single run, no significance tests are reported, and the text contradicts its own tables twice (the AQuA \"decrease\" that Table 1 shows as a 2.1-point gain, and the GSM8K 82.3 vs 83.9 confusion). Those are fixable sloppiness, but they need cleaning up.\n\nThe real problem is the transparency claim. The paper says IAO makes knowledge flow explicit and supports verification, but the Input/Action/Output fields are just text the model writes; there is no check that they correspond to knowledge actually retrieved or used. The authors cite Turpin et al. on unfaithful CoT but never ask whether IAO chains suffer the same failure. Their own flagship StrategyQA example (Table 5) is damning: GPT-4 fills the Input fields with invented numbers (100,000 daily passengers, 7,610 arena seats) and the paper describes this as \"transparent and verifiable.\" It isn't verification; it's a plausible-sounding guess presented inside a structured box. The human evaluation only shows that readers prefer IAO chains for locating mistakes, not that the chains are faithful traces—and when the answer is wrong, preferring a chain that confidently fabricates assumptions is not obviously a good outcome.\n\nI would not desk-reject this. The method is simple, the accuracy gains are consistent enough to be worth investigating, and the ablation is helpful. A serious referee should push the authors to either drop the \"transparency into stored knowledge\" language or validate it with an external measure—e.g., testing whether IAO's Input fields match retrieved or known facts, or comparing against post-hoc rationalizations. The paper also needs the prompt template included in the appendix so others can reproduce the results without reverse-engineering it.\n\nBottom line: worth a round of review, but the central contribution should be reframed as a structured prompting method that sometimes improves accuracy and helps humans find errors in the chain, not as a window into how LLMs access knowledge.","headline":"IAO prompting shows a plausible small accuracy gain over zero-shot CoT, but its headline transparency claim is unsupported and is undercut by the paper's own StrategyQA case study.","tokens_in":24014,"tokens_out":2190,"would_cite":false,"duration_ms":22177,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"IAO prompting uses a structured Input-Action-Output template to make LLM knowledge flow explicit, yielding small accuracy gains and much clearer error traces.","keywords":["IAO prompting","chain-of-thought","knowledge transparency","large language models","reasoning templates","zero-shot reasoning","hallucination detection","knowledge verification"],"falsifier":"Generate an IAO trace, then re-run the prompt with only the Input line edited to a different quantity; if the model still produces the same Output as before across many examples, the declared Input fields are not causally steering the reasoning.","tokens_in":22875,"feed_emoji":"🧠","tokens_out":7309,"duration_ms":64498,"temperature":0.7,"pith_summary":"IAO prompting asks an LLM to format each reasoning step as a Subquestion, the Input knowledge it uses, the Action it applies, and the Output it produces. The paper claims this makes the knowledge flow visible enough to audit while also improving zero-shot accuracy over chain-of-thought baselines on seven reasoning datasets. The strongest support comes from the transparency side: when answers were wrong, human evaluators preferred the IAO trace for locating the mistake 73–87% of the time, and rated it more transparent and interpretable. If true, the result is a simple, domain-independent method for turning free-text reasoning into checkable knowledge steps.","feed_headline":"Structured prompts make LLM reasoning auditable","feed_subtitle":"A four-field template helps humans locate reasoning errors and lifts accuracy on seven benchmarks.","key_machinery":"The central object is the IAO template: each reasoning step is a quadruple (Subquestion, Input, Action, Output). Subquestion breaks the problem into a knowledge query; Input declares which given facts or previously derived outputs are used; Action states the operation to be performed; Output records the newly derived knowledge, which becomes available as Input for subsequent steps. This explicit carry-forward of Output to Input is what turns a free-form rationale into a checkable trace of knowledge flow.","core_discovery":"The central claim of the paper is that prompting LLMs to format every reasoning step as Input-Action-Output makes the knowledge the model uses explicit and, as a side effect, improves zero-shot accuracy on a range of reasoning benchmarks. Across GSM8k, AQuA, StrategyQA, CommonsenseQA, Last Letter, Date Understanding, and Object Tracking, IAO beats zero-shot CoT almost everywhere; for example, GPT-4's GSM8k accuracy rises from 90.1% to 94.2% and PALM-2's Last Letter accuracy from 75.6% to 88.8%. The main stated payoff, however, is verifiability: in human evaluation, when both IAO and CoT answers were wrong, crowdworkers chose IAO as the better text for locating the mistake 73–87% of the time, and preferred IAO for transparency and interpretability. Ablations show the Output field carries much of the benefit: removing it drops average accuracy by 12 percentage points, while removing Subquestion, Input, or Action also hurts.","pith_inferences":["An untested extension is to mine IAO traces automatically for knowledge gaps: a step whose Output contradicts the given facts, or whose Action is vague, could be flagged as a candidate hallucination site.","IAO traces could be fed back into the model for self-correction: when an Output does not match its Input or Action, the model could be prompted to redo that specific step.","The structured steps could serve as a distillation format, using IAO traces from a large model as training data to teach smaller models the same auditable reasoning style.","A useful separation test would compare IAO to CoT with matched output length to see how much of the accuracy gain comes from the explicit fields rather than from generally writing more text."],"forward_implications":["If IAO's gains hold, zero-shot reasoning can be made more auditable without any task-specific examples or multi-prompt planning.","The Output-to-Input carry-forward gives a natural slot for external tools such as a calculator or Python shell to verify arithmetic between steps.","The ablation result implies that simply asking a model to state its output is not enough; the declared Input and Action also matter for accuracy.","Because the template is domain-independent, it can be applied to any question-answering or reasoning setting where a free-text chain of thought is currently used.","The human-evaluation asymmetry, with IAO preferred for finding errors but CoT preferred when the answer is correct, suggests the two formats serve different purposes."],"supporting_citations":[{"why":"Defines the zero-shot CoT baseline ('Let's think step by step') that IAO is compared against.","marker":"Kojima et al. 2022"},{"why":"Provides few-shot CoT and the Last Letter dataset used for symbolic reasoning.","marker":"Wei et al. 2022"},{"why":"Supplies the GSM8k math word problem benchmark.","marker":"Cobbe et al. 2021"},{"why":"Supplies the AQuA arithmetic reasoning benchmark.","marker":"Ling et al. 2017"},{"why":"Supplies the StrategyQA benchmark used for commonsense and strategy reasoning.","marker":"Geva et al. 2021"},{"why":"Supplies the CommonsenseQA benchmark.","marker":"Talmor et al. 2019"},{"why":"Supplies the Date Understanding and Object Tracking tasks from BIG-bench.","marker":"Srivastava et al. 2023"},{"why":"Documents the PALM-2 model used in the experiments.","marker":"Anil et al. 2023"},{"why":"Documents the GPT-4 model used in the experiments.","marker":"Achiam et al. 2023"},{"why":"Plan-and-Solve baseline compared against in the evaluation.","marker":"Wang et al. 2023"}],"fun_headline_variants":["IAO prompting turns LLM reasoning into an audit trail","Structured prompts make LLM thinking visible","IAO: a prompt template that exposes LLM knowledge use","See how LLMs reason: IAO prompts clarify each step","Prompting LLMs with Input-Action-Output boosts transparency"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The transparency argument rests on the assumption that the labels the model writes under Subquestion, Input, Action, and Output genuinely mirror how it retrieves and applies knowledge, rather than being plausible-sounding text generated after the fact.","fun_headline_variants_meta":{"raw":{"variants":["IAO prompting turns LLM reasoning into an audit trail","Structured prompts make LLM thinking visible","IAO: a prompt template that exposes LLM knowledge use","See how LLMs reason: IAO prompts clarify each step","Prompting LLMs with Input-Action-Output boosts transparency"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000185,"raw_usage":{"total_tokens":1315,"prompt_tokens":935,"completion_tokens":380,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":551,"completion_tokens_details":{"reasoning_tokens":299}},"tokens_in":551,"tokens_out":380,"duration_ms":3862,"temperature":1.0,"reasoning_tokens":299,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-09T05:58:20.038184+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Generate an IAO trace, then re-run the prompt with only the Input line edited to a different quantity; if the model still produces the same Output as before across many examples, the declared Input fields are not causally steering the reasoning.","supporting_citations":[],"review_version":1}