{"id":"6132cf81-a294-46f3-a32b-896296f20dbd","arxiv_id":"2412.16533","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":3,"one_line_summary":"A large language model can plan and write an executable network of elementary reasoning steps for itself, and this self-guided workflow beats prior prompting schemes on sorting, arithmetic, and language counting tasks.","lead":"This paper introduces kNoT, a prompting method in which a large language model writes a structured, step-by-step workflow for itself, then executes each elementary step as a separate call. On six reasoning tasks, this self-generated workflow approach beat Chain-of-Thought, Tree of Thoughts, and Graph of Thoughts, especially on longer problems.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Section 5.1's model asymmetry confounds the headline gains: kNoT's per-query LWT scripts are written by GPT-4o, while baselines use hand-written GPT-3.5-turbo prompts, so the reported advantage does not isolate the LWT scheme.","rationale":"The reader's conditional verdict is sound, and this stress-test does not move it. However, the most load-bearing weakness is not primarily the risk that GPT-4o-generated scripts fail to parse; the reported accuracies already suggest that scripts usually execute. The sharper issue is experimental control: Section 5.1 gives kNoT access to GPT-4o for per-query script generation while baselines are executed on GPT-3.5-turbo with hand-written, non-adaptive modules. This confound directly threatens the headline comparison, because the gain could come from giving kNoT a stronger planner rather than from LWT's arbitrary-network, elementary-operation design. The reader did note the planner/executor model mismatch in the rationale, but chose a different weakest assumption, so agreement is partial. The proposed test is a minimal, feasible control: regenerate scripts with GPT-3.5-turbo and re-run Table 3. If kNoT still outperforms, the central claim is substantially strengthened; if not, the claim should be narrowed to a statement about using a strong model for planning plus LWT execution. Since either outcome is possible and the current evidence does not settle it, the conditional verdict stands.","tokens_in":19784,"tokens_out":5150,"duration_ms":45929,"concrete_test":"Rerun the six-task evaluation with kNoT's knowledge-extraction and LWT-translation steps performed by GPT-3.5-turbo, the same model used for execution and for all baselines, keeping every other prompt and setting unchanged. If sorting-32 accuracy drops from 92% toward the 12-31% baseline range, or if other headline margins shrink substantially, the reported advantage is largely attributable to GPT-4o-generated per-query scripts rather than to LWT's network and elementary-operation design. A complementary control would use GPT-4o to generate per-query ToT and GoT modules as well, testing whether the advantage survives when all schemes receive the same-strength automated planner.","verdict_should_be":"UNCHANGED","load_bearing_attack":"Section 5.1 states that for kNoT the authors 'directly leverage the LWT-formatted script generated with GPT-4o as the task instructions' while 'all prompt scheme execution' uses GPT-3.5-turbo. Baselines, by contrast, use manually prepared task-specific designs following original source code (Section 5.1, Appendix C.2). This asymmetry means kNoT receives a per-query, GPT-4o-authored plan that already performs much of the task decomposition; the arithmetic examples in Appendix A.1 show scripts specifying exactly which indexed operands to combine and in what order. ToT and GoT must instead operate from fixed hand-written modules. The central claim that the LWT network of elementary operations outperforms these baselines is therefore not isolated from the strength of the planner model. Table 6 reinforces the concern: when all schemes are executed with GPT-4o, ToT and GoT also reach 100% on most tasks, so the Table 3 margins are highly sensitive to which model writes or executes the prompts. If GPT-4o's planning ability, rather than LWT's structure, drives the gains, the headline comparison collapses even though kNoT may still be a useful method. The reduced-prompt-engineering claim is less threatened because script generation is automated, but the superiority claim needs a matched-model comparison.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes kNoT, a prompting scheme in which an LLM first produces a natural-language solution plan, translates that plan into an LWT-formatted script of numbered 'LLM' instructions with input-field and indexing notation, and then executes the script sequentially, allowing arbitrary networks of elementary LLM operations. The method is evaluated on six tasks spanning natural-language, symbolic, and arithmetic reasoning, with comparisons to CoT, ToT, GoT, and other baselines, and the paper reports large accuracy gains, lower task-specific prompt sizes, ablations, scalability results, and API cost measurements.","tokens_in":20046,"tokens_out":8396,"duration_ms":72846,"significance":"If the empirical claims are validated, kNoT is a worthwhile contribution to prompt engineering: the LWT format cleanly separates plan generation from plan execution, supports flexible network topologies, and the indexing mechanism is a natural way to force elementary operations. The paper is also refreshingly concrete in publishing its prompt templates and examples. The ablations and cost tables are useful, and the scalability experiments address an important weakness of prior schemes. However, the central comparison is currently confounded by the use of GPT-4o for kNoT's script generation while baselines are executed on GPT-3.5-turbo, so the magnitude of the reported gains over the baselines is not yet established.","major_comments":[{"comment":"The headline comparison is not model-matched. Section 5.1 states that kNoT 'directly leverage[s] the LWT-formatted script generated with GPT-4o as the task instructions', while all baseline prompt schemes are executed on GPT-3.5-turbo. Baselines therefore receive hand-written, fixed task-specific modules, whereas kNoT receives a per-query plan authored by a stronger model. Table 6 reinforces the concern: when ToT and GoT are executed with GPT-4o, they reach 100% accuracy on most tasks, so the large margins in Table 3 may reflect the planner's strength rather than the LWT structure. Please add matched-model conditions, for example kNoT with GPT-3.5-turbo-generated scripts, and ToT/GoT with GPT-4o-generated modules or at least GPT-4o execution, and report the same task table under matched planner/executor settings.","section":"§5.1, Table 3"},{"comment":"The pipeline has no specified validation or fallback for script generation, and no failure-rate measurement is reported. The claim that kNoT can 'directly leverage' a generated LWT script assumes GPT-4o always produces a syntactically valid, complete, and executable script. Please report, per task and problem size, the fraction of queries for which knowledge extraction or LWT translation fails, including malformed instructions, unresolved or out-of-range indices, non-list outputs when indexed fields are used, or scripts that reference non-existent steps. Without this information, the reliability of the method and the interpretation of the reported accuracies cannot be assessed.","section":"§5.1, Algorithm 1"},{"comment":"The few-shot arithmetic examples provided for the CoT baseline contain arithmetic and formatting errors. For example, the first example writes '25+2=37' where the preceding line already gives 35, and the second example writes '27+7=24' where the correct value is 34; later examples use '52=10', '33=9', and '527=70' without multiplication symbols. If these strings were used in the executed baseline prompts, the baselines may have been systematically handicapped, making the comparison in Table 3 unfair. Please verify the executed prompts, correct any typos in the manuscript, and re-run the affected baselines if the erroneous strings were actually used.","section":"Appendix C.2"},{"comment":"The quantitative labor-reduction claim is not internally consistent. Table 9 reports kNoT's task-specific token counts averaging about 307 tokens per task, but §5.4 states that kNoT 'requires only 600 tokens per task'; the stated ToT and GoT averages of 1467 and 1893 tokens also do not match Table 9, whose averages are about 1390 and 1746, respectively. Because the advertised 'up to 84.4% and 87.3%' reductions are central to the paper's contribution, please clarify which prompt components are counted in each table, recompute the averages, and report token and character counts consistently.","section":"§5.4, Tables 7 and 9"}],"minor_comments":[{"comment":"The symbols in Table 1 render as broken glyphs (e.g., '/reve'), making the comparison table difficult to interpret; please replace them with standard checkmark/cross symbols.","section":"Table 1"},{"comment":"The accuracy results are reported as point estimates on 100 queries per setting without confidence intervals or significance tests. Adding standard errors or a small statistical comparison would strengthen the claim of 'significantly outperforms', especially for the smaller margins such as Yelp (75% vs 52%).","section":"§5.2, Table 3"},{"comment":"Figure 4 is referenced in the text but is not described in enough detail in the manuscript; please ensure axis labels, task definitions, and the baselines shown are fully specified in the caption.","section":"Figure 4"}],"recommendation":"major_revision","confidential_remarks":"The paper is within scope for an LLM-reasoning venue and the core idea is interesting. The main obstacle is the GPT-4o/GPT-3.5-turbo asymmetry in the central comparison; this is fixable with matched-model experiments and failure-rate reporting, so I am recommending major revision rather than rejection. I would also check the baseline prompt strings in Appendix C.2 carefully, since the arithmetic errors there, if real, would unfairly weaken the baselines."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Hi,\n\nThe one thing to know: the LWT format is a genuine contribution, but the paper's headline accuracy claim is not backed by its own comparison. Table 3 gives kNoT GPT-4o-generated scripts while every baseline runs hand-written GPT-3.5-turbo prompts. That's two variables changing at once. Table 6 makes the problem concrete: put everything on GPT-4o and ToT and GoT also reach ~100% on most tasks. The 92%-vs-12% sorting numbers is therefore a between-condition gap, not a clean test of LWT.\n\nWhat's actually new: LWT itself. Numbered LLM instructions with input-field references and list indexing let the model write an executable network of elementary steps. That's a real step beyond ToT's fixed tree and GoT's split-then-merge, and the appendix has enough detail to reimplement. The ablation showing the LWT example and indexing are load-bearing is useful. The cost analysis shows kNoT is cheaper even when all schemes run on GPT-4o, so I'd accept 'lower cost and less per-task prompt text' even if the accuracy margins shrink.\n\nSoft spots:\n- Model asymmetry: the main issue. Fix with a matched-model comparison (GPT-4o-generated modules for baselines, or GPT-3.5-generated LWT scripts, or a full GPT-4o-execution table).\n- No error bars or significance tests; margins are large, so this is fixable but missing.\n- Script-generation failure rate is never measured. If GPT-4o occasionally produces malformed LWT, the reliability claim needs a number.\n- Minor: per-task context and LWT example are hand-authored, so 'automated' covers the plan/script, not the whole per-task setup. Character counts still favor kNoT, just less dramatically.\n\nThis is for anyone working on prompting schemes or LLM reasoning; the LWT idea could carry over to agent frameworks. It deserves a serious referee, but the revision should include a matched-model comparison and error bars. I'd accept with major revisions.\n\nRegards,","headline":"LWT is a genuinely useful new prompting format, but the paper's headline accuracy claims rest on a mismatched-model comparison and need a controlled rerun before they can be trusted.","tokens_in":20623,"tokens_out":3278,"would_cite":true,"duration_ms":52355,"reading_group":"yes","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"Large language models that script their own step-by-step reasoning networks outperform standard prompting schemes on six tasks while needing fewer hand-written prompts.","keywords":["knowledgeable network of thoughts","LLM workflow template","prompt engineering","reasoning networks","elementary operations","Chain-of-Thought","Tree of Thoughts","Graph of Thoughts"],"falsifier":"Run the first two kNoT steps on a few hundred fresh queries per task and check whether every produced LWT script is syntactically valid, contains no unresolvable {(N)} or {(N)}[M] references, and would yield the correct answer if every instruction were answered perfectly; a substantial fraction of failing scripts would refute the claim that kNoT's advantage is structural.","tokens_in":19517,"feed_emoji":"🧠","tokens_out":4589,"duration_ms":36842,"temperature":0.7,"pith_summary":"The paper claims that a large language model can plan its own reasoning and then execute that plan as a network of simple, individually verifiable steps. It introduces the LLM Workflow Template (LWT), a text format in which each node is one LLM call and each edge passes a previous output, optionally a single indexed element, into the next call. The claim is that this arrangement beats Chain-of-Thought, Tree of Thoughts, and Graph of Thoughts on six test tasks, with the largest gains at longer problem sizes, while cutting human-written task-specific prompts by up to 84.4 percent versus ToT and 87.3 percent versus GoT. If true, it would mean that prompting schemes can delegate both the decomposition and the execution of multi-step reasoning to the model itself rather than to human prompt designers.","feed_headline":"Self-written LLM scripts lift sorting accuracy to 92%","feed_subtitle":"A new prompt scheme has the model plan and execute elementary steps, beating ToT and GoT while needing fewer task-specific prompts.","key_machinery":"The LLM Workflow Template (LWT) is a script format where each numbered instruction is a single LLM call, input fields {(N)} pass an entire earlier output and {(N)}[M] pass one indexed item from it. This carries the argument by turning an LLM's plan into an executable sequence of elementary operations connected by explicit message passing, so no single inference must track the whole task and the reasoning structure can be any network rather than only a chain, tree, or split-then-merge graph.","core_discovery":"kNoT works in three steps: knowledge extraction, where the LLM produces a solution plan; LWT translation, where the plan is turned into a numbered script whose instructions reference earlier outputs through {(N)} and {(N)}[M]; and script execution, where each instruction is run by the LLM and its output is stored for later reference. By indexing individual list elements, each operation can be elementary, for example adding two digits or checking one set element against another. The paper reports 92 percent accuracy for sorting 32 numbers, versus 12 percent for ToT and 31 percent for GoT, and near-zero baseline accuracy at larger problem sizes on several tasks, while using fewer task-specific prompt characters.","pith_inferences":["If the planner and executor are different models, kNoT could be tested as a pipeline in which a strong model writes scripts that a weaker model executes, potentially separating planning ability from execution reliability.","The LWT indexing mechanism resembles a restricted programming language; one could extend it with conditionals or iteration, though the paper deliberately forbids loops to keep scripts sequential.","The approach may be most fragile exactly where the paper's gains are largest: as problem size grows, scripts get longer and the planner must keep indices consistent without any feedback, so an error-injection study that perturbs indices would reveal how much of the accuracy margin comes from planning versus execution.","The paper's framework for counting constant versus task-specific prompts in human labor could be applied to compare future prompt schemes on engineering effort rather than final accuracy alone."],"forward_implications":["On the six evaluated tasks, kNoT reaches accuracy levels that CoT, ToT, and GoT do not reach at the same problem sizes, especially with 32 to 128 elements.","Because LWT supports indexing, kNoT confines each LLM call to an elementary operation, which the paper argues is why accuracy holds on long sequences where whole-task prompting fails.","The scheme reduces task-specific prompt engineering: a single short LWT example per task type suffices, cutting task-specific prompt characters by up to 84.4 percent versus ToT and 87.3 percent versus GoT.","LLM API costs for high-accuracy runs are lower for kNoT than for ToT and GoT on the tested tasks.","LLMs generate instance-specific scripts, so the reasoning network is customized per query rather than fixed by a human-designed template."],"supporting_citations":[{"why":"Chain-of-Thought baseline that kNoT must beat and the origin of step-by-step prompting.","marker":"(Wei et al. 2022)"},{"why":"Tree of Thoughts baseline used for accuracy, cost, and prompt-labor comparisons, and the source of the branching search idea kNoT generalizes.","marker":"(Yao et al. 2024)"},{"why":"Graph of Thoughts baseline with split, process, and merge modules that kNoT compares against and whose predefined structure motivates LWT's arbitrary networks.","marker":"(Besta et al. 2024)"},{"why":"Self-Consistency baseline included in the evaluation as a CoT refinement.","marker":"(Wang et al. 2022)"},{"why":"Zero-Shot Chain-of-Thought baseline included in the evaluation as an early prompting method.","marker":"(Kojima et al. 2022)"},{"why":"Cited as evidence that CoT output deviates from the provided example, motivating the need for modularized and executable plans.","marker":"(Stechly, Valmeekam, and Kambhampati 2024)"},{"why":"Cited as evidence that prior modular approaches suffer cascading errors from losing earlier context, motivating LWT's message-passing and indexing.","marker":"(Bao et al. 2024)"}],"fun_headline_variants":["LLM writes own recipe for 92% sorting","LLM writes its own script to beat ToT/GoT","Self-scripting LLM nails 32-number sort at 92%","LLM's own plan hits 92% on sorting","Self-authored LLM plan beats ToT and GoT"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"Everything rests on the assumption that the model can reliably write down a correct, parseable plan script before execution begins; if that script is malformed, uses a wrong index, or omits a step, the whole pipeline fails regardless of how well individual steps run.","fun_headline_variants_meta":{"raw":{"variants":["LLM writes own recipe for 92% sorting","LLM writes its own script to beat ToT/GoT","Self-scripting LLM nails 32-number sort at 92%","LLM's own plan hits 92% on sorting","Self-authored LLM plan beats ToT and GoT"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.001227,"raw_usage":{"total_tokens":5021,"prompt_tokens":899,"completion_tokens":4122,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":515,"completion_tokens_details":{"reasoning_tokens":4036}},"tokens_in":515,"tokens_out":4122,"duration_ms":25297,"temperature":1.0,"reasoning_tokens":4036,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-11T10:29:29.056029+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Run the first two kNoT steps on a few hundred fresh queries per task and check whether every produced LWT script is syntactically valid, contains no unresolvable {(N)} or {(N)}[M] references, and would yield the correct answer if every instruction were answered perfectly; a substantial fraction of failing scripts would refute the claim that kNoT's advantage is structural.","supporting_citations":[{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Graph of Thoughts baseline with split, process, and merge modules that kNoT compares against and whose predefined structure motivates LWT's arbitrary networks."},{"cited_title":"V.; Chi, E","cited_arxiv_id":null,"evidence_quote":"Self-Consistency baseline included in the evaluation as a CoT refinement."}],"review_version":1}