{"id":"12cd1345-e02c-4d49-a179-ea3c68f89c23","arxiv_id":"2412.03987","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":4.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":4,"one_line_summary":"A multi-mode thought-tree prompting method that claims to improve LLM accuracy on GPQA, TruthfulQA, and GSM8K, but with weak statistical support.","lead":"MTMT is a prompting method that makes an LLM build a tree of sub-questions by alternating between several thinking modes (decompose, compare, associate, etc.), expanding nodes when the model's perplexity is high. On three benchmarks with GPT-4o mini it reports small accuracy gains over direct and chain-of-thought prompting, but the gains are not statistically verified and some comparisons are inconsistent.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The claimed gains rest on an unvalidated perplexity-to-correctness link, and the paper's own numbers are internally inconsistent about which dataset gained 5.2% vs. 3.1%.","rationale":"The reader's weakest_assumption correctly identifies the unvalidated perplexity-to-correctness link as the load-bearing assumption. I agree with that identification. The paper proposes a plausible framework, but the empirical evidence is not yet sufficient: no significance tests, no code, post-hoc parameter selection, and a clear data inconsistency in the reported improvements. A conditional verdict is appropriate: the authors should release code and error bars, and run the random-expansion control to demonstrate that the perplexity signal, rather than extra compute, drives the gains.","tokens_in":11604,"tokens_out":1403,"duration_ms":13531,"concrete_test":"Run a GPQA ablation with identical thinking-mode prompts and compute budget, but replace perplexity-based expansion with random selection of which questions to expand (matching the distribution of node counts). If random expansion yields the same accuracy as MTMT, the perplexity signal is not load-bearing for the reported 5.2% improvement. Separately, verify the Table 1 numbers and re-read Section 4.4 to confirm which dataset receives the 5.2% vs. 3.1% improvement.","verdict_should_be":"CONDITIONAL","load_bearing_attack":"The central mechanism is the perplexity threshold (Eq. 6, Eq. 7; Section 3.3): nodes are expanded, regenerated, and deactivated based on whether response perplexity exceeds P P T0 + αD(i). The paper never shows that perplexity correlates with answer correctness, yet every expansion and stop decision depends on it. If perplexity does not track correctness, the tree growth is essentially arbitrary, and the reported gains could come simply from spending more tokens/prompts on harder questions rather than from the \"confidence\" signal. The threshold P P T0 and α are tuned per dataset (Figures 3–5), which further weakens the causal story. There is also an internal inconsistency in Section 4.4: the text says \"5.2% and 3.1% improvement on the TruthfulQA and GPQA datasets, respectively,\" but the table shows GPQA gains 5.2% (38.8→44.0) and TruthfulQA gains 3.1% (55.4→58.5). This swap makes the headline quantitative claim ambiguous and reinforces the need for a controlled test of the perplexity mechanism.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes MTMT, a multi-thinking-mode tree search method for improving zero-shot LLM reasoning. Starting from a root question, the method repeatedly asks the LLM to answer sub-questions generated by several 'thinking modes' (decompose, association, compare, importance, inference), stores the extracted information in a graph, and uses the perplexity of responses to decide when to expand, regenerate, or deactivate nodes. The final root answer is produced after the perplexity condition is met. Experiments with GPT-4o mini on GPQA, TruthfulQA, and GSM8K report accuracy gains over direct answering and CoT baselines (e.g., 38.8% to 44.0% on GPQA), plus parameter sweeps and ablation studies.","tokens_in":11936,"tokens_out":7656,"duration_ms":62032,"significance":"If the claimed effects hold, MTMT would be a useful addition to the toolbox of zero-shot reasoning methods for LLMs, with the distinctive feature of using multiple heterogeneous reasoning prompts and a graph structure. Strengths of the paper include a concrete algorithm description with prompt templates in the appendix, a systematic parameter sweep, and mode-level ablations. However, the reported gains are modest and not supported by statistical tests, and the central confidence mechanism (perplexity-based expansion) is not validated; the current evidence is therefore suggestive rather than conclusive.","major_comments":[{"comment":"The sentence 'our method shows a 5.2% and 3.1% improvement on the TruthfulQA and GPQA datasets, respectively' reverses the dataset labels; Table 1 and Section 1 show GPQA 44.0% (from 38.8%, a 5.2-point gain) and TruthfulQA 58.5% (from 55.4%, a 3.1-point gain). This makes the headline quantitative claim ambiguous and needs to be corrected.","section":"Section 4.4, Table 1"},{"comment":"The entire node expansion, regeneration, and deactivation policy is driven by the assumption that response perplexity indicates whether a sub-question has been correctly resolved. The paper provides no evidence for this correlation on the datasets used, and it does not compare against a control that spends the same node budget with a random or fixed expansion rule. Without such a control, the reported gains may be due to additional computation rather than to the perplexity signal. I ask the authors to either validate the perplexity-correctness relationship or include a compute-matched baseline.","section":"Section 3.3, Eqs. (6)-(7)"},{"comment":"The paper never states how the token probabilities P(t_i | t_1...t_{i-1}) are obtained for the GPT-4o mini API. If the API is used in the standard chat-completion mode, per-token log-probabilities are not returned; if a local model or a logprobs parameter is used, that must be described. As written, the central mechanism is not reproducible.","section":"Section 3.3, Eq. (6)"},{"comment":"The threshold PPT0 and scaling coefficient α are swept on the same test sets used for the headline results, and the ablation experiments use different parameter values on the two datasets (PPT0=1.25, α=0.1 for GPQA; PPT0=1.45, α=0.05 for TruthfulQA). The paper claims a general strategy that works 'without the need to adjust prompts for different datasets,' but it in fact selects control parameters per dataset. This is a form of test-set fitting that likely overstates the gains; a validation split or a single fixed hyperparameter setting should be used.","section":"Section 4.4, Figures 3-5; Section 4.5"},{"comment":"No error bars, confidence intervals, or significance tests are reported, and no repeated runs with different seeds are described. Given the stochasticity from random thinking-mode selection and the modest size of the gains (5.2 and 3.1 percentage points), the results could be within sampling noise; the relevant standard errors for binomial accuracies at n=448 and n=817 are roughly 2.3 and 1.7 percentage points.","section":"Section 4.4, Table 1"},{"comment":"The stopping criterion and final-answer selection are not specified. Section 3.1 says that once 'certain criteria' are met the answer Ak becomes final, but Section 3.3 does not state those criteria, nor how the final root answer is selected when multiple candidate answers have been generated at different iterations. This makes the algorithm underspecified.","section":"Section 3.1, Eq. (5), Section 3.3"}],"minor_comments":[{"comment":"The text says TruthfulQA 'contains 654 test instances' but then says 'We use a total of 817 multiple-choice questions from the dataset.' The standard TruthfulQA benchmark has 817 questions; please clarify whether 654 is a subset or a typo.","section":"Section 4.2"},{"comment":"The formula for AP sums over all data points j, which does not give an average depth as the figure axes and the text ('AP as the depth of the graph') suggest; it should be divided by n or renamed as a total.","section":"Eq. (8)"},{"comment":"The prompt for 'Choose_answer' contains a typo, 'diferences', and inconsistent spacing around 'answer1' and 'answer2'; also 'The diferences' should be 'The differences'.","section":"Table 4"},{"comment":"The reference 'R OpenAI et al. 2023' is an awkward author string; it should be formatted as 'OpenAI'.","section":"References"},{"comment":"The statement 'Other thinking modes show similar effects, causing accuracy drops ranging from 2.7% to 0.6%' mixes the GPQA and TruthfulQA tables; on GPQA the drops are 1.4-2.7%, while 0.6% appears only in the TruthfulQA table.","section":"Section 4.5"},{"comment":"The paper says a thinking mode is 'randomly chosen' when not assigned, but it does not specify the random seed or distribution, which affects reproducibility even at temperature 0.","section":"Section 3.3.1"}],"recommendation":"major_revision","confidential_remarks":"The paper is a prompt-engineering contribution whose core mechanism is not validated. The reported gains are small and the parameter selection is per-dataset, so the top-line claims are not yet convincing. I would like the authors to add the missing control experiments and statistical analysis. The presentation of results also needs to be cleaned up. With these changes, the paper could become acceptable; in its current form it is a borderline submission for a journal."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Dear colleague,\n\nQuick take on arXiv:2412.03987. The genuinely new thing here is that the authors wire several thinking modes—decompose, associate, compare, importance, inference—into a tree and use perplexity as the control signal for expanding, regenerating, and deactivating nodes. I haven't seen that exact configuration in ToT, GoT, or self-consistency work, and the system is described carefully enough that a motivated reader could reimplement it. The ablations, parameter sweeps, and the honest limitations paragraph are also real work; they don't hide the cost of the method or the fact that GSM8K saturates.\n\nThe soft spots are the empirical ones. The whole mechanism rests on perplexity being a reliable proxy for answer correctness, and the paper never tests that correlation. The threshold PPT0 and the depth scaling α are tuned per dataset, and from the figures it's clear that the chosen values are the good ones. No error bars or significance tests anywhere; the headline gains of 5.2% and 3.1% are plausible but could easily be noise. There's also an internal inconsistency: Section 4.4 swaps the datasets, attributing 5.2% to TruthfulQA and 3.1% to GPQA, while the table shows the opposite. And the TruthfulQA wording says 654 test instances but the method is run on 817 multiple-choice questions; that needs a footnote. Missing baselines—ToT, GoT, self-consistency—matter because MTMT is billed as a general System 2 method, not just a new prompt. No code, no seeds, no per-question breakdowns.\n\nI wouldn't call this fatally flawed. The combination is plausible and the writing is honest about limitations. But the central perplexity-correlation claim is unproven, and the current evidence doesn't separate 'better reasoning' from 'more tokens spent on hard questions.' A serious referee should ask for a controlled test of the perplexity signal, error bars, the missing baselines, and code.\n\nFor a reading group: maybe—the idea is worth discussing, but the empirical claims shouldn't be taken at face value. I'd accept it for peer review, but only with the expectation of a major revision.\n\nCheers.","headline":"Perplexity-guided thought-tree is a plausible combination, but the paper doesn't validate its central control signal, and the reported gains look fragile.","tokens_in":12390,"tokens_out":2107,"would_cite":false,"duration_ms":19264,"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":"By consolidating five thinking modes into a perplexity-gated thought tree, MTMT raises GPT-4o mini's accuracy on GPQA from 38.8% to 44.0% and on TruthfulQA from 55.4% to 58.5%, without external knowledge or per-dataset prompt engineering.","keywords":["MTMT","multi-thinking modes","thought tree","perplexity-gated expansion","System 2 reasoning","zero-shot reasoning","GPQA benchmark"],"falsifier":"Take a labeled subset of GPQA, compute the perplexity of each node's sub-answer, and check its correlation with whether that sub-answer is correct; a near-zero or negative correlation would falsify the load-bearing assumption. The complementary experiment holds node count fixed and replaces perplexity-based expansion with random expansion; if accuracy stays the same, the tree's expansion signal is not what produces the gain.","tokens_in":11372,"feed_emoji":"🌳","tokens_out":7015,"duration_ms":60175,"temperature":0.7,"pith_summary":"MTMT is a zero-shot method that tries to make a large language model reason the way a careful person would: instead of answering a hard question in one pass, the model is repeatedly prompted to decompose the task, associate related knowledge, compare options, judge importance, and run counterfactual inferences. Each prompted exchange becomes a node in a thought tree, and nodes whose responses show high model uncertainty, measured by perplexity, are expanded further; once the root question's uncertainty drops below a threshold, the accumulated answer is returned. The paper reports that this raises GPT-4o mini accuracy from 38.8% to 44.0% on the graduate-level GPQA benchmark and from 55.4% to 58.5% on TruthfulQA, beating zero-shot Chain of Thought and few-shot baselines. On GSM8K the method barely changes accuracy, which the paper interprets as a sign that the model is already confident on simpler middle-school math. If the result holds, it offers a general, prompt-agnostic way to extract latent knowledge from an LLM for complex reasoning tasks.","feed_headline":"Thought tree lifts GPT-4o mini on GPQA by 5.2 points","feed_subtitle":"MTMT's multi-mode tree also beats zero-shot chain-of-thought on TruthfulQA, with no per-dataset prompts.","key_machinery":"The load-bearing object is the thought tree (the MTMT graph), whose nodes are sub-questions generated by five named thinking modes: decompose, association, compare, importance, and inference. Expansion is gated by the model's perplexity on its own response: a node whose perplexity exceeds a threshold $PPT_i = PPT_0 + \\alpha D(i)$ (where $D(i)$ is the node's depth) is treated as confused and receives further thinking-mode prompts. After child nodes contribute relevant information, the parent is regenerated, and a compare-mode prompt selects the better answer; irrelevant nodes are deactivated. The same threshold controls stopping, so the graph's depth, node count, and accuracy-cost balance are governed by $PPT_0$ and $\\alpha$.","core_discovery":"The central discovery MTMT claims is that consolidating multiple human-like thinking modes into a single tree structure strengthens an LLM's reasoning more than any single mode on its own. The method formalizes the model's direct answer as System 1 and the tree search as System 2: the graph repeatedly queries the model with prompts drawn from decomposition, association, comparison, importance, and inference, extracts structured information from each response, and decides whether to expand, regenerate, or deactivate nodes based on the response's perplexity. Iterating this loop lets the model revisit its root answer with new information generated from sub-questions. The reported outcome is that this process yields 44.0% on GPQA, 58.5% on TruthfulQA, and 93.9% on GSM8K, with decomposition and association identified by ablation as the most consequential modes.","pith_inferences":["A sharper test of the mechanism would compare perplexity-gated expansion to random expansion at the same node budget; if accuracy does not fall, the tree's structure and regeneration, not the uncertainty signal, would be the cause of the gains.","Because $PPT_0$ is set separately for GPQA (1.25) and TruthfulQA (1.45), a natural extension is to evaluate MTMT on a third benchmark using either dataset's tuned threshold, which would reveal whether the method transfers without parameter retuning.","The same graph could host a retrieval node type: instead of generating an association from memory, the model would retrieve external facts, turning MTMT into a structured RAG controller while keeping the perplexity-based stopping rule.","The explicit perplexity-to-node-count dial also gives a practical way to budget inference cost in agentic settings, trading a few accuracy points for a target number of API calls."],"forward_implications":["On GPQA, MTMT reaches 44.0% without external knowledge, a 5.2 percentage-point gain over direct GPT-4o mini and higher than the CoT and few-shot baselines tested.","On TruthfulQA, MTMT reaches 58.5%, a 3.1-point gain over direct responses, with no per-dataset prompt rewriting.","On GSM8K, accuracy is roughly unchanged at 93.9%, consistent with the claim that the method helps most when the base model is not already confident.","Ablation shows decomposition is the most important mode (removing it costs 4.7 points on GPQA and 2.1 on TruthfulQA), with association also contributing 4.5 points on GPQA.","Higher temperature hurts accuracy, so the paper sets temperature to 0; accuracy falls as the perplexity threshold rises on GPQA, meaning fewer nodes means less correction."],"supporting_citations":[{"why":"Defines Chain-of-Thought prompting, the key baseline and the single-mode approach MTMT generalizes.","marker":"Wei et al., 2022"},{"why":"Introduces tree-of-thought search over LLM-generated thoughts, the structural precedent for MTMT's graph.","marker":"Yao et al., 2023a"},{"why":"Provides the zero-shot 'Let's think step by step' baseline that MTMT is compared against.","marker":"Kojima et al., 2022"},{"why":"Defines the GPQA graduate-level QA benchmark where MTMT reports its largest gain.","marker":"Rein et al., 2023"},{"why":"Defines TruthfulQA, the second benchmark where MTMT claims a 3.1-point improvement.","marker":"Lin et al., 2021"},{"why":"Defines GSM8K, the math benchmark where MTMT shows negligible change.","marker":"Patel et al., 2021"},{"why":"Identifies GPT-4o mini, the base model queried by MTMT and all baselines.","marker":"OpenAI, 2024"}],"fun_headline_variants":["Multi-mode thought tree boosts LLM reasoning on three benchmarks","Tree of thinking modes lifts GPT-4o mini on GPQA and TruthfulQA","MTMT: one tree, many thinking modes, stronger LLM answers","Consolidated thought tree beats single-mode prompting on three tasks","Multi-thinking modes tree improves GPT-4o mini on GPQA, GSM8K"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The load-bearing premise is that the model's uncertainty score on a sub-answer is a reliable signal that the sub-answer is wrong or incomplete, so expanding the most uncertain nodes will improve the final answer; if that correlation is weak, the tree's expansion and stopping rules lose their claimed benefit.","fun_headline_variants_meta":{"raw":{"variants":["Multi-mode thought tree boosts LLM reasoning on three benchmarks","Tree of thinking modes lifts GPT-4o mini on GPQA and TruthfulQA","MTMT: one tree, many thinking modes, stronger LLM answers","Consolidated thought tree beats single-mode prompting on three tasks","Multi-thinking modes tree improves GPT-4o mini on GPQA, GSM8K"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000323,"raw_usage":{"total_tokens":1787,"prompt_tokens":889,"completion_tokens":898,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":505,"completion_tokens_details":{"reasoning_tokens":801}},"tokens_in":505,"tokens_out":898,"duration_ms":7001,"temperature":1.0,"reasoning_tokens":801,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-11T21:51:34.927234+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Take a labeled subset of GPQA, compute the perplexity of each node's sub-answer, and check its correlation with whether that sub-answer is correct; a near-zero or negative correlation would falsify the load-bearing assumption. The complementary experiment holds node count fixed and replaces perplexity-based expansion with random expansion; if accuracy stays the same, the tree's expansion signal is not what produces the gain.","supporting_citations":[{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Identifies GPT-4o mini, the base model queried by MTMT and all baselines."}],"review_version":1}