{"id":"42333782-3e0a-46e1-9b5c-c3e3c4485ef6","arxiv_id":"2502.08904","paper_version":3,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":7,"one_line_summary":"MIH-TCCT reduces inconsistent hallucinations by cyclically training LLMs to translate event-based text into structured code and back, without task-specific fine-tuning.","lead":"The paper trains LLMs by converting event-based Wikipedia texts into Python-like code and back, aiming to transfer code's logical consistency to natural language. On summarization and QA benchmarks, the method lowers inconsistency-hallucination scores across three LLMs, though evaluation breadth is limited.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The quality filter in §3.4 cannot distinguish genuinely structured code from code that trivially embeds the original text, and the paper reports no manual/code audit; the claimed transfer of code logic to natural language is therefore unverified.","rationale":"I agree with the reader's weakest assumption; it is the most load-bearing point because it attacks the mechanism, not just the evaluation. The MIH-TCCT claim has two parts: (1) the training signal improves consistency, and (2) it does so by transferring code structure. Even if part (1) is true, part (2) requires the quality filter to select structurally faithful code. The filter is self-referential because the same model writes the code and the reorganized text, and ROUGE against the original text does not enforce structure. The paper's only code illustration is hand-crafted, not drawn from the actual pipeline, and no error bars or code/data release allow independent verification. Other concerns—no QA task accuracy, homogeneous-data ratio selected on test data, no statistical tests—are real but secondary; they affect the strength of the evidence, not the existence of the proposed mechanism. A code audit is the decisive test: if accepted code is mostly degenerate, the central 'logical rigor of code' narrative is unsupported and the observed improvements may be attributable to standard SFT on filtered self-generated data. If the audit finds clean, structured code, the conditional acceptance is appropriate. Therefore I keep the reader's CONDITIONAL verdict and recommend no change; if the audit later reveals pervasive degeneracy, the verdict should move toward REJECT.","tokens_in":15007,"tokens_out":6370,"duration_ms":67964,"concrete_test":"Release or reconstruct the accepted parallel-code corpus and audit it: sample 200 (text, code) pairs that pass the §3.4 threshold, execute each code, and automatically flag any code whose execution reproduces the source text via a verbatim string literal, a single print of the entire text, or attribute values set to whole original sentences. Have two annotators manually verify a 50-sample subset. If degenerate code exceeds 20% of accepted pairs, the ROUGE filter is rewarding re-encoding rather than logical structure; if it is below 5%, this specific objection is resolved and the mechanism is not contradicted by this concern.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The paper's central claim is that cyclic text-code training transfers the logical consistency of code to natural language, reducing inconsistent hallucinations. The load-bearing premise is that the accepted parallel code is genuinely structured and semantically faithful. In §3.3–3.4, the same model G generates the code c (Eq. 8), the reorganized text creorg (Eq. 10), and the final executed text creorg′, and the filter accepts a pair iff S(creorg′, t) > 0.85 (Eq. 11). Nothing in this loop prevents G from producing code that stores the original text verbatim—for example, `text = \"...\"; print(text)`, or attribute values that are full sentences—so that execution reproduces t with ROUGE ≈ 1. The hand-crafted example in Figure 4 and Appendix A.2 shows one valid style, but the paper reports no distributional statistics, manual inspection, or degenerate-code checks on the actual generated corpus. If a substantial fraction of accepted code is text-as-literal, Code2Text training teaches extraction/copying from code rather than logical structure, and the observed gains could come from SFT on filtered self-generated data, not from code-based logic. Additionally, the paper explicitly does not evaluate QA task performance (§4.1), so 'maintaining overall performance' is only checked for summarization. These gaps leave the transfer mechanism unverified.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes MIH-TCCT, a training framework that converts event-based natural language texts into Python code, uses a cyclic text-to-code and code-to-text training loop, and filters generated code by executing a reconstructed text and comparing it to the original with a ROUGE-based threshold. The method is evaluated on three instruction-tuned LLMs (Llama 3.1-8B, Ministral-8B, Qwen2.5-7B) on CNN/Daily Mail summarization and HaluEval question answering, reporting reductions in inconsistency-type hallucination metrics (UniEval consistency, AlignScore, Anah-v2). The central claim is that this task-agnostic procedure transfers the logical rigor of code to natural language, reducing inconsistent hallucinations while maintaining overall performance.","tokens_in":15290,"tokens_out":4402,"duration_ms":43493,"significance":"If the central mechanism is real, the paper offers a task-agnostic training recipe that improves logical consistency of LLM outputs without per-task adaptation, which would be a useful contribution to hallucination mitigation. The paper has concrete strengths: it evaluates across three different base models and two task families, compares against SFT and a symbolic-CoT baseline, reports ablations of the filter, quality assessment, and cyclic training, and uses external benchmarks rather than only self-generated data. The main empirical results in Table 1 are consistently positive for summarization, and the authors provide honest details about which metrics were not evaluated (e.g., QA task performance). However, the load-bearing premise—that the accepted code is genuinely structured and semantically faithful, rather than a trivial re-encoding of the text—is not verified, and the 'maintaining overall performance' claim is only supported for summarization. These gaps currently prevent the paper from substantiating its strongest conclusions.","major_comments":[{"comment":"The quality filter cannot distinguish genuinely structured code from code that embeds the original text as a literal string. In the loop defined by Eqs. (8)–(11), the same model G generates the code c, the reorganized text creorg, and the executed output creorg′; a degenerate solution such as `text = \"<original sentences>\"; print(text)` would pass the ROUGE threshold with a near-perfect score without requiring any event structure, class/instance decomposition, or logical operations. The paper provides no manual inspection, distributional statistics of generated code patterns, checks for string-literal embedding, or AST-level analysis of the training corpus. Without such evidence, the claimed transfer of 'logical rigor of code' is unverified: the improvements in Table 1 could arise from self-training on filtered self-generated data rather than from code-based structural consistency. The authors should add an analysis of the generated code (e.g., proportion of literal-string-only programs, AST depth, manual annotation of a random sample) or otherwise demonstrate that the filter rewards genuine structure.","section":"§3.4 (Eq. 11)"},{"comment":"The abstract claims the method 'maintain[s] overall performance,' but §4.1 explicitly states that in the QA task (HaluEval), 'the actual performance of the QA task has not been evaluated.' The only evidence for maintained task performance is the summarization results in Table 2. This is a mismatch between the claim and the evidence. Either add QA task metrics (e.g., exact match, F1, or answer accuracy) or revise the abstract and the corresponding statements in the introduction and conclusion to say that overall performance is maintained on summarization, and that QA is evaluated only for hallucination consistency.","section":"§4.1 / Abstract"},{"comment":"The empirical claims lack any measure of variability or statistical significance. Table 1 reports single values per configuration, as do the ablation figures (Figures 5–8). Some improvements are small (e.g., Llama-3 AlignScore: 87.15 vs. 86.51 for Prompt), and on some HaluEval metrics MIH-TCCT is worse than SFT (e.g., Qwen Anah-v2: 9.22 vs. 8.14; Ministral AlignScore: 97.50 vs. 98.61). Without error bars, confidence intervals, or significance tests, the reader cannot determine whether the reported gains are reliable or within run-to-run noise. Reporting standard deviations over multiple runs (or at least bootstrap confidence intervals) is necessary to support the stated 'significant' reductions.","section":"Table 1 and §4.2"},{"comment":"The description of the cyclic training procedure is ambiguous in an important way. Section 3.3 presents text-to-code (Eq. 8) and code-to-text (Eq. 9) as the training loop, Section 3.4 describes a quality filter, and the ablation in §4.3 refers to 'cyclic training discussed in Section 3.5,' which does not exist (the last numbered subsection is 3.4). Moreover, §3.4 states that 'after each round of generation, all text outputs predict all corresponding code (including those discarded in the previous round),' which seems to contradict the idea that the filter discards low-quality data. The authors should clarify whether the model is trained on accepted pairs only, on all generated pairs, or on a combination, and fix the section reference. Without this clarification, the contribution of the filter to the training signal is unclear.","section":"§3.3–§3.5"}],"minor_comments":[{"comment":"The heading 'Text-Code Cylic Training' contains a typo; it should be 'Cyclic.'","section":"Section title"},{"comment":"The sentence 'As illustrated in Figure 2' appears to reference the wrong figure; the trend of similarity scores and qualifying-data proportion is shown in Figure 8, not Figure 2.","section":"§4.4.2"},{"comment":"The Impact Statement says the datasets and code are 'built based on public medical datasets,' but the experiments use wiki-40b-en, CNN/Daily Mail, and HaluEval. This appears to be a copy-paste error and should be corrected.","section":"Impact Statement"},{"comment":"The prompt examples contain invalid Python (e.g., `def init (self, first name, last name, title=\"\")` with spaces in parameter names, and `mitra ghosh publishers` as a variable name). If these examples are representative of the prompts actually used, the generated 'code' may not be executable, which would break the execution step in §3.4. Please provide the exact, executable prompts or clarify that the appendix is a simplified illustration.","section":"Appendix A.2/A.3"},{"comment":"The table caption states that the consistency metric serves both as a hallucination measure and a summarization metric; consider presenting these separately or explaining the dual role more clearly.","section":"§4.4.1 / Table 2"},{"comment":"The reference 'Program of thoughts prompting' is cited twice (as 'Chen et al.' with and without a year) and the 'Ministral-8B-Instruct' model name is not matched to a reference (Mistral 7B is cited instead); please correct these citation and naming inconsistencies.","section":"References"}],"recommendation":"major_revision","confidential_remarks":"The paper addresses a timely problem and the core idea is interesting, but the lack of verification of the code-generation quality and the overclaim about QA performance are substantive. The authors should be asked to provide an analysis of the generated code corpus and to either add QA performance metrics or soften the claims. The writing quality also needs a careful pass."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"You should know this paper for one thing: it proposes a genuinely new training loop that turns event-based text into code and back, and it shows consistent hallucination-reduction gains across three LLMs and two tasks. That is real. The improvements over base models are uniform, the ablations show each component matters, and Figure 8's rising acceptance rate at least demonstrates the loop is doing something. Credit is due for the idea and for the breadth of base models tried.\n\nThat said, the core claim—that the logical rigor of code transfers to natural language—is not actually verified. The quality filter in §3.4 cannot tell genuinely structured code from code that just embeds the original text as string literals, and the paper offers no manual inspection, no degenerate-code statistics, and no distributional evidence that the accepted parallel code is legitimately structured. Since the same model generates the code, the reorganized text, and the executed text, the whole filter could be passing trivial re-encodings with ROUGE near 1. If that happens, the gains could come simply from self-training on filtered self-generated data, not from any code-specific logic. The stress-test note lands.\n\nThere are also standard-but-important weaknesses. No error bars or significance tests, so the consistent margins could be noise. The claim of \"maintaining overall performance\" is only checked for summarization; HaluEval QA performance is explicitly not evaluated. The homogeneous-data mixing ratio (0.4) is chosen after looking at test-set results, which biases the AdvTrain numbers. And the Impact Statement about medical datasets and public release has nothing to do with this paper—that kind of copy-paste residue makes you trust the rest of the manuscript less.\n\nWho should read it: researchers working on hallucination mitigation, synthetic data, or cross-modal transfer between text and code. It is a plausible direction and deserves referee time, but a serious referee should demand the code audit, error bars, and a cleaner evaluation before this becomes a citation. I would not put it in my own bibliography until the code and data are actually released and the degenerate-code concern is addressed.\n\nRecommendation: send it to peer review, but with a clear expectation of major revision. The idea is worth engaging with; the current evidence does not yet carry the load.","headline":"New cyclic text-code training reduces hallucinations consistently, but the code-quality filter is self-referential and the evaluation lacks the rigor needed to confirm the transfer-of-logic story.","tokens_in":15842,"tokens_out":1324,"would_cite":false,"duration_ms":16177,"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":"This paper claims that cyclically translating event-based text into structured code and back transfers code's logical consistency to natural language, reducing inconsistent hallucinations across three LLMs and two tasks without…","keywords":["inconsistent hallucination","event-based text","text-code cyclic training","logical consistency","hallucination mitigation","code generation","LLM fine-tuning","quality filtering"],"falsifier":"Inspect the code generated by MIH-TCCT for degenerate patterns: if a large share of samples that pass the quality threshold embed the original text verbatim in a string, comment, or print call, then passing ROUGE does not require code-based structure. A controlled comparison replacing the code step with a random-but-faithful reordering of the original text should then show the same consistency gains if the gains come from the filter and cycle rather than from code structure.","tokens_in":14794,"feed_emoji":"🧩","tokens_out":6181,"duration_ms":55759,"temperature":0.7,"pith_summary":"The paper sets out to show that a language model's tendency to contradict its input or its own earlier statements can be reduced by training it to translate event-based text into structured code and then back into text. The proposed framework, MIH-TCCT, filters general wiki-style text for event descriptions, generates parallel executable Python code with classes, objects, and functions, and runs several rounds of text-to-code and code-to-text training. The authors claim this cyclic alignment transfers the logical consistency of code into natural language, and their experiments report reduced inconsistent hallucinations on summarization and question answering across three instruction-tuned LLMs while preserving task performance. The point of the claim is generality: unlike task-specific synthetic-data methods, the training uses only generic event text and carries over to downstream tasks without adaptation.","feed_headline":"Turning events into code curbs LLM hallucinations","feed_subtitle":"Cyclic text-code training cuts inconsistent outputs across three LLMs and two tasks without task-specific adaptation.","key_machinery":"The central mechanism is text-code cyclic training with an executable-quality filter. Event-based text is converted to structured Python code; the code is then converted back to text; and each generated code sample is judged by executing it, constructing a reorganized text from the object attributes, and comparing it to the original text with a ROUGE-based similarity score against a threshold of 0.85. Only code that passes the threshold feeds the next round, and the cycle repeats for three iterations, pushing the model to align the semantic styles of the two languages. The load-bearing idea is that code's strict structure forces the model to keep entities, attributes, and relations stable, and that this stability carries over to text generation.","core_discovery":"MIH-TCCT claims that a structural correspondence between event-driven text and code (participants as class instances, their attributes as variables, causes and processes as functions) lets an LLM learn logical consistency in natural language by cyclically predicting code from text and text from code. In the reported experiments, this cyclic training reduces both input-conflicting and context-conflicting hallucinations across three LLMs and two task families, with consistency gains over base, prompting, task-specific SFT, and symbolic-CoT baselines, and without sacrificing overall summarization quality. The authors further claim the framework is task-agnostic because the training corpus is generic event-based text rather than task-specific synthetic data.","pith_inferences":["If the mechanism is real, a similar cycle could be run with other structured intermediate representations such as tables, knowledge graphs, or formal logic, and might transfer consistency in the same task-agnostic way.","The framework predicts that the consistency gains should shrink or vanish if the code step is replaced by a non-structured encoding that still passes the ROUGE filter; a control experiment with shuffled but faithful reorderings would test this directly.","Because the quality filter is scored by the same LLM family, part of the observed gain may reflect learning to satisfy the filter rather than genuine logical consistency; a human-annotated audit of generated code quality would separate these effects.","The reported homogeneous-data trend suggests a practical scaling rule: only a modest amount of in-domain text-code parallelism is needed to obtain most of the consistency benefit, after which additional data mostly trades summarization quality for hallucination reduction."],"forward_implications":["MIH-TCCT reduces inconsistent hallucinations on CNN/Daily Mail summarization and HaluEval question answering across three instruction-tuned LLMs without requiring downstream task adaptation.","Both the quality filter and the cyclic training are necessary: ablations show that removing cyclic training causes the largest average drop of 1.93% in consistency metrics.","The framework preserves overall summarization quality on coherence, fluency, and relevance, whereas task-specific SFT in the paper shows noticeable declines on relevance and coherence.","Adding a small proportion of homogeneous data further improves consistency metrics, with diminishing returns and a slight cost to summarization performance beyond roughly 40% mixing.","The iterative generation trend stabilizes by the third round, which the paper uses to justify three training cycles."],"supporting_citations":[{"why":"Supplies the wiki-40b-en corpus from which event-based training text is filtered.","marker":"Guo et al., 2020"},{"why":"Provides the CNN/Daily Mail benchmark used to evaluate summarization consistency.","marker":"Chen et al., 2016"},{"why":"Provides the HaluEval benchmark used to evaluate question-answering hallucinations.","marker":"Li et al., 2023"},{"why":"Supplies the AlignScore evaluation metric used for both summarization and QA consistency.","marker":"Zha et al., 2023"},{"why":"Supplies the UniEval consistency metric used for the summarization task.","marker":"Zhong et al., 2022"},{"why":"The SymbCOT method is the main task-agnostic baseline that MIH-TCCT is compared against.","marker":"Xu et al., 2024"},{"why":"CodeRL is one of the code-training results the paper cites as motivation for code-to-language transfer.","marker":"Le et al., 2022"},{"why":"CoCoGen motivates the claim that training on code improves consistency in code-related tasks.","marker":"Madaan et al., 2022"}],"fun_headline_variants":["Code-cycle training slashes LLM hallucination","Turning text into code cuts AI confusion","Event-based code training reduces LLM lies","Cyclic code-text training tames LLM hallucinations","Code loop training curbs inconsistent LLM outputs"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The pipeline assumes the generated parallel code is genuinely structured and semantically faithful, so the quality filter's ROUGE comparison rewards logical structure rather than a trivial re-encoding such as the model pasting the original text into a string literal or comment.","fun_headline_variants_meta":{"raw":{"variants":["Code-cycle training slashes LLM hallucination","Turning text into code cuts AI confusion","Event-based code training reduces LLM lies","Cyclic code-text training tames LLM hallucinations","Code loop training curbs inconsistent LLM outputs"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000127,"raw_usage":{"total_tokens":1036,"prompt_tokens":787,"completion_tokens":249,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":403,"completion_tokens_details":{"reasoning_tokens":180}},"tokens_in":403,"tokens_out":249,"duration_ms":2895,"temperature":1.0,"reasoning_tokens":180,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-07T23:15:43.660580+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Inspect the code generated by MIH-TCCT for degenerate patterns: if a large share of samples that pass the quality threshold embed the original text verbatim in a string, comment, or print call, then passing ROUGE does not require code-based structure. A controlled comparison replacing the code step with a random-but-faithful reordering of the original text should then show the same consistency gains if the gains come from the filter and cycle rather than from code structure.","supporting_citations":[],"review_version":1}