{"id":"9f64e741-52bc-402a-9f99-9de5aa742ac0","arxiv_id":"2608.08447","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":3,"one_line_summary":"Reasoning models often stop using the requested language as problems get harder, and this language breakdown can make accuracy look better than it is.","lead":"This paper measures whether multilingual reasoning models think and answer in the language they were asked to use, not just whether the final answer is correct. It finds that as problems get harder, many models quietly switch to a dominant language, which can even make accuracy look better at harder levels.","discovery_kind":"extension","skeptic_critique":{"model":"deepseek-v4-flash","headline":"TC/AC are computed from hard whole-sequence language labels; the reported 'breakdown' and four-case taxonomy may be a threshold artifact of gradual language mixing rather than an abrupt model behavior.","rationale":"The reader's weakest assumption was that PolyMath's official language-detection scripts are unvalidated and may be biased for Bengali, Swahili, or Telugu. I agree that detector validity is the load-bearing issue, but I would sharpen it: the specific risk is not merely per-language bias but the use of a hard whole-sequence label, which can convert a gradual increase in English tokens into an abrupt binary flip. This directly threatens the headline 'language consistency breakdown effect' and the Case 3 versus Case 4 taxonomy, not just the numerical values of TC/AC. A second, related artifact is the classification of boxed numeric answers, which could depress AC for all non-English languages uniformly. The accuracy-increase claim in Section 4.3 depends on the same binary TC/AC measurements, so it inherits the same vulnerability. This is a measurement-validity concern, not a disagreement with external consensus, and it is addressable without new model runs if the raw generations are retained. Since the paper releases no code or data, the check would require the authors to share or rerun their pipeline. The core descriptive message—that accuracy alone hides language-consistency behavior—remains plausible even if the breakdown is partly a threshold artifact, so the reader's CONDITIONAL verdict is appropriate; the concern does not move the verdict, but it does sharpen the conditions under which the paper should be accepted.","tokens_in":17962,"tokens_out":5021,"duration_ms":59583,"concrete_test":"Recompute TC and AC from the stored generations using token-level language signals instead of whole-sequence hard labels: run a per-sentence or per-word language identifier on each thinking trace and answer, and compute the mean proportion of target-language tokens at each difficulty level for DeepSeek-R1-Distill-Qwen-7B on Bengali, Swahili, and Telugu, with English and Chinese as controls. If the target-language token proportion declines gradually while the binary TC/AC scores show a sharp cliff between the same difficulty levels, the breakdown effect is a measurement artifact. Separately, tabulate how many final answers are purely numeric and how the official detector labels them; recompute AC excluding numeric answers to check whether the AC breakdown survives.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The paper's central findings—the four-case taxonomy, the language consistency breakdown effect, and the non-monotonic accuracy claim in Section 4.3—are all defined through binary indicators in Equations 1 and 2, where a whole thinking trace or final answer is labeled as either matching or not matching the input language. The official PolyMath detection scripts are used without validation on these models and languages, and the paper does not report language-detection confidence or token-level language proportions. This matters because a hard label on a long mixed-language trace is a thresholded function of the underlying language mix: as the model gradually inserts more English tokens, the whole-trace label can flip abruptly at a critical ratio, manufacturing a 'breakdown' even when the model's language mixing changes smoothly. The striking numbers cited for Bengali (TC 97.9% to 1.6%) and the Case 3 vs Case 4 distinction in Figure 2 may therefore reflect detector thresholding rather than a genuine sudden behavioral collapse. A related artifact threatens AC: final answers are often boxed numeric expressions, and if the detector labels numeric strings as English, AC for non-English languages is systematically depressed. Until these are ruled out, the abrupt-breakdown and accuracy-increase claims are not established as properties of the model rather than of the measurement.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper studies whether multilingual reasoning models preserve the requested language in their thinking traces and final answers as task difficulty increases. Using the PolyMath benchmark in eight languages and four difficulty levels, with five reasoning and three non-reasoning models under forced-target-language prompting, it defines thinking-language consistency (TC) and answer-language consistency (AC) as binary whole-sequence indicators. The authors report four difficulty-dependent consistency cases, a 'language consistency breakdown effect' defined by a 30-point drop between consecutive difficulty levels, and a claim that this breakdown can break the expected monotonic accuracy–difficulty relationship. They additionally compare W4A16 quantization methods (GPTQ, AWQ, AutoRound) for their effects on accuracy and TC, concluding that multilingual evaluation should jointly track accuracy, TC, AC, and difficulty.","tokens_in":18173,"tokens_out":3462,"duration_ms":38417,"significance":"If established, the core claim is practically important: accuracy-only evaluation of multilingual reasoning models would be incomplete, and language consistency would need to be tracked jointly with difficulty. The paper covers a broad set of models and languages, uses three seeds, and includes useful sensitivity analyses for the breakdown threshold and the tolerance parameter. The central finding, however, rests on a measurement instrument that is not validated, on comparisons across disjoint problem sets, and on point estimates without uncertainty quantification. The descriptive patterns in Figures 3 and 4 are visible and plausible, but the paper's stronger inferences—the breakdown effect and its causal role in non-monotonic accuracy—require additional evidence. The claimed contribution is therefore conditional on the validity of the language detector and the comparability of difficulty levels.","major_comments":[{"comment":"TC and AC are computed exclusively from whole-sequence hard language labels produced by the official PolyMath detection scripts, but the paper provides no validation of these detectors on these eight models or languages. A long mixed-language thinking trace can receive a hard label that flips abruptly at a critical ratio of language tokens, so the 'breakdown' in Case 4 and the numbers in Section 4.2 (e.g., Bengali TC dropping from 97.9% to 1.6%) may be a threshold artifact of gradual code-mixing rather than a sudden behavioral change. The authors should report detector confidence or token-level language proportions, validate the detector per language (especially Bengali, Swahili, and Telugu), and show that the breakdown persists under token-level or soft-label consistency metrics. This validation is load-bearing because Eqs. (1)–(2) are the basis for all four findings.","section":"§3, Eqs. (1)–(2); §4.2, Eqs. (3)–(4)"},{"comment":"The claim that language-consistency breakdown causes accuracy to 'preserve or improve' at a harder difficulty level compares accuracy on disjoint problem sets: low, medium, high, and top are different PolyMath items. The reported increases (Bengali 47.5% to 50.9%, Swahili 4.5% to 35.7%, Telugu 22.9% to 41.9%) could reflect difficulty calibration or item selection rather than a language-driven shift. To support the causal statement, the paper needs a control that holds problem content fixed while varying difficulty, or an item-level analysis showing that accuracy gains concentrate in generations where the language switched. Without this, the monotonicity-breaking claim in Section 4.3 is not established.","section":"§4.3, Figure 3"},{"comment":"All quantitative claims are point estimates without confidence intervals or significance tests, despite the statement that three seeds are used. The quantization ranking in Table 2 relies on average differences of a few accuracy points (e.g., AutoRound −0.80 vs. AWQ −2.53 for DeepSeek-R1-Distill-Qwen-7B), and Table 3 reports co-winner counts without any measure of seed-level variance. The authors should report the spread across seeds, bootstrap confidence intervals, or a paired significance test for the headline comparisons; otherwise findings 3 and 4, which hinge on small differences, are not robustly supported.","section":"§3; Tables 2 and 3"},{"comment":"The four-case taxonomy is presented with hand-selected examples and no operational rule for assigning a model–language pair to a case, and it is derived from the same data used to illustrate it. The breakdown definition in Eq. (3) depends on a user-chosen threshold Δbreak = 30, and Appendix C.2 shows that counts change materially with the threshold (e.g., OLMo-3-7B-Think has 0/8 TC breakdowns at all thresholds, while its AC count varies from 6/8 to 2/8). The paper should specify a reproducible classification procedure for the four cases and report the number of model–language pairs in each case; otherwise the taxonomy is a descriptive organizing device rather than an empirical result.","section":"§4.1, Figure 2 and §4.2, Eq. (3)"}],"minor_comments":[{"comment":"The phrase 'thelan guage' appears in the first line of the introduction, and Section 1 contains an incomplete sentence listing 'identify various language consistency behaviors,language-consistency breakdown effect, prompt control'; these should be fixed.","section":"Abstract and §1"},{"comment":"The header row of the table in Figure 2 (a) contains 'Model Accuracy Input Consistency Output Consistency' with empty cells under 'Accuracy' and 'Input Consistency'; the figure caption does not explain these columns, and the four case names in Figure 2 (b) are not defined until Section 4.1.","section":"Figure 2"},{"comment":"The column header 'W-A-KV Bits' is not defined anywhere in the text; the paper should state that this denotes weight-activation-key/value bit widths.","section":"Tables 2, 6, 7, and 8"},{"comment":"The caption says 'Table 3 reports tolerance-based voting results for ϵ = 1.0,' but Table 3 covers ϵ ∈ {0.5, 1.0, 2.0}; the reference should be corrected.","section":"Table 8 caption"},{"comment":"The phrase 'as showed in Table 2' is ungrammatical, and the sentence 'This gives the overall accuracy-preservation order' should read 'This gives the overall accuracy-preservation ordering'.","section":"§5.1"},{"comment":"The Limitations section lists benchmark, language, model, and prompt coverage but does not mention the two main threats identified above: detector validity and the lack of matched difficulty-level items; adding these limitations would make the scope of the claims clearer.","section":"§4.3 and §7"}],"recommendation":"major_revision","confidential_remarks":"The paper is a reasonable empirical study with a clear descriptive contribution, but its strongest claims—the abrupt breakdown effect and its causal role in accuracy non-monotonicity—depend on measurement validity and matched comparisons that are currently missing. I would be willing to reconsider after the authors add detector validation or token-level metrics, seed-level uncertainty, and an item-matched control for Section 4.3. The incremental novelty over PolyMath's aggregate consistency scores is modest, though the difficulty-dependent breakdown framing is useful if it survives the validation."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Two things you should know. First, the core observation is right: accuracy alone hides language drift, and disaggregating TC and AC by difficulty is a genuine extension of PolyMath. The four-case taxonomy, the breakdown effect, and the quantization analysis are all new and worth discussing. Second, the most dramatic numbers—Bengali TC falling from 97.9% to 1.6%—are only as trustworthy as the language detector that produced them, and that detector is never validated.\n\nThe paper does several things well. It moves beyond PolyMath's aggregate consistency scores, defines a clear taxonomy, and includes sensitivity analysis for the breakdown threshold and the tolerance parameter. The prompt control experiment is a nice touch: relaxing the reasoning-language constraint doesn't fix answer consistency, which strengthens the point that the phenomenon is not just an artifact of forcing a language. The quant section is exploratory but honest about the method-dependence of the rankings. If the measurement chain is sound, this is a solid empirical contribution.\n\nThe weak spot is measurement validity. TC and AC are computed from hard whole-sequence labels from the official PolyMath scripts, with no validation on these eight languages and these specific models. A hard label on a long trace is a thresholded function of the underlying language mix. As the model gradually inserts more English tokens, the label can flip abruptly even if the mixing changes smoothly. That the Bengali drop is so large suggests a real shift, not just a threshold artifact, but we can't rule out detector bias for code-switched, short, or numeric-heavy outputs—especially the latter, since a detector that labels numeric boxed expressions as English would systematically depress AC for all non-English languages. The accuracy-increase finding compares disjoint problem sets across difficulty levels; we don't actually know that medium Swahili problems are harder than low ones, only that they're from a different bucket. Three seeds are run, but no confidence intervals or significance tests appear anywhere, so the quantization rankings and breakdown counts are statistically fragile. No code, data, or predictions are released, which matters because the entire analysis rests on a detector the authors didn't build and didn't inspect.\n\nWho is this for? Evaluation researchers and practitioners who want to add consistency and difficulty axes to multilingual benchmarks. It deserves a serious referee, not a desk reject. The right referee will ask for token-level language-proportion analysis or a manual error audit of the detector on a sample, plus release of the evaluation pipeline and model outputs. If the detector holds up, the paper is a solid contribution. If it doesn't, the taxonomy and breakdown claims could be artifacts. Conditionally accept, with measurement validation as the gating condition.","headline":"The difficulty-resolved consistency analysis is a real step beyond PolyMath, but the dramatic breakdown numbers rest on an unvalidated language detector and should be treated as provisional.","tokens_in":18696,"tokens_out":2089,"would_cite":false,"duration_ms":22555,"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":"Rising task difficulty can make reasoning models abruptly drop the requested language, and the drop can inflate accuracy at harder levels.","keywords":["multilingual reasoning","language consistency","language consistency breakdown","chain-of-thought","quantization","LLM evaluation","PolyMath","low-resource languages"],"falsifier":"Take the same eight languages and four difficulty levels, have human annotators label the language of a sample of thinking traces and final answers, and compare TC and AC with the script-based scores; if the abrupt drops in languages like Bengali, Swahili, or Telugu vanish under human labels, the breakdown effect is an artefact of detection rather than model behaviour.","tokens_in":17727,"feed_emoji":"🌐","tokens_out":7376,"duration_ms":65245,"temperature":0.7,"pith_summary":"This paper argues that multilingual reasoning cannot be judged by final-answer accuracy alone: a model can solve a problem while thinking or answering in the wrong language, and this drift grows as problems get harder. Using PolyMath across eight languages and four difficulty levels, the authors identify four difficulty-dependent consistency behaviours and name the abrupt one the language consistency breakdown effect, where output-language consistency drops by 30 or more points between adjacent difficulty levels. Their central result is that this breakdown can break the usual monotonic accuracy–difficulty relationship, with accuracy preserved or even improved at a harder level as the model shifts to a dominant internal language. They also show that quantization changes language consistency independently of accuracy, so compressed models need separate consistency checks.","feed_headline":"Harder problems make reasoning models abandon the requested language","feed_subtitle":"Consistency can collapse while accuracy climbs, so multilingual evaluation must track language, not just answers.","key_machinery":"The load-bearing machinery is a pair of per-difficulty metrics: thinking-language consistency (TC) and answer-language consistency (AC), each defined as the fraction of model outputs in which an automatic language detector identifies the reasoning trace or final answer as the requested language. Forced-target-language prompting supplies the reference language, and a breakdown is defined as a drop of at least $\\Delta_{\\mathrm{break}}=30$ percentage points in TC or AC between two adjacent difficulty levels. PolyMath supplies eight languages and four difficulty levels so the same benchmark can track accuracy and consistency jointly; tolerance-based voting with $\\epsilon=1.0$ lets the quantization comparison treat small TC differences as ties.","core_discovery":"On the paper's own terms, the discovery is that language consistency is a distinct, measurable axis of multilingual reasoning behaviour that moves with task difficulty. Thinking-language consistency (TC) and answer-language consistency (AC) are defined as the fraction of reasoning traces and final answers, respectively, whose detected language matches the requested language. Across eight languages and four difficulty levels, consistency either stays aligned with the input, stays misaligned, degrades gradually, or collapses abruptly; the abrupt case is formalised as a drop of at least 30 percentage points between consecutive difficulty levels and called the language consistency breakdown effect. The effect is concentrated in less strongly represented and non-Latin-script languages, and it can make accuracy rise at a harder level, as in Bengali, Swahili, and Telugu for DeepSeek-R1-Distill-Qwen-7B. Quantization can improve or degrade TC independent of accuracy, with GPTQ and AWQ often beating AutoRound on thinking-language consistency even though AutoRound best preserves accuracy.","pith_inferences":["If the breakdown is driven by the model's internal dominant language, a testable mitigation is to insert periodic language anchors or per-step target-language verification during decoding; the paper does not test this.","Because TC and AC inherit the accuracy of automatic language detection, manually annotating a sample of short, code-switched, or low-resource outputs would confirm whether the reported breakdowns are linguistic behaviour rather than detector artefacts.","The same difficulty-consistency analysis could be ported to non-mathematical reasoning domains, such as science or code generation, where longer generations may show earlier or sharper breakdowns.","The four-case taxonomy suggests that averaging consistency across languages hides the most important behaviour; reporting per-language trajectories should become the default for multilingual evaluation."],"forward_implications":["Accuracy-only leaderboards can misrank multilingual reasoning models, because a harder level can look 'better' while language consistency collapses.","A model's accuracy gain at higher difficulty should be treated as a possible language shift rather than improved reasoning.","Compressed-model deployments should measure thinking- and answer-language consistency alongside accuracy, since the method that best preserves accuracy does not best preserve language.","Reasoning models are more susceptible to answer-language breakdowns than the non-reasoning models tested, so reasoning-specific evaluation suites need language checks.","Relaxing the instruction to reason in a preferred language does not guarantee the final answer stays in the requested language at high difficulty."],"supporting_citations":[{"why":"Supplies PolyMath, the benchmark whose eight languages and four difficulty levels ground every accuracy and consistency measurement, along with its official evaluation scripts.","marker":"Wang et al., 2026"},{"why":"Introduces DeepSeek-R1, the family from which two of the main reasoning models are distilled.","marker":"Guo et al., 2025"},{"why":"Provides OLMo-3-7B-Think, one of the two models whose per-language difficulty curves are the paper's main exhibits.","marker":"Olmo et al., 2026"},{"why":"Provides Phi-4-mini-reasoning, used for Case 1 and the prompt-control experiment.","marker":"Abdin et al., 2025"},{"why":"Establishes prior evidence of language confusion in LLM responses, which the paper extends to thinking traces.","marker":"Marchisio et al., 2024"},{"why":"Defines GPTQ, one of the three W4A16 quantization methods compared for language-consistency effects.","marker":"Frantar et al., 2022"},{"why":"Defines AWQ, the activation-aware quantization method that often preserves thinking-language consistency.","marker":"Lin et al., 2024"},{"why":"Defines AutoRound, the quantization method that best preserves accuracy but not consistency in the paper's comparison.","marker":"Cheng et al., 2024"},{"why":"Provides the baseline inference settings and the prior study of quantization on reasoning models that the paper adapts.","marker":"Liu et al., 2025"}],"fun_headline_variants":["Harder tasks make reasoning LLMs drop the requested language","Language consistency breaks as reasoning difficulty rises","Accuracy holds, but language consistency collapses under harder tasks","Multilingual reasoning: consistency falls while accuracy climbs","Reasoning models abandon language consistency as difficulty climbs"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The load-bearing premise is that the benchmark's language-detection scripts correctly identify the language of thinking traces and answers in all eight languages; if they misread short, mixed, or code-switched outputs, the reported breakdowns and the four-case taxonomy shift.","fun_headline_variants_meta":{"raw":{"variants":["Harder tasks make reasoning LLMs drop the requested language","Language consistency breaks as reasoning difficulty rises","Accuracy holds, but language consistency collapses under harder tasks","Multilingual reasoning: consistency falls while accuracy climbs","Reasoning models abandon language consistency as difficulty climbs"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000492,"raw_usage":{"total_tokens":2433,"prompt_tokens":973,"completion_tokens":1460,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":589,"completion_tokens_details":{"reasoning_tokens":1388}},"tokens_in":589,"tokens_out":1460,"duration_ms":11834,"temperature":1.0,"reasoning_tokens":1388,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-14T04:35:25.894823+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Take the same eight languages and four difficulty levels, have human annotators label the language of a sample of thinking traces and final answers, and compare TC and AC with the script-based scores; if the abrupt drops in languages like Bengali, Swahili, or Telugu vanish under human labels, the breakdown effect is an artefact of detection rather than model behaviour.","supporting_citations":[],"review_version":1}