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REVIEW 4 major objections 6 minor 16 cited by

Voxtral

T0 review · 4 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read An open-weight 24B audio model claims state-of-the-art transcription and translation across tested speech benchmarks.

desk verdict The open-weights release and benchmarks are genuinely useful, but the MCV state-of-the-art claim is an artifact of dropping Arabic, and the paper should fix or soften that claim before it is trusted. read the letter →

arxiv 2507.13264 v1 pith:HXSYQGJY submitted 2025-07-17 cs.SD cs.AIeess.AS

Alexander H. Liu , Andy Ehrenberg , Andy Lo , Clément Denoix , Corentin Barreau , Guillaume Lample , Jean-Malo Delignon , Khyathi Raghavi Chandu
show 98 more authors
Patrick von Platen Pavankumar Reddy Muddireddy Sanchit Gandhi Soham Ghosh Srijan Mishra Thomas Foubert Abhinav Rastogi Adam Yang Albert Q. Jiang Alexandre Sablayrolles Amélie Héliou Amélie Martin Anmol Agarwal Antoine Roux Arthur Darcet Arthur Mensch Baptiste Bout Baptiste Rozière Baudouin De Monicault Chris Bamford Christian Wallenwein Christophe Renaudin Clémence Lanfranchi Darius Dabert Devendra Singh Chaplot Devon Mizelle Diego de las Casas Elliot Chane-Sane Emilien Fugier Emma Bou Hanna Gabrielle Berrada Gauthier Delerce Gauthier Guinet Georgii Novikov Guillaume Martin Himanshu Jaju Jan Ludziejewski Jason Rute Jean-Hadrien Chabran Jessica Chudnovsky Joachim Studnia Joep Barmentlo Jonas Amar Josselin Somerville Roberts Julien Denize Karan Saxena Karmesh Yadav Kartik Khandelwal Kush Jain Lélio Renard Lavaud Léonard Blier Lingxiao Zhao Louis Martin Lucile Saulnier Luyu Gao Marie Pellat Mathilde Guillaumin Mathis Felardos Matthieu Dinot Maxime Darrin Maximilian Augustin Mickaël Seznec Neha Gupta Nikhil Raghuraman Olivier Duchenne Patricia Wang Patryk Saffer Paul Jacob Paul Wambergue Paula Kurylowicz Philomène Chagniot Pierre Stock Pravesh Agrawal Rémi Delacourt Romain Sauvestre Roman Soletskyi Sagar Vaze Sandeep Subramanian Saurabh Garg Shashwat Dalal Siddharth Gandhi Sumukh Aithal Szymon Antoniak Teven Le Scao Thibault Schueller Thibaut Lavril Thomas Robert Thomas Wang Timothée Lacroix Tom Bewley Valeriia Nemychnikova Victor Paltz Virgile Richard Wen-Ding Li William Marshall Xuanyu Zhang Yihan Wan Yunhao Tang
This is my paper · ORCID
classification cs.SDcs.AIeess.AS
keywords speechrecognitiontranslationmultimodallanguagemodelaudiounderstandingopen-weightslong-contextquestionansweringdirectpreferenceoptimization
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Voxtral Mini and Voxtral Small are two open-weight chat models trained to understand both spoken audio and text, released under an Apache 2.0 license. The paper's central claim is that the larger Small model, at 24B parameters, achieves state-of-the-art word error rates on English short-form and multilingual Common Voice transcription and beats all tested open and closed systems, and that it reaches the best BLEU scores on every FLEURS speech-translation pair tested. The authors argue this is possible without sacrificing text performance and while keeping the model small enough to run locally, with a 32K context window for audio up to 40 minutes. They also release three speech-synthesized benchmarks and an internal understanding benchmark for knowledge and trivia questions.

What carries the argument

The mechanism that carries the argument is the chunked audio front end: the encoder uses Whisper's fixed 30-second receptive field, processing each chunk independently with positional encodings reset per chunk, then concatenating the chunk embeddings and passing them through an MLP adapter that downsamples the frame rate 4x to 12.5 Hz before the language decoder autoregressively generates text. Two special tokens, <repeat> and <next>, disambiguate the two pretraining patterns, audio-to-text repetition and cross-modal continuation, whose balanced mixture is shown in ablations to be necessary for both transcription and understanding.

What would settle it

Build a QA set where the answer requires combining facts from two chunks separated by more than 30 seconds, and swap the chunk order to create a control: if accuracy is statistically unchanged or near chance after the swap while short-form WER stays low, the reset-position chunk design, not the decoder, is the ceiling on long-context reasoning.

Watch

Extended reading notes

Core claim

On its own terms, the paper establishes that a 24B audio-text model built from a Whisper-style encoder, a 4x downsampling adapter, and a text decoder can surpass larger and closed-source comparison systems on speech recognition and translation: Voxtral Small posts the best English short-form and MCV word error rates, the best FLEURS translation score in every tested source/target pair, and competitive accuracy on speech question answering while retaining text-only benchmark performance. The smaller Mini model, when trained only for transcription, also beats the closed API transcribe systems on every recognition task tested.

Load-bearing premise

Everything about long-form understanding rests on the assumption that concatenating independently encoded 30-second chunks, with positions reset per chunk, gives the decoder enough cross-chunk information to reason about the full audio.

Editorial extensions

If this is right

  • If the central claims hold, a 24B Apache-licensed model is a viable local drop-in for cloud transcription and translation services on the tested benchmarks.
  • The 32K context, at 12.5 Hz audio embeddings, makes single-pass handling of 40-minute audio practical for question answering and summarization rather than requiring segmentation and stitching.
  • The ablation results imply that training recipe choices, balanced repetition and continuation patterns and a 4x downsampling rate, are load-bearing for the balanced performance rather than incidental details.
  • Online DPO improves response-quality grades on the internal speech-understanding benchmark, but for Small it costs about 0.2 word error rate on English short-form, which is why the released Small checkpoint is the SFT version.
  • The released speech-synthesized GSM8K, TriviaQA, and MMLU subsets give the field a standard way to measure spoken knowledge and reasoning that did not previously exist.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • My inference: the reset-per-chunk positional encoding puts a testable ceiling on cross-chunk reasoning; tasks whose answer depends on the relative order of two chunks more than 30 seconds apart should be fragile even if short-form ASR stays strong.
  • My inference: the synthesized benchmarks mix speaker diversity and TTS filtering with spoken-input difficulty, so their scores are not directly comparable to text MMLU numbers and should be read as a new capability axis rather than a substitute.
  • My inference: the observation that a text reward model fed only the transcription improves audio response quality suggests that preference alignment for speech can reuse text infrastructure, which would lower the cost of aligning other audio models.
  • My inference: the 6.25 Hz frame-rate penalty on FLEURS French suggests a floor on how far temporal downsampling can go before fine phonetic detail is lost, so scaling to longer context by more downsampling is unlikely to be free.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper introduces Voxtral Mini and Voxtral Small, two Apache-2.0-licensed audio-text language models built from Mistral text backbones (Ministral 3B and Mistral Small 3.1), a Whisper large-v3 audio encoder, and a temporal downsampling adapter. Training consists of speech-text pretraining with two interleaving patterns signaled by special tokens, supervised finetuning on synthetic and real data, and DPO/Online DPO alignment. The paper reports word error rates, speech translation BLEU scores, speech understanding accuracy, text-only benchmark scores, and ablations on padding, adapter downsampling, pretraining pattern ratios, and alignment. It also contributes speech-synthesized versions of GSM8K, TriviaQA, and MMLU, plus an in-house speech understanding (SU) benchmark. The central claims are that Voxtral Small achieves state-of-the-art speech recognition on English Short-Form and MCV, state-of-the-art translation on all tested FLEURS pairs, and competitive speech understanding while preserving text performance.

Significance. If the headline results hold after the issues below are addressed, this is a useful open-weights system contribution: both model sizes and the three contributed benchmarks are released under permissive licenses, and the ablations on padding, frame-rate, and pretraining patterns are informative for practitioners. The paper is also honest about several limitations, such as the padding degradation and the regression of online DPO on ASR. However, the state-of-the-art claims are currently supported by a narrow comparison set and at least one aggregation choice that reverses the headline MCV result, and the long-context capability is asserted without a direct cross-chunk evaluation. These issues are fixable, but they need to be corrected before the claims can be accepted as stated.

major comments (4)
  1. [Section 4.1, Figure 3, Table 5] The claim that Voxtral Small achieves state-of-the-art transcription on MCV depends on omitting Arabic from the macro-average. Table 5's caption states that Arabic is omitted 'for fairness, since all models score in excess of 45%', but adding Arabic back to the macro-average reverses the ranking: without Arabic, Voxtral Small is 5.65% WER versus Scribe's 5.87%, while with Arabic, Voxtral Small is 11.91% versus Scribe's 10.44%. Arabic is the only language meeting the stated 45% threshold, and it is also the language that flips the outcome, so the exclusion is not a neutral quality filter. Please report both the with-Arabic and without-Arabic macro-averages, or use a pre-specified aggregation rule, and adjust the state-of-the-art claim accordingly.
  2. [Section 4.2, Table 7] The statement that Voxtral Small achieves 'state-of-the-art translation scores in every source/target combination' is not supported by the baseline set. Table 7 compares only against GPT-4o mini Audio, Gemini 2.5 Flash, and Whisper large-v3 (restricted to X-to-English). There are no dedicated open-weights speech translation systems (for example, SeamlessM4T-family models) and no other open-weights speech LLMs, so 'state-of-the-art' is a claim about this specific comparison set rather than the field. BLEU scores are also reported as point estimates without confidence intervals or significance testing. Please broaden the comparison, or rephrase the claim as 'outperforms the compared closed models on this subset.'
  3. [Section 2.1, Abstract, Section 1] The long-context capability is asserted for audio files up to 40 minutes, but the architecture processes 30-second chunks independently with positional encodings reset per chunk and the chunk embeddings concatenated afterward. This design provides no cross-chunk relative positional information to the decoder, so tasks that require integrating information from two distant chunks are not guaranteed to work. The reported evaluations do not directly test cross-chunk integration: the SU benchmark has audio up to 19 minutes, the English long-form earnings data are segmented into 10-minute clips, and the ASR/translation benchmarks are short-form. Please add at least one evaluation that requires combining information across chunk boundaries, or qualify the long-context and 40-minute claims to the current evidence.
  4. [Section 5.4, Figure 5, Table 8] The in-house SU benchmark is used both for checkpoint selection and as a headline evaluation result. In Table 2, the SU benchmark scores are used to justify the released Voxtral Mini Online DPO checkpoint and to discuss the Voxtral Small SFT versus Online DPO choice, and the same benchmark is then reported in Figure 5 and Table 8 as evidence of speech understanding capability. Without a clear train/evaluation split or a statement that the reported SU numbers were computed on examples not used for any selection decision, there is a real risk of selection-on-test-set bias. Please disclose how the SU benchmark was partitioned, or separate the model-selection set from the final evaluation set.
minor comments (6)
  1. [Section 5.2] The text says 'FLUERS French' and Table 3's caption says 'SPGISPeech'; both should be corrected to 'FLEURS' and 'SPGISpeech'.
  2. [Section 5.1] Section 5.1 refers to 'Figure 8' when describing the padding experiment, but the padding plots are in Figure 7; the downsampling experiment in Section 5.2 correctly refers to Figure 8.
  3. [Throughout] The model name is typeset with an intervening space as 'V oxtral' throughout, which appears to be a rendering artifact; please use the intended 'Voxtral' consistently.
  4. [Tables 3-7] WER and BLEU values are reported without confidence intervals or significance tests, although Table 2 shows that the internal evaluation uses repeated judging. Please state whether the ASR/translation differences are stable, or provide variance estimates.
  5. [Section 3.4] The acronyms 'MMAU' and 'AU Bench' in Table 8 are not defined in the text or appendix; please define them and cite the sources.
  6. [Section 3.4, Appendix A.3] The speech-synthesized benchmarks are generated with the same lab's TTS system and filtered/rewritten with Mistral Large; please report the number of examples kept after each filtering step to let readers assess the difficulty and potential selection bias.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: headline ASR/translation results are external-benchmark comparisons; disclosed evaluation choices (MCV Arabic exclusion, SU-based checkpoint selection) are robustness concerns, not circular derivations.

full rationale

The paper is an empirical model report rather than a derivation chain. The headline claims (Section 4.1 English Short-Form/MCV and Section 4.2 FLEURS translation) are comparisons against external corpora with per-language tables disclosed in Appendix A.1, so they do not reduce by construction to the model's own definitions. The MCV macro-average excludes Arabic under a stated rule in the Table 5 caption ('For fairness, we omit Arabic from the macro-average in Figure 3, since all models score in excess of 45%.'); this exclusion can flip the ranking and is a legitimate evaluation-construction concern, but it is not circular because the underlying numbers are external, the rule is disclosed, and the claim is not an equation or a fitted parameter renamed as a prediction. The internal SU benchmark (Section 3.4) is used both for checkpoint selection and for the DPO gains reported in Table 2, which creates selection bias; however, the paper reports SFT/DPO/Online DPO side by side and does not define Voxtral's capability in terms of that benchmark. Self-citations to Ministral, Mistral Small 3.1, and Magistral (Sections 2.3 and 3.3) describe components and infrastructure rather than serving as load-bearing evidence for the SOTA claims. The chunk-wise 30-second encoder design in Section 2.1 is an architectural assumption with disclosed trade-offs, not a self-justifying result. No concrete reduction of a claimed result to its inputs was found.

Assumptions & free parameters 4 free parameters · 5 assumptions · 2 invented entities

The central empirical claims rest on pretrained components (Whisper encoder, Mistral text backbones), on synthetic data generated by Mistral Large and TTS, on chunk-wise long-audio processing, and on LLM-judge evaluation. These are reasonable engineering assumptions, but they are not derived results and several are not independently verified.

free parameters (4)
  • Adapter downsampling factor = 4x (12.5 Hz)
    Selected in Section 5.2 as the best trade-off between WER, Llama QA accuracy, and sequence length. This is a hyperparameter tuned on the evaluation sets.
  • Pretraining pattern ratio = 50/50 repeat vs continuation
    Section 5.3 ablates pattern proportions and chooses equal sampling. The ratio is chosen from FLEURS and Llama QA results.
  • Audio padding length = 30 seconds
    Section 5.1 keeps Whisper-style padding because removing it degrades FLEURS French WER. This is a design choice tuned on evaluation data.
  • Online DPO sampling temperature = 0.5
    Section 3.3 states candidate responses are sampled at temperature T=0.5 for Online DPO. This is a tuned hyperparameter for preference alignment.
assumptions (5)
  • domain assumption Whisper large-v3 pretrained encoder provides strong audio representations suitable for the full model.
    Section 2.1 initializes the audio encoder from Whisper large-v3; the paper does not derive why this transfer works.
  • domain assumption The text decoder backbones (Ministral 3B, Mistral Small 3.1) retain text knowledge after audio training.
    Section 2.3 builds on these backbones, and Section 4.4 reports preserved text performance. This relies on the pretrained text weights.
  • domain assumption Synthetic data generated by Mistral Large and TTS is suitable supervision for real speech understanding.
    Section 3.2 relies heavily on Mistral Large for QA, summarization, and translation data, and on TTS for audio-only inputs. The quality and coverage of this synthetic data are assumed.
  • domain assumption Chunk-wise attention with reset positional encodings is sufficient for long audio understanding.
    Section 2.1 processes audio in independent 30-second chunks and concatenates embeddings. Cross-chunk reasoning depends on the decoder, with no positional ordering across chunks.
  • domain assumption An LLM judge with access to the transcript and reference answer is a valid measure of audio QA helpfulness.
    Section 3.4 and Appendix A.4 use an LLM judge for the internal SU benchmark. The judge never hears the audio, only a transcript, so transcript errors can bias scores.
invented entities (2)
  • <repeat> and <next> special tokens independent evidence
    purpose: Signal whether the model should transcribe the audio segment or continue with the following text segment during pretraining and inference.
    These are new vocabulary tokens whose effects are demonstrated in the Section 5.3 ablation and are observable in the released model.
  • Transcribe mode special token independent evidence
    purpose: Allow the model to perform transcription without a text prompt.
    Introduced in Section 3.2 for ASR use cases; it is part of the released checkpoints and can be tested directly.

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Cite this review

Pith. "Pith review of Voxtral." pith.science (2026). https://pith.science/paper/HXSYQGJY

@misc{pith2026250713264,
  author       = {Pith},
  title        = {Pith review of: Voxtral},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HXSYQGJY}},
  note         = {Machine review of arXiv:2507.13264}
}
read the original abstract

We present Voxtral Mini and Voxtral Small, two multimodal audio chat models. Voxtral is trained to comprehend both spoken audio and text documents, achieving state-of-the-art performance across a diverse range of audio benchmarks, while preserving strong text capabilities. Voxtral Small outperforms a number of closed-source models, while being small enough to run locally. A 32K context window enables the model to handle audio files up to 40 minutes in duration and long multi-turn conversations. We also contribute three benchmarks for evaluating speech understanding models on knowledge and trivia. Both Voxtral models are released under Apache 2.0 license.

Figures

Figures reproduced from arXiv: 2507.13264 by the authors.

Figure 1
Figure 1. Voxtral Architecture. The audio encoder processes the speech input, attending to 30-second chunks of audio independently. The audio embeddings are concatenated at the output, and downsampled by a factor of 4x in the audio-language adapter. The multimodal LLM decoder auto-regressively predicts text tokens, conditional on the audio and text inputs. 2.1 Audio Encoder The audio encoder is based on Whisper large-v3 [Radf… view at source ↗
Figure 2
Figure 2. Pretraining patterns. A single audio-text example (A, T) is first segmented into a set of audio￾text pairs {(An, Tn)} N n=1, based on the timestamps and transcriptions returned by segmentation stage. For the audio-to-text repetition pattern, a given audio An is repeated in the text space Tn. For the cross-modal continuation pattern, each audio An is followed by its subsequent text Tn+1. The task is signaled to the m… view at source ↗
Figure 3
Figure 3. plots the macro-averaged word error rates (WER) on four benchmarks: English Short-Form, English Long-Form, Mozilla Common Voice 15.1 (MCV) [Ardila et al., 2020] and FLEURS [Conneau et al., 2022]. We compute the macro-average across tasks for English Short and Long-Form, and languages for MCV and FLEURS. Voxtral Small achieves state-of-the-art transcription results on English Short-Form and MCV, beating all open and … view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: FLEURS Translation. BLEU scores for source/target language pairs on the FLEURS Translation benchmark. Voxtral Small achieves state-of-the-art for every combination of languages. 4.3 Speech Understanding We evaluate Voxtral on a range of public Speech QA benchmarks, suc…
Figure 5
Figure 5. Figure 5: Speech Understanding Benchmarks. We report the accuracy across three speech understanding benchmarks and three synthesized speech subsets of text benchmarks. Voxtral Small is competitive with closed￾source models, surpassing GPT-4o mini Audio on three of the seven benc…
Figure 6
Figure 6. Figure 6: Text-Only Benchmarks. We report the accuracy across five standard text understanding benchmarks. Voxtral Small performs comparably to Mistral Small 3.1, highlighting its strong text capabilities. 5 Analysis In this Section, we share results and analyses for two archite…
Figure 7
Figure 7. Figure 7: Effect of Padding. Word error rate results on FLEURS English (left) and FLEURS French (middle), alongside 3-shot Accuracy on Llama QA (right) for models trained with and without 30-second padding. 5.2 Adapter Downsampling The baseline audio encoder operates at a frame-…
Figure 8
Figure 8. Figure 8: Effect of Downsampling. Word error rate results on FLEURS English (left) and FLEURS French (middle), alongside 3-shot Accuracy on Llama QA (right) for various frame-rates, achieved by increasing the downsampling factor by powers of 2. 5.3 Pre-Training Patterns Recall t…
Figure 9
Figure 9. Figure 9: Pattern Proportions. Word error rate results on FLEURS English (left) and FLEURS French (middle), alongside 3-shot Accuracy on Llama QA (right) for varying proportions of pretrain patterns. 5.4 DPO and Online DPO [PITH_FULL_IMAGE:figures/full_fig_p009_9.png]

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Works this paper leans on

17 extracted references · 17 canonical work pages · cited by 16 Pith papers

  1. [1]

    Numbers should be written in English words rather than Arabic or roman numerals

    The question should not contain content that cannot be synthesized by a Text To Speech(TTS) model. Numbers should be written in English words rather than Arabic or roman numerals. If they seem to be roman numerals after names of kings and queens, say it as the second, or the third corresponding to the roman number. If the instruction contains only a numbe...

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  2. [2]

    The question should be relatively brief without excessive verbiage

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  3. [3]

    Expand abbreviations and acronyms (e.g., ’macOS’ as ’mac O S’, ’TensorRT’ as ’Tensor R T’, ’CMake as C Make’, ’JDBC’ as ’Java Database Connectivity’, ’API as A. P. I.’). An abbreviation is hard for a TTS model to say because its not a legitimate english word. Its better to break it up into capital characters

    work page

  4. [4]

    Only if the bullets start with alphabets, use corresponding alphabets like A, B, C, D or use Option A, Option B, Option C, Option D

    If there are number bullets, asterisk bullets, hyphen bullets or dot bullets and the bullets do not seem like options being given by user in the instruction, list them as first, second, lastly or number one, number two and so on. Only if the bullets start with alphabets, use corresponding alphabets like A, B, C, D or use Option A, Option B, Option C, Opti...

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  5. [5]

    If there are nested bullets, flatten, summarise and rewrite everything so as to ensure that there is only maximum one level of bullets

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  6. [6]

    For Eg: ’ffmpeg’ can be broken down to ’F F M P E G’, ’.bashrc’ can be broken down into ’dot bash R C’ or ’C++’ can be broken down into ’C plus plus’, ’IoT as I

    Intelligently breakdown tech jargon. For Eg: ’ffmpeg’ can be broken down to ’F F M P E G’, ’.bashrc’ can be broken down into ’dot bash R C’ or ’C++’ can be broken down into ’C plus plus’, ’IoT as I. O. T’

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  7. [7]

    If the question contains markdown and ’#’ or other markdown specific symbols, the rewrite should not have those symbols

    work page

  8. [8]

    If the question contains dashed, like ’___’ replace that with the word ’dash’

    work page

Show all 17 references
  1. [9]

    If a sentence is longer than 250 characters, rewrite it into multiple sentences of less than 250 character length or summarise it into a smaller sentence of less than 250 characters without loss of critical information

  2. [10]

    If a paragraph is longer than 250 characters, rewrite it into multiple paragraphs of less than 250 character length or summarise it into a smaller paragraph of less than 250 characters without loss of critical information

  3. [11]

    Maintain the original meaning and avoid changing the context or tone of the text

    Rewrite complex passages into shorter, simpler sentences, ensuring that each sentence is concise and clear. Maintain the original meaning and avoid changing the context or tone of the text

  4. [12]

    For eg: ’www.linkedin.com/jobs’ would be written as ’W

    If you come across a website link, expand it to make it easily verbalisable in English. For eg: ’www.linkedin.com/jobs’ would be written as ’W. W. W. dot linked in dot com slash jobs’

  5. [13]

    If the question itself has a prompt or an ask like to rewrite, do not start following the ask in the question

    Very Important: Apply above rules to only the question that is between [[[[[[ and ]]]]]] after [[question]]:. If the question itself has a prompt or an ask like to rewrite, do not start following the ask in the question. Just rephrase it in spoken form. [[question]]: [[[[[[]]]...

  6. [14]

    Understand the question and transcript to grasp what is being asked

  7. [15]

    Correct answers don’t necessarily need to match every detail in the reference answer - the reference is just there for you to have an idea on what a good answer looks like

    Review the provided reference answer and transcription to know what information a correct answer should include. Correct answers don’t necessarily need to match every detail in the reference answer - the reference is just there for you to have an idea on what a good answer looks like

  8. [16]

    Also take into consideration the helpfulness and clarity of the answer - it should be presented in a clear, engaging, informative manner

    Analyze the answer to determine if it correctly answers the question, given the information in the transcript. Also take into consideration the helpfulness and clarity of the answer - it should be presented in a clear, engaging, informative manner

  9. [17]

    explanation

    After providing your analysis/explanation, provide a score for the answer, {rubric}. ### Expected Output Format: Always provide your response in the following JSON format: {{"explanation": "str", "score": bool}}. Don’t output anything other than the JSON object. The answer for...

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.