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REVIEW 5 major objections 6 minor 1 cited by

M2-Reasoning: Empowering MLLMs with Unified General and Spatial Reasoning

T0 review · 5 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read M2-Reasoning-7B reports state-of-the-art scores on eight multimodal benchmarks by pairing a 294.2K-sample data pipeline with dynamic multi-task RLVR and task-specific rewards.

desk verdict Plausible RLVR recipe and a thoughtful data pipeline, but the 'SOTA across 8 benchmarks' label is overstated and the VSI-Bench claim rests on a cross-paper comparison under 0.3 points. read the letter →

arxiv 2507.08306 v1 pith:57IYSYUL submitted 2025-07-11 cs.AI cs.CLcs.CVcs.LG

Inclusion AI: Fudong Wang , Jiajia Liu , Jingdong Chen , Jun Zhou , Kaixiang Ji , Lixiang Ru , Qingpei Guo , Ruobing Zheng
show 5 more authors
Tianqi Li Yi Yuan Yifan Mao Yuting Xiao Ziping Ma
This is my paper · ORCID
classification cs.AIcs.CLcs.CVcs.LG
keywords multimodallargelanguagemodelsspatialreasoninggeneralreinforcementlearningwithverifiablerewardsGRPOchain-of-thoughtdatasynthesiscurationcurriculum
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

The paper claims that a 7-billion-parameter multimodal model, M2-Reasoning-7B, can simultaneously reach state of the art in two domains that are usually trained separately: abstract general reasoning and dynamic spatial reasoning. The recipe is a large, quality-filtered data pipeline — 168K cold-start chain-of-thought samples plus 126.2K reinforcement-learning prompts — followed by a GRPO variant with curriculum sampling, difficulty-based sample weighting, a cosine-annealed KL penalty, and rewards tailored to each task family. The central empirical claim is that this combination raises the model's average on six general-reasoning benchmarks to 45.0, a 9.5-point gain over its base, while setting the best base-scale average on CV-Bench (82.3) and a VSI-Bench average (42.3) that edges past InternVL3-8B. The significance would be that spatial interaction, a known weak point of multimodal reasoners, can be bootstrapped from synthetic annotations without sacrificing general math and logic performance.

What carries the argument

The paper's central machinery is a two-stage training pipeline anchored by two components. First, a data-construction pipeline turns public image-text and simulation data into 168K cold-start chain-of-thought samples (filtered by answer correctness plus a three-axis quality score) and 126.2K RLVR prompts (100K general, 18.7K spatial image, 7.5K spatial video), with RLVR prompts scored for difficulty as $1 - \text{accuracy}$. Second, a modified GRPO objective adds dynamic advantage weighting ($\sigma \cdot \text{mean\_acc} \cdot (1 - \text{mean\_acc})$, $\sigma = 7.2$), a cosine-annealed KL penalty coefficient, curriculum sampling ordered by difficulty, and task-specific accuracy rewards — rule-based exact matching for general questions, and the Exponential Decay Numeric Matching (EDNM) reward $R_{\text{EDNM}}(x) = \gamma \cdot \exp(-\lambda |x - x_{gt}| / (|x_{gt}| + \epsilon))$ with $\gamma = 1$, $\lambda = 2$ for spatial numeric answers. The EDNM reward is the named object that lets spatial fill-in-the-blank questions contribute smooth gradients when exact matches are rare.

What would settle it

Train or run the identical recipe but drop the 26.2K synthetic spatial RLVR samples (or replace them with spatial questions sampled from the benchmarks' own source data), and compare CV-Bench and VSI-Bench scores; if the 82.3 and 42.3 averages do not fall materially, the claimed spatial advantage is not caused by the synthetic data pipeline. Alternatively, test for overlap by asking whether any generated training question shares objects, scenes, or templated wording with CV-Bench or VSI-Bench items.

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Extended reading notes

Core claim

The central discovery claimed is that a unified MLLM can be made strong in both general reasoning and spatial reasoning by separating the two-stage reasoning recipe (cold-start SFT then RLVR) from the data and reward design. The authors report that M2-Reasoning-7B sets new state-of-the-art results across the eight evaluated benchmarks: a 45.0 average on six general-reasoning benchmarks (MathVista 75.0, MathVision 31.5, MathVerse 44.7, DynaMath 26.8, WeMath 41.8, LogicVista 50.0), an 82.3 average on CV-Bench, and a 42.3 average on VSI-Bench. They attribute the gains to two things: a data pipeline that synthesizes logically coherent reasoning traces, evaluates them for correctness, structure, cognitive load, and verification richness, and pre-scores RLVR prompts by difficulty; and a dynamic multi-task training strategy where each batch contains one task, steps are distributed uniformly, moderate-difficulty samples receive higher advantage weights, the KL penalty is annealed with cosine scheduling, and spatial numeric questions receive an exponential-decay reward instead of a binary match.

Load-bearing premise

The spatial gains rest on the assumption that spatial questions synthesized from depth and segmentation on real images and from simulated videos transfer to the real-image and real-video statistics of CV-Bench and VSI-Bench — and that none of those generated questions overlap with the benchmark tests, which the paper does not measure or check.

Editorial extensions

If this is right

  • A 7B MLLM can beat larger or dedicated reasoning models on general multimodal reasoning; the paper reports the best average (45.0) on the six-benchmark suite, with top scores on MathVista (75.0) and DynaMath (26.8).
  • Spatial reasoning in MLLMs can be improved substantially with synthetic annotations: the model reaches 82.3 on CV-Bench (best base-scale average) and 42.3 on VSI-Bench, surpassing InternVL3-8B in both averages.
  • Task-specific reward design matters: continuous EDNM rewards, rather than exact-match rewards, are what the paper credits with letting spatial numeric questions train effectively.
  • Dynamic, difficulty-aware GRPO — curriculum sampling, moderate-difficulty advantage weighting, and cosine-annealed KL — contributes the remaining gains over the cold-start model (42.9 to 45.0).
  • Data quality filtering, not just quantity, drives the result: the 168K cold-start chains were filtered by correctness and a three-axis reasoning-quality score before training.

Reading between the lines

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

  • If the synthetic-to-real transfer assumption holds, the same depth-and-segmentation-driven QA recipe could extend to other spatial tasks the paper does not test, such as 3D layout estimation, path planning, or motion prediction.
  • The reported CV-Bench advantage over InternVL3-8B is 0.3 points and the VSI-Bench advantage is 0.2 points; without a leakage or domain-gap check, those margins are within the range where distribution overlap or reward overfitting could explain them.
  • The EDNM reward is a generic continuous-scoring mechanism; it could in principle be applied to other continuous-output perception tasks such as depth estimation or bounding-box regression, a transfer the paper does not explore.
  • Because the paper excludes prompts at 0% and 100% response accuracy, the final RLVR set is concentrated on mid-difficulty items; the interaction between this filtering threshold and the dynamic advantage weighting is not ablated.
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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

5 major / 6 minor

Summary. The paper introduces M2-Reasoning-7B, a 7B multimodal LLM built on Qwen2.5-7B and the M2-Omni framework, trained in two stages: a cold-start SFT stage on a 6.2M-sample mixture (with 168K curated CoT samples) and a GRPO-style RLVR stage on 126.2K prompts (100K general, 18.7K spatial image, 7.5K spatial video). The method includes a difficulty-scored curriculum, dynamic advantage weighting, cosine-annealed KL, and a smooth EDNM reward for numeric spatial outputs. Evaluations on six general benchmarks plus CV-Bench and VSI-Bench report a general-reasoning average of 45.0 (up from a 35.5 base), a CV-Bench average of 82.3, and a VSI-Bench average of 42.3. The paper claims these results set a new SOTA across all eight benchmarks.

Significance. If the empirical claims hold under a unified evaluation protocol, the paper is a useful contribution: it releases model weights and code, gives a clearly specified data-synthesis and filtering pipeline, and demonstrates a substantial +9.5 average gain over the base model on the general-reasoning suite. The dynamic multi-task RLVR recipe and the EDNM reward are concrete, reproducible design choices. However, the spatial SOTA claims currently rest on evaluation-rigor weaknesses, especially the cross-paper VSI-Bench comparison and the absence of base-model or ablation rows for spatial benchmarks. The core training idea is plausible, but its significance cannot be fully assessed until those comparisons are made apples-to-apples and the individual contributions are separated.

major comments (5)
  1. [Abstract; §4.1, Table 1; §4.2, Table 3] The abstract and conclusion claim a 'new state-of-the-art across 8 benchmarks,' but the per-benchmark tables do not support that reading. In Table 1, M2-Reasoning-7B is not the best on MathVision (31.5 vs URSA-8B-PS-GRPO's 31.8), LogicVista (50.0 vs WeThink-VL-7B's 51.2), or WeMath (41.8 vs WeThink-VL-7B's 48.0). In Table 3, it is second to Gemini-1.5-pro on the VSI-Bench average (42.3 vs 45.4). If 'SOTA across 8 benchmarks' means the best average on the six-benchmark general suite plus best base-scale CV-Bench average, the authors should state that explicitly and soften the per-benchmark implication.
  2. [§4.2, Table 3; Appendix B.1] The VSI-Bench comparison is not apples-to-apples. All InternVL3-8B entries are marked †, i.e., copied from the InternVL3 technical report, while M2-Reasoning-7B is evaluated with its own trained system prompt and output-format requirements. VSI-Bench contains eight subtasks with distinct answer schemas, so scores are sensitive to prompt wording, answer extraction, and scoring. No error bars or item-level variance are reported, yet the average gap is only 0.2 points. The per-task pattern is lopsided (M2 trails on Object Count 41.0 vs 68.1 and Appearance Order 28.8 vs 35.4, while leading on Room Size 55.4 vs 33.6 and Relative Direction 47.3 vs 36.4), which is exactly the set of skills the spatial RLVR curriculum emphasizes. The claim that M2 'surpasses' InternVL3-8B on VSI-Bench is therefore not supported; the authors should rerun InternVL3-8B in their own harness or weaken the claim to 'competitive.'
  3. [§4.2, Tables 2 and 3] No base-model or cold-start-only rows are reported for CV-Bench or VSI-Bench. Table 1 separates Base Model, M2-Reasoning-CI-7B, and M2-Reasoning-7B, but the spatial tables report only the final model. Without these rows, the claimed spatial gains cannot be attributed to the RLVR stage or to the task-specific reward; they could come from the cold-start SFT or from the change of backbone. Please add base and cold-start evaluations for both spatial benchmarks.
  4. [§2.2; §4.2] The spatial training data are generated from depth maps, segmentation, and simulated videos, while CV-Bench and VSI-Bench contain real images and videos. The paper does not report any overlap check between the training samples and the evaluation benchmarks, nor any measure of the train-to-test domain gap. Given that the reported margins are thin on CV-Bench (82.3 vs 82.0) and VSI-Bench (42.3 vs 42.1), the possibility of reward overfitting or unintentional distribution overlap should be addressed, for example by reporting per-task generalization curves or by evaluating on a held-out set drawn from a different distribution.
  5. [§3.2, Eq. (9); Table 4] The EDNM reward is presented as central to spatial reasoning, but the manuscript contains no sensitivity analysis or ablation of this component. The hyperparameters γ=1 and λ=2 are fixed without supporting experiments, and Table 4 does not include a variant with a binary exact-match reward or different λ values. Similarly, the dynamic advantage scale σ=7.2 in Eq. (6) and the per-task epochs are not varied. Adding ablations would substantiate the claim that the task-specific reward and dynamic weighting, rather than the larger cold-start dataset or the backbone change, are responsible for the observed gains.
minor comments (6)
  1. [§1; §2.1.1] There are typos and duplicated phrases: 'diffucilty' in Section 1, 'serves as an automated evaluator for response correctness serves as an automated evaluator for response correctness' in Section 2.1.1, 'Genral' in Figure 2, and 'Visulization' in Appendix C.
  2. [§4.1, Table 1] The row labeled 'Base Model' is not defined in the text; please clarify whether it is M2-Omni with the Qwen2.5-7B language model before any reasoning-specific training, and report its evaluation setup.
  3. [Table 2] InternVL3-8B* is marked as 'reproduced by us,' but the reproduction settings (prompt, number of samples, answer parsing) are not described; without this information the comparison has the same ambiguity as the VSI-Bench cross-paper comparison, only at a smaller scale.
  4. [§3.1.2; Appendix B.2] The curriculum sampling is described qualitatively as 'increasing order of difficulty,' but the operationalization is not given; please specify how the offline difficulty scores are converted into training order (e.g., buckets, thresholds, or full ordering) and how this interacts with the per-task epoch counts in Table 4.
  5. [§2.1.2; §2.3] The paper reports that prompts with 0% or 100% response accuracy are excluded from RLVR, but it does not report the number of prompts removed at each stage or the final distribution of difficulty scores; reporting these flow statistics would strengthen the data-pipeline description.
  6. [§4.2] The phrase 'surpasses the strong InternVL3-8B baseline (42.1)' in the VSI-Bench discussion should be revised to reflect the cross-paper nature of the comparison, regardless of the outcome of the recommended rerun.

Circularity Check

0 steps flagged · score 2.0 of 10

No circular derivation: the central claims rest on external benchmarks and rule-based rewards; the paper's self-citations are initialization choices, not load-bearing evidence.

full rationale

The paper's claimed derivation is a data-construction and training recipe evaluated on external benchmark suites (MathVista, MathVision, MathVerse, DynaMath, WeMath, LogicVista, CV-Bench, VSI-Bench). No benchmark score appears as a fitted parameter in the training objective, and no equation identifies a predicted benchmark number with an input quantity. The RLVR rewards are rule-based answer checks (Eq. 8) and EDNM (Eq. 9) against synthetic ground-truth annotations, not against the evaluation benchmarks. The prompt-difficulty score used for curriculum sampling is 1 minus response accuracy on generated RLVR prompts, which is an input-ordering heuristic, not a prediction of evaluation performance. Initialization from M2-Omni and reuse of its non-thinking data are self-references, but they are engineering starting points with external provenance (a released checkpoint), and the reported gains are measured against the paper's own base model (35.5 to 45.0 in Table 1), not derived from the self-citation. The VSI-Bench comparison to an InternVL3-8B score copied from the technical report is an evaluation-comparability concern, not a circularity: it does not make the paper's result equivalent to its inputs by construction. Therefore no specific circular step can be quoted and exhibited, and the paper is best scored as essentially non-circular.

Assumptions & free parameters 8 free parameters · 8 assumptions · 0 invented entities

Most of the contribution is empirical, so the ledger is dominated by training choices and data assumptions rather than mathematical postulates. The EDNM and dynamic-weighting hyperparameters are hand chosen with no sensitivity analysis. The largest scientific assumption is that synthetic spatial QA transfers to real-world video and image benchmarks without leakage, which is not tested.

free parameters (8)
  • EDNM decay rate lambda = 2
    Controls the sensitivity of the numeric spatial reward to relative error (Eq. 9). Set by hand in Section 3.2 with no sensitivity analysis.
  • EDNM scaling gamma = 1
    Sets the maximum reward in Eq. 9. Chosen by hand in Section 3.2 without justification from data.
  • Dynamic advantage scale sigma = 7.2
    Appears in the advantage weighting formula (Eq. 6). Chosen by hand in Section 3.1.3 with no sensitivity study.
  • Initial KL coefficient beta = 0.01 with cosine annealing
    Used in Eq. 7 and Table 4. A standard RLVR hyperparameter chosen without sensitivity analysis.
  • Per-task epochs = General: 1, spatial image: 2, spatial video: 3
    Reported in Table 4. These per-task epoch counts are hand chosen and directly affect the curriculum and final performance.
  • Generations per prompt G = 16 general and spatial image, 4 spatial video
    Reported in Table 4. Affects the advantage estimate and training cost.
  • RLVR difficulty filter = exclude prompts with 0% or 100% response accuracy
    Section 2.1.2 removes extreme prompts from the RLVR dataset. This threshold determines the curriculum and is a data-selection choice.
  • Cold-start CoT quality threshold = not reported
    Section 2.1.1 uses an unspecified cutoff on the Qwen-based quality score to retain 168K chains. The threshold is not given, which limits reproduction.
assumptions (8)
  • standard math GRPO objective (Eq. 4) with importance sampling and KL penalty is a valid learning signal for reasoning.
    Uses standard policy-gradient machinery. The paper does not prove convergence or stability, but this is accepted background in the RLVR literature.
  • domain assumption Qwen2.5-7B-Instruct and Qwen2.5-VL-32B-Instruct give correct automated scores for answer correctness, reasoning quality, difficulty, and spatial QA validity.
    Sections 2.1.1 and 2.2.1 rely on these models as judges. No agreement analysis with human labels is given.
  • domain assumption Preprocessing of real images with depth estimation, segmentation, normals, and camera parameters yields accurate 3D annotations for spatial QA.
    Section 2.2.1 builds all image-based spatial tasks on automatically derived annotations from these preprocessing steps.
  • domain assumption Simulated video data captures real-world dynamic spatial properties such as room size and appearance order.
    Section 2.2 states the video simulation is crafted for these properties, but no domain-gap measurement is reported.
  • domain assumption Training samples do not overlap with or leak into the evaluation benchmarks.
    Implicit in Section 4. No contamination check is described, despite the same task types appearing in CV-Bench and VSI-Bench.
  • ad hoc to paper EDNM reward with gamma=1 and lambda=2 is an appropriate shaping for numeric spatial estimation.
    Section 3.2 introduces this reward to improve gradient signals. No benchmark-driven or theoretical justification is offered.
  • ad hoc to paper Difficulty defined as one minus response accuracy from ten samples is a valid curriculum ordering.
    Section 2.1.2 uses this to order RLVR data. Accuracy is noisy with ten samples and the ordering is never validated.
  • domain assumption M2-Omni's pretrained foundation is suitable for initializing the reasoning model.
    Section 3.1.1 initializes from the authors' own M2-Omni framework and data. This is self-referential but not circular.

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Cite this review

Pith. "Pith review of M2-Reasoning: Empowering MLLMs with Unified General and Spatial Reasoning." pith.science (2026). https://pith.science/paper/57IYSYUL

@misc{pith2026250708306,
  author       = {Pith},
  title        = {Pith review of: M2-Reasoning: Empowering MLLMs with Unified General and Spatial Reasoning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/57IYSYUL}},
  note         = {Machine review of arXiv:2507.08306}
}
read the original abstract

Recent advancements in Multimodal Large Language Models (MLLMs), particularly through Reinforcement Learning with Verifiable Rewards (RLVR), have significantly enhanced their reasoning abilities. However, a critical gap persists: these models struggle with dynamic spatial interactions, a capability essential for real-world applications. To bridge this gap, we introduce M2-Reasoning-7B, a model designed to excel in both general and spatial reasoning. Our approach integrates two key innovations: (1) a novel data pipeline that generates 294.2K high-quality data samples (168K for cold-start fine-tuning and 126.2K for RLVR), which feature logically coherent reasoning trajectories and have undergone comprehensive assessment; and (2) a dynamic multi-task training strategy with step-wise optimization to mitigate conflicts between data, and task-specific rewards for delivering tailored incentive signals. This combination of curated data and advanced training allows M2-Reasoning-7B to set a new state-of-the-art (SOTA) across 8 benchmarks, showcasing superior performance in both general and spatial reasoning domains.

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Reference graph

Works this paper leans on

25 extracted references · 25 canonical work pages · cited by 1 Pith paper

  1. [1]

    Optimal Structural Organization:Is the reasoning process clearly and logically struc- tured? Does it break down complex steps with sufficient detail, while keeping straightfor- ward steps concise? High-quality solutions adapt the level of detail (granularity) to the complexity of each step, ensuring that critical transitions receive appropriate elaboration

    work page

  2. [2]

    Effective Cognitive Scaffolding:Does the solution guide the reader’s understanding by gradually building up concepts and insights? Are key ideas introduced at the right time, and does the explanation bridge conceptual gaps thoughtfully, making the solution easier to follow and learn from?

    work page

  3. [3]

    Scoring Rubric: • 5 — Excellent:Exemplary reasoning with adaptive step granularity, strong pedagogical clarity, and thorough verification

    Rigorous V erification:Does the reasoning include frequent verification of intermedi- ate results? Are assumptions explicitly checked, and is logical consistency maintained throughout the solution? A high-quality answer thoroughly validates its correctness at each stage. Scoring Rubric: • 5 — Excellent:Exemplary reasoning with adaptive step granularity, s...

    work page

  4. [4]

    Thoroughly analyze the provided image, question, answer, and the choice list before scoring

    work page

  5. [5]

    – ObjectS (Object Score): Assess if the object(s) relevant to the question are clearly visible and identifiable in the image

    Evaluate the data quality based on the following three dimensions, providing a score of 1 (Good) or 0 (Bad) for each. – ObjectS (Object Score): Assess if the object(s) relevant to the question are clearly visible and identifiable in the image. – Score 1 if the objects are clear and unambiguous. – Score 0 if the objects are significantly occluded, blurry, ...

    work page

  6. [6]

    Output MUST be valid JSON format which can be loaded by json.loads() and contains the reasoning step

    work page

  7. [7]

    Reason\_Step

    Example: { "Reason\_Step": <reasoning step by step>, "ObjectS": 1, "AnswerS": 1, "OptionS": 0 }

    work page

  8. [8]

    Vision Only

    Please reason step by step, and put your final JSON format output within\boxed{}. 19 B Experimental Implementation Details B.1 System Prompts To guide the reasoning process during training, we employ the following system prompt in both the cold-start and RL VR stages: System Prompt Y ou are a helpful assistant. When the user asks a question, your response...

    work page 2025

Show all 25 references
  1. [9]

    Identify the fuel consumption for the Xtam car on the motorway from the bar chart

  2. [10]

    Calculate the monthly fuel consumption

  3. [11]

    From the bar chart: - The fuel consumption for the Xtam car on the motorway is 33.5 miles per gallon

    Multiply the monthly fuel consumption by 12 to get the annual consumption. From the bar chart: - The fuel consumption for the Xtam car on the motorway is 33.5 miles per gallon. Next, we need to calculate the monthly fuel consumption: - The driver travels 4,250 miles per month....

  4. [12]

    This means∠ACB = 90 ◦

    Since AB is the diameter of the circle, the angle subtended by the diameter at any point on the circle is a right angle. This means∠ACB = 90 ◦

  5. [13]

    Since ∠ADC and ∠ABC are both inscribed angles that subtend the same arc AC, they are equal

    We are given that ∠ADC = 26 ◦. Since ∠ADC and ∠ABC are both inscribed angles that subtend the same arc AC, they are equal. Therefore,∠ABC = 26 ◦

  6. [14]

    In triangle ABC, we know that the sum of the angles in a triangle is 180 ◦. So, we can write: ∠CAB+∠ABC+∠ACB= 180 ◦ Substituting the known values: ∠CAB+ 26 ◦ + 90◦ = 180◦ Simplifying this, we get: ∠CAB+ 116 ◦ = 180◦ ∠CAB= 180 ◦ −116 ◦ = 64◦ Therefore, the measure of∠CAB is 64 ...

  7. [15]

    The chair is located on the right side of the image

  8. [16]

    The bookcase is on the left side of the image, behind the couch and the desk

  9. [17]

    Given the positions: - The table is directly in front of the chair and appears to be closer to it

    The table is in the center of the image, closer to the chair than the bookcase. Given the positions: - The table is directly in front of the chair and appears to be closer to it. - The bookcase is further away, on the opposite side of the room. Therefore, the table is closer t...

  10. [18]

    The sofa is visible

    The first frame shows a living room with a sofa, a table, and a potted plant. The sofa is visible

  11. [19]

    The second frame shows a dining area with a table and chairs, but no sofa, blanket, or microwave

  12. [20]

    The third frame shows a view of the living room again, with the sofa and a blanket on it

  13. [21]

    The fourth frame shows a view of the living room, with the sofa and a blanket

  14. [22]

    The fifth frame shows a view of the living room, with the sofa and a blanket

  15. [23]

    The sixth frame shows a view of the living room, with the sofa and a blanket

  16. [24]

    The seventh frame shows a view of the living room, with the sofa and a blanket

  17. [25]

    From the frames, the order of appearance is: - Sofa appears first

    The eighth frame shows a view of the kitchen, with a trash can and a microwave. From the frames, the order of appearance is: - Sofa appears first. - Blanket appears next. - Trash can appears next. - Microwave appears last. Thus, the correct order is: sofa, blanket, trash can, ...

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