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REVIEW 3 major objections 4 minor 69 references

The Low Frequency Trap: Video Language Models Fail at Simple Event Bookkeeping

T0 review · 3 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read Video-language models fail at event counting once load rises, trace audit shows.

desk verdict Careful trace-grounded benchmark with a real diagnostic payoff, but the headline representation claim overreaches because the blink pulse duration is unreported and may be an input-sampling artifact. read the letter →

arxiv 2608.06361 v1 pith:KEQIS47K submitted 2026-08-06 cs.AI

Sarvesh Baskar , Zikui Cai , Shayan Shabihi , Anirudh Satheesh , Muhammad R. Islam , Udari Madhushani Sehwag , Tom Goldstein , Furong Huang This is my paper
classification cs.AI
keywords eventcountingvideo-languagemodelstemporalreasoningexecutabletracescapabilitysurfacesbenchmarkingfrequencyparametricprofiling
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 argues that current video-language models cannot reliably do simple event bookkeeping—counting repeated visual events—once event count or frequency grows, and that standard final-answer benchmarks hide this failure. Using 2,190 controlled videos across three tasks, each paired with an executable ground-truth event trace, the authors map where Gemini 3.6 Flash stays above an 80% accuracy threshold over event count and frequency. Persistent state transitions count reliably through twelve events at low frequency, while transient blinks never reach a reliable positive-count region. In the high-count, high-frequency regime only 0.2% of final counts are correct and only 18.1% of true events are recovered. The paper's central claim is that aggregate accuracy scores overstate temporal fidelity because correct final answers often coexist with unfaithful reported event traces.

What carries the argument

The central object is the executable event trace, a renderer-generated schedule $T^* = \{(t_i, e_i, s_i^-, s_i^+, c_i)\}_{i=1}^N$ produced by the same event schedule used to generate the video. It carries the argument by letting the authors align model-reported timestamps to ground truth within a rate-relative window $\delta(F) = 1/(2F)$, the half inter-event period, and then score precision, recall, trace F1, visual observation ratio, accidental correctness, and reasoning failure. This trace alignment is what converts a final-count benchmark into a diagnosis of where temporal reasoning fails.

What would settle it

Render the Blinking task with a pulse duration at least as long as one frame interval, such as one second at 1 FPS, or report the exact on-pulse duration used; if the no-reliable-positive-count region persists with full-frame pulses, the transient-representation claim is supported, and if a reliable region appears, the observed failure is an input-sampling artifact.

Watch

Extended reading notes

Core claim

On the paper's own terms, the discovery is a staged, representation-dependent temporal failure: whether an event persists on screen determines whether the model can access the evidence at all, and once count and frequency rise, models under-report events and stop aggregating them correctly. The executable traces show that reported timestamps align with ground truth only partially, with macro trace F1 at 36.4%, that correct final counts can arise from low-fidelity traces, and that more input frames raise final accuracy from 19.6% to 29.3% while reported-sequence agreement stays at 3.7%. Better final scores therefore need not reflect faithful event recovery.

Load-bearing premise

The Blinking task's pulse duration is never reported, so if each blink is short relative to the supplied one-frame-per-second sampling rate, many pulses will fall entirely between frames and the model's failure to count them could reflect input undersampling rather than a limit of event bookkeeping.

Editorial extensions

If this is right

  • If the claim is correct, final-answer exact match should not be the primary measure of temporal video understanding; benchmark suites should pair videos with executable traces and score reported timestamps.
  • Persistent state transitions being countable through twelve events at 0.5–1.0 Hz shows that low-frequency bookkeeping is not inherently impossible, so progress should be measured against the count-by-frequency boundary rather than aggregate accuracy.
  • Dense sampling alone will not restore faithful event recovery: 4 FPS raised exact match from 19.6% to 29.3% but trace F1 stayed at 3.7%, so extra frames can inflate final scores while missing events remain hidden.
  • Prompting strategies—direct, structured trace, multi-turn verification, chain of thought, and role prompting—all stay near 19–20% on Bounce Ball, indicating the bottleneck is not instruction format.
  • On natural repeated-event videos the same concentration of accuracy at low counts appears across prompting formats, suggesting the controlled failure pattern transfers outside synthetic clips.

Reading between the lines

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

  • An untested implication is that the bottleneck is largely retention or aggregation rather than perception: oracle keyframes at every event reach 68.6% accuracy but collapse past five events, which separates event localization from sequence retention.
  • A testable extension is to vary blink pulse duration in the Blinking task, for example from 0.1 to 2 seconds, to separate the transient-representation effect from the frame-sampling effect; the paper itself leaves event duration outside its controlled scope.
  • If the 0.2% high-load figure generalizes, applications that require counting many fast events cannot trust current video-language models without timestamp-level auditing.
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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

3 major / 4 minor

Summary. The paper introduces trace-grounded parametric profiling for video event counting. It generates 2,190 controlled Manim videos across three tasks (bouncing-ball wall contacts, visual blinks, categorical state transitions), sweeping event count N and frequency F while holding rendering fixed. Each video is paired with an executable ground-truth trace, and the authors align model-reported timestamps with these traces to compute final-answer exact match, precision, recall, trace F1, and two new diagnostics (ACR and RFR). The primary results show that Gemini 3.6 Flash has a broad reliable region for persistent State Machine transitions up to N=12 at low frequencies, no reliable positive-count region for Blinking, very low trace fidelity in the high-count/high-frequency regime, and only limited gains from denser sampling, oracle keyframes, or prompting. A supplementary Qwen3-VL check and a real-world TransRAC transfer evaluation are also reported. The paper argues that aggregate final-answer scores overstate temporal fidelity and that trace-level evaluation reveals a staged failure of event access, retention, and aggregation.

Significance. If the central claims hold, this is a valuable methodological contribution: it replaces a single aggregate accuracy score with capability surfaces over event count and frequency, ships an executable trace format with timestamp-level evaluation, introduces ACR/RFR to separate unsupported correct answers from faithful traces, and provides unusually transparent cost and sensitivity analyses. The cross-model comparisons and the natural-video transfer check are useful robustness evidence. The paper's independent trace-fidelity findings—especially the 4 FPS result where final accuracy rises but trace F1 collapses to 3.7%—are strong and are not undermined by the sampling concern raised below. However, the headline representation-dependence claim (persistent vs. transient events) is currently under-specified, because the Blinking task does not report pulse duration and the persistent-versus-transient comparison is not isolated from other domain differences.

major comments (3)
  1. [Appendix A.1, §3.1] The Blinking event primitive is defined only as "visual pulse opacities" (Appendix A.1), and the on-pulse duration or duty cycle is never reported. Since Gemini receives approximately 1 FPS (Section 3, Appendix A.2), a pulse substantially shorter than one frame interval can fall entirely between supplied frames, so even a perfect event counter would miss many blinks. The observed absence of a reliable positive-count region for Blinking is therefore compatible with a pure input-sampling ceiling rather than with the paper's claim that "event representation dictates whether a model initially accesses evidence." This is load-bearing because the persistent-State-Machine versus transient-Blinking comparison is the only direct evidence for the representation claim. Please report the blink on-pulse duration in the task specification, state the expected frame-capture probability per (N,F) cell, and ideally add a control that varies event persistence while holding the event schedule and visual salience fixed.
  2. [§3.3, Eq. (5)] The capability boundary is defined using tau=0.80 on 10 trials per cell. A cell with 8 correct answers has a two-sided 95% Wilson interval of roughly [0.49,0.95], so the distinction between a "reliable" and an "unreliable" cell at the reported boundary is not statistically robust. The aggregate Wilson intervals in Appendix B.4 do not cover cell-level boundaries, and the boundary at N=12 at 1.0 Hz for State Machine rests on a single cell at exactly 8/10. Please report per-cell intervals or an explicit robustness analysis over tau (e.g., 0.70, 0.90) and over seed resampling, or soften the wording from "reliably counts" to "achieves at least 80% exact match on the tested renderings." The boundary definitions themselves are reasonable, but the operational language overstates their stability.
  3. [§4, §5, Abstract] The paper explicitly concedes that "the domains also vary visual form and semantics" (Section 4) and "while not isolating persistence from other domain differences" (Section 5), yet the abstract and introduction state the representation claim as causal: "event representation dictates whether a model initially accesses the evidence." The design does not manipulate persistence independently of event duration, visual salience, or semantics. The Appendix keyframe results on Blinking (e.g., Figure 14) are consistent with a large sampling-related component in the Blinking deficit, which makes the missing pulse-duration control more salient. Please either add a matched control (same object and event times, varying only whether the event leaves a persistent state change) or restrict the main claim to a descriptive statement that performance differs across the three controlled domains, with causality attributed to the combination of representation, duration, and sampling.
minor comments (4)
  1. [§7] The text says "We use 151 clips from TransRAC" but also reports "152 TransRAC clips" in the Figure 5 caption; please reconcile the count.
  2. [Figure 2] The Blinking caption says "An object pulsing ON and OFF at 1.0 Hz." If a blink is defined as an ON transition rather than a full ON-OFF cycle, the wording is ambiguous and should be clarified, ideally with the pulse duration stated in the figure or its caption.
  3. [§4, Figure 3 caption] The caption says "Qwen shows the same concentration of accuracy at low counts and frequencies," but the Qwen panel in Figure 3b has no contiguous reliable positive-count region; consider making the caption more precise by noting that Qwen's reliability is limited to isolated low-count cells.
  4. [§5, Table 1] The Part B region definitions (N≤3, N≥5, F≤2.0, F≥2.5) leave the N=4 and F=2.0/2.5 cells unassigned; please state explicitly how those cells are treated or note that they are excluded from the regional aggregation.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper is a measurement study whose headline numbers are computed from model outputs against renderer-produced ground truth, and its self-citations are contextual rather than load-bearing.

full rationale

The paper does not contain a derivation chain in which a prediction is equivalent, by construction, to an input. The central results—accuracy surfaces over N×F, trace precision/recall/F1, ACR/RFR, and intervention deltas—are all measured quantities computed from model responses on newly generated videos paired with executable ground-truth traces. Equations (1)–(6) define evaluation metrics and descriptive reliability boundaries; no parameter is fitted to data and then renamed as a prediction. The trace alignment uses an a priori rate-relative window δ(F)=1/(2F), which is a scoring rule, not a fitted input that determines the outcomes. The only self-references (MORSE-500 and Mementos) appear as related work and do not supply any measured value in the paper. The paper itself states limitations that could affect interpretation—e.g., 'the profiles evaluate complete end-to-end systems' with 'sampling and interface remain confounds', and the State Machine comparison is 'while not isolating persistence from other domain differences'—but these are experimental confounds and reporting gaps, not circular reductions. The unreported blinking pulse duration is a legitimate concern about input undersampling, but it does not make any claim circular. No load-bearing step reduces to its own inputs or to an unverified self-citation, so the appropriate finding is no significant circularity.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The paper's central claims rest on hand-selected thresholds and cutoffs, including the 0.80 reliability target, the 0.80 trace-fidelity cutoff, the rate-relative matching window, and the capability region bins, and on domain assumptions about rendering fidelity, the behavioral meaning of parsed outputs, and the comparability of the three tasks. No new physical or model entities are introduced.

free parameters (5)
  • reliability target tau = 0.80
    Hand-selected operational threshold used to define reliable operating regions and the headline count and frequency boundaries; all 'reliable' claims are conditional on this value.
  • ACR/RFR trace fidelity cutoff = 0.80
    Hand-selected F1 threshold that defines whether a reported trace is 'faithful enough'; Accidental Correctness Rate and Reasoning Failure Rate depend directly on this cutoff.
  • timestamp matching tolerance delta(F) = 1/(2F), from 1.0 s at 0.5 Hz to 0.125 s at 4.0 Hz
    Hand-selected rate-relative window for aligning model timestamps with ground truth; precision, recall, and trace F1 shift with the tolerance, with sensitivity analysis in Appendix C.16.
  • capability region bins = N<=3, N>=5, F<=2.0, F>=2.5 Hz
    Hand-chosen binning of the N by F surface for the regional trace analysis in Table 1 Part B; the reported error-pattern shift depends on these cutoffs.
  • blink on-pulse duration = not reported
    The visual duration of the blink pulse is a renderer parameter that determines how transient the event is; it is never stated in the available text, making the Blinking result hard to interpret without the code.
assumptions (5)
  • domain assumption Manim rendering is frame-accurate with no compression artifacts or timing jitter, so executable traces exactly describe visual events.
    Invoked in Appendix A.1; underpins the validity of timestamp-level alignment.
  • domain assumption Parsed final counts and explicit timestamps are valid behavioral readouts of a model's event bookkeeping.
    All metrics are computed from parsed outputs; the paper acknowledges these are externalized traces, not direct measurements of internal reasoning, in Section 5 and Appendix C.15.
  • domain assumption The three domains differ primarily in event persistence, not in other factors relevant to counting.
    The paper uses State Machine versus Blinking to argue event representation dictates access, while acknowledging visual form and semantics also vary in Section 4.
  • domain assumption The supplied sampling rates, 1 FPS for Gemini and 2 FPS for Qwen, are fixed external input settings.
    Declared in Section 3 and Appendix A.2; the capability surfaces characterize the full input-output pipeline, not model-internal processing.
  • domain assumption The N=0 no-event control is rendered at nominal F=1.0 Hz and is excluded from trace ratios.
    Design choice stated in Section 3.1; it means no-event control accuracy is not directly comparable to frequency-swept cells.

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Cite this review

Pith. "Pith review of The Low Frequency Trap: Video Language Models Fail at Simple Event Bookkeeping." pith.science (2026). https://pith.science/paper/KEQIS47K

@misc{pith2026260806361,
  author       = {Pith},
  title        = {Pith review of: The Low Frequency Trap: Video Language Models Fail at Simple Event Bookkeeping},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KEQIS47K}},
  note         = {Machine review of arXiv:2608.06361}
}
read the original abstract

Real-world video benchmarks provide broad coverage, but their fixed clips entangle event count, rate, duration, and visual complexity, making failure modes hard to isolate. While existing programmatic benchmarks offer better control, they score only the final answer rather than auditing reported events against executable ground truth. To bridge this gap, we introduce trace-grounded parametric profiling for event counting in three controlled video tasks: bouncing-ball wall contacts, visual blinks, and categorical state transitions. Across 2,190 videos, we vary event count N and frequency F while holding rendering fixed. Each video includes an executable event trace for capability-surface estimation and timestamp-level evaluation. Our results reveal a staged temporal failure. At an 80% reliability threshold, Gemini 3.6 Flash reliably counts persistent state transitions up to 12 events at 0.5 and 1.0 Hz, yet demonstrates no reliable positive-count region for transient blinking events. Thus, event representation dictates whether a model initially accesses evidence -- a limitation that compounds as count and frequency increase. In the high-count, high-frequency regime, only 0.2% of final counts are correct and the model recovers just 18.1% of true events. To test if visual access is the primary bottleneck, we increase sampling rate. Although this boosts Bounce Ball accuracy from 19.6% to 29.3%, the reported sequence agrees with ground truth only 3.7% of the time. Extra frames can therefore inflate final scores without producing faithful event recovery. Different prompting strategies yield similarly limited gains, and real-world video evaluations show the same concentration of success at low event counts. Ultimately, trace-grounded profiling shifts video evaluation from aggregate accuracy metrics to a detailed diagnostic of where temporal reasoning fails.

Figures

Figures reproduced from arXiv: 2608.06361 by the authors.

Figure 1
Figure 1. Overview of trace-grounded parametric profiling. Unlike standard benchmark evaluation based on a single aggregate [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Controlled videos and executable traces. For each (N, F), the renderer records event times, state changes, and cumulative count ci for alignment with model-reported timestamps. 3.3 Experimental Protocol and Operating Boundaries Unparseable final answers are incorrect and malformed traces receive no matches. Interventions use the same videos and seeds. Sampling and prompting alter one requested compo￾nent; event-cent… view at source ↗
Figure 3
Figure 3. Capability surfaces across synthetic domains. Final-answer exact match over event count N and frequency F for Bounce Ball, Blinking, and State Machine. Gemini supports the broadest region for persistent State Machine transitions. Qwen shows the same concentration of accuracy at low counts and frequencies. Gemini frequency boundaries. Bounce Ball is generally reliable only through 0.5 Hz, while Blinking has no reliab… view at source ↗
Figures from the paper (25 more)
Figure 4
Figure 4. Figure 4: Bounce Ball interventions. Final-answer exact match averaged over frequencies. Sampling helps low counts, event￾centered frames help through N = 5, and prompting does not expand the reliable region. Keyframe evidence produces the largest recovery. For each ground-truth…
Figure 5
Figure 5. Figure 5: Natural-video count-pattern check. Exact-match accuracy on 152 TransRAC clips (Hu et al. 2022). All prompt￾ing formats concentrate accuracy in low-count bins [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: Bounce Ball interventions. Final-answer exact match averaged over frequencies across visual sampling densities, keyframe evidence, and prompting strategies. 0 2 4 6 8 10 12 Event Count N 0.0 0.2 0.4 0.6 0.8 1.0 Final Answer Accuracy A. Visual Sampling Densities 1 FPS 2…
Figure 7
Figure 7. Figure 7: Blinking interventions. Final-answer exact match averaged over frequencies across visual sampling densities, keyframe evidence, and prompting strategies. 0 2 4 6 8 10 12 Event Count N 0.0 0.2 0.4 0.6 0.8 1.0 Final Answer Accuracy A. Visual Sampling Densities 1 FPS 2 FP…
Figure 8
Figure 8. Figure 8: State Machine interventions. Final-answer exact match averaged over frequencies across visual sampling densities, keyframe evidence, and prompting strategies [PITH_FULL_IMAGE:figures/full_fig_p016_8.png]
Figure 9
Figure 9. Figure 9: Qwen3-VL-8B-Instruct under denser frame sam￾pling (10 FPS). Exact-match capability surfaces across tem￾poral counting tasks under 10-FPS supplied-frame prepro￾cessing. The figure provides an open-model counterpart to the visual-access interventions. quantitative trace …
Figure 11
Figure 11. Figure 11: Qwen3-VL family capability heatmaps. Final-answer exact match across event count and frequency for State Machine transitions. The panels compare Qwen3-VL-8B, Qwen3-VL-32B, and Qwen3-VL-235B [PITH_FULL_IMAGE:figures/full_fig_p021_11.png]
Figure 12
Figure 12. Figure 12: InternVL3.5 family capability heatmaps. Final-answer exact match across event count and frequency for State Machine, Blinking, and Bounce Ball. Columns compare the evaluated InternVL3.5 scales [PITH_FULL_IMAGE:figures/full_fig_p022_12.png]
Figure 13
Figure 13. Figure 13: Complete Gemini visual-access intervention surfaces on Bounce Ball. Final-answer exact-match accuracy for native video, supplied frame-sampling densities, and event-centered keyframes [PITH_FULL_IMAGE:figures/full_fig_p023_13.png]
Figure 14
Figure 14. Figure 14: Complete Gemini visual-access intervention surfaces on Blinking. Final-answer exact-match accuracy for native video, supplied frame-sampling densities, and event-centered keyframes [PITH_FULL_IMAGE:figures/full_fig_p024_14.png]
Figure 15
Figure 15. Figure 15: Complete Gemini visual-access intervention surfaces on State Machine. Final-answer exact-match accuracy for native video, supplied frame-sampling densities, and event-centered keyframes [PITH_FULL_IMAGE:figures/full_fig_p025_15.png]
Figure 16
Figure 16. Figure 16: GPT-5.6 Sol capability surfaces on Blinking. Final-answer exact-match accuracy under 1–2 FPS sampling densities, oracle keyframes, and 2-FPS prompting interventions. 0 1 2 3 4 5 6 8 10 12 Event Count N 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 Event Frequency F (Hz) 1.00 1.00 1…
Figure 17
Figure 17. Figure 17: GPT-5.6 Sol capability surfaces on State Machine. Final-answer exact-match accuracy under 1–2 FPS sampling densities, oracle keyframes, and 2-FPS prompting interventions [PITH_FULL_IMAGE:figures/full_fig_p026_17.png]
Figure 18
Figure 18. Figure 18: Faithful Event Recovery (Part 1: Cases 1–3): Gemini 3.6 Flash accurately tracks all key event timestamps and running counts on blinking, bounce_ball, and state_machine, achieving 100% Trace F1 and matching final integer counts [PITH_FULL_IMAGE:figures/full_fig_p027_18.png]
Figure 19
Figure 19. Figure 19: Faithful Event Recovery (Part 2: Cases 4–5): Additional faithful event recovery profiles on state_machine across low-frequency operating conditions (N ≤ 12, F ≤ 1.0Hz) [PITH_FULL_IMAGE:figures/full_fig_p028_19.png]
Figure 20
Figure 20. Figure 20: Missed Events / Under-Reporting (Part 1: Cases 1–3): Under high temporal load (N ≥ 2, F ≥ 1.5Hz), Gemini omits intermediate event transitions on blinking and bounce_ball, reporting only a fraction of true timestamps (Trace Recall < 60%) [PITH_FULL_IMAGE:figures/full_…
Figure 21
Figure 21. Figure 21: Missed Events / Under-Reporting (Part 2: Cases 4–5): Severe recall degradation under compressed inter-event timing on state_machine [PITH_FULL_IMAGE:figures/full_fig_p030_21.png]
Figure 22
Figure 22. Figure 22: Hallucinated Events / Over-Reporting (Part 1: Cases 1–3): Spurious event generation on blinking and bounce_ball. Gemini logs non-existent boundary collisions and state transitions during continuous motion, degrading Trace Precision (P < 50%) [PITH_FULL_IMAGE:figures/…
Figure 23
Figure 23. Figure 23: Hallucinated Events / Over-Reporting (Part 2: Cases 4–5): Over-reporting profiles on bounce_ball and state_machine [PITH_FULL_IMAGE:figures/full_fig_p032_23.png]
Figure 24
Figure 24. Figure 24: Wrong Accumulation / Reasoning Failure Ratio (Part 1: Cases 1–3): Disconnect between trace maintenance and final answer output on blinking, bounce_ball, and state_machine. Gemini logs an accurate event ledger (F1 ≥ 80%), but miscalculates the final integer aggregation…
Figure 25
Figure 25. Figure 25: Wrong Accumulation / Reasoning Failure Ratio (Part 2: Cases 4–5): Additional RFR instances on state_machine isolating arithmetic aggregation failures despite faithful trace records [PITH_FULL_IMAGE:figures/full_fig_p034_25.png]
Figure 26
Figure 26. Figure 26: Accidental Correctness / ACR (Part 1: Cases 1–3): Unfaithful final counting on blinking and bounce_ball. The final integer prediction matches ground truth (yˆ = N), but the intermediate reasoning trace is severely degraded (F1 < 40%) [PITH_FULL_IMAGE:figures/full_fig…
Figure 27
Figure 27. Figure 27: Accidental Correctness / ACR (Part 2: Cases 4–5): Unverified correct answers on state_machine masking underlying temporal perception failures [PITH_FULL_IMAGE:figures/full_fig_p036_27.png]
Figure 28
Figure 28. Figure 28: Temporally Displaced Events (Part 1: Cases 1–3): Timestamp boundary offset on blinking. Gemini detects event occurrences, but reported seconds fall outside the 1.0s tolerance window relative to ground truth [PITH_FULL_IMAGE:figures/full_fig_p037_28.png]
Figure 29
Figure 29. Figure 29: Temporally Displaced Events (Part 2: Cases 4–5): Temporal boundary drift profiles on blinking and state_machine [PITH_FULL_IMAGE:figures/full_fig_p038_29.png]

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Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.