Source-linked AI summary
PhysWeep: Does a Video Generator Realize the Physics You Ask For?
Rasul Khanbayov, Hasan Kurban
TL;DR
Plausibility scores cannot determine whether a generator realizes the physical parameter it was asked to produce. PhysWeep audits that question with fixed, label-free parameter recovery, finding seed-selected wrong dynamics in two of three generators when motion is trackable.
Problem
Plausibility benchmarks assess whether motion looks physical, but do not measure whether a conditioned parameter is realized in the pixels.
Source: §1 · pp. 1–2Method
Results
Conditional on trackable motion, two of three generators converge to a few fixed, wrong values selected by sampling seed rather than request, reproducibly across model families, axes, and trackers.
Source: §1 · p. 2, §8 · p. 13Takeaways & Limitations
Seed-averaged benchmarks can hide samples locked to wrong constants, so seed-resolved reporting is needed to reveal this failure pattern.
Source: §1 · p. 2, §8 · p. 13Trackability never exceeds 80% and is 0% on ten of eighteen pairs, leaving behavior where no generation clears the fit gate unresolved.
Source: §7 · p. 12
Abstract
from arXiv · showhide
Image-to-video generators are often credited with absorbing physical dynamics as implicit world models, a claim the community currently checks with plausibility scores that ask whether a clip looks consistent with real-world motion. Plausibility is the wrong test on its own, because a clip can look natural while encoding the wrong value of the governing physical parameter, and no existing benchmark measures this gap directly. PhysWeep closes it with a fixed, label-free audit, treating a frozen generator as a black box, recovering the realized parameter from generated pixels, and reporting how often generation is trackable at all, how far the realized value sits from the requested one, and which, if either, of the literature's two proposed failure mechanisms the data support. A deterministic-simulator positive control confirms every score is exactly checkable. Applied to three open generators across six sweep axes, PhysWeep finds a specific, reproducible, previously undocumented failure. Conditional on producing trackable motion, two of the three generate confident, well-fit dynamics that converge to one of a small number of fixed, wrong values selected by the sampling seed rather than by the request, reproducing across two independent model families, two physical systems, and an independent tracker. It matches neither the prior reversion nor the case-based clamping the literature anticipates, because the reversion target is seed-conditional rather than a single global default, and a leave-one-out selection rule rejects both; the in-range faithfulness slope is statistically indistinguishable from zero wherever a response is estimable at all. A benchmark averaging over seeds would never see this: each sample is confidently locked to a wrong constant, exactly the failure a plausibility score is structurally blind to. We release the protocol, suite, and analysis code.
1 Introduction
Plausibility scores ask whether motion looks physical, but not whether a generator realizes the parameter its conditioning requests. PhysWeep proposes a fixed, label-free audit and reports that sampling seeds can select wrong dynamics despite smooth-looking, trackable motion.
1 Introduction
- Plausibility can miss parameter errors: locally consistent motion may encode the wrong physics, motivating measurement of whether realized values follow conditioning.The paper also asks whether this holds beyond values common in training.Source: §1 · pp. 1–2
- PhysWeep treats a frozen generator as a black box, recovers realized parameters from pixels, and measures faithfulness, out-of-range behavior, and failure form across six sweep axes.Its contribution is a swept control parameter and continuous recovery readout, rather than a fixed default and binary or judge score.Source: §1 · p. 2
- Averaging over seeds hides that each sample can be confidently locked to a wrong constant, a failure plausibility scores do not test.The paper frames the missing measurement as whether a conditioned parameter is realized in pixels.Source: §1 · p. 2
- PhysWeep releases deterministic parameterized systems, a fixed label-free estimator, analysis code, and metrics for testing faithfulness and competing failure forms.The estimator is checked against simulator video as a positive control.Source: §1 · p. 2, Figure 1 · p. 3
- Sampling seed, not request, picks the physics: CogVideoX gravity and DynamiCrafter elasticity stay flat across requests while separating by seed.Figure 1 reports 80% in-range trackability for CogVideoX and 25.0% for DynamiCrafter.Source: §1 · p. 2, Figure 1 · p. 3
- The audit formalizes why seed-conditional defaults can make both prior-reversion and case-clamping tests reject the data, motivating a seed-aware statistic.The paper notes that the signature also arises from noise, so PRI alone cannot establish the mechanism.Source: §1 · p. 3
2 Related work
Prior work measures whether generated video looks physical and has also recovered physical quantities from pixels. PhysWeep distinguishes its question—whether a requested parameter controls the realized value—from work measuring fixed-law defaults.
2 Related work
- Many benchmarks rate physical appearance using human or learned judges, law taxonomies, conservation measures, valid-versus-invalid likelihoods, or VLM proxies.These approaches ask whether a clip looks right, not whether it realizes a requested parameter.Source: §2 · p. 3
- The closest pixel-readout studies recover falling dynamics or effective gravity, but fix the target law to gravity and characterize default behavior rather than swept conditioning.PhysWeep describes the questions as complementary: whether realized values move with requests across six sweep axes.Source: §2 · pp. 3–4
- Prior work links sampling seed to generated appearance; PhysWeep extends that connection to quantitative dynamics, which the paper says had not previously been shown.The paper also positions its fixed measurement as a way to re-check physics-controllability claims without training access.Source: §2 · p. 4
3 Problem: plausibility versus parameter faithfulness
The paper distinguishes physical plausibility from parameter faithfulness: a clip may look physical while encoding the wrong value. It defines a black-box recovery setup and contrasts two proposed out-of-range behaviors.
3 Problem: plausibility versus parameter faithfulness
- A generator receives conditioning rendered from a known law’s trajectory, produces video, and an external estimator recovers the realized parameter from pixels.The setup assumes no access to generator weights, data, or activations.Source: §3 · p. 4
- Parameter faithfulness requires the recovered value to remain within ε of the request in expectation over initial conditions, at every value in the sweep.The definition applies on a specified sweep subset.Source: §3 · p. 4
- Plausibility scores the video itself independently of the requested parameter, so smooth, physical-looking motion can still encode the wrong value.The two criteria come apart when the video is locally smooth but parameter-inaccurate.Source: §3 · p. 4
- Prior reversion predicts a blend toward one fixed default, while case clamping predicts the nearest seen value; both treat the realized value as a function of the request alone.The hypotheses differ when the default and nearest in-range edge do not coincide.Source: §3 · p. 4
4 Metrics
PhysWeep combines recovery, trackability, and failure-form metrics with uncertainty controls. Its seed-conditional-default analysis derives why request-only failure models can reject the observed pattern and introduces a seed-aware statistic.
4 Metrics
- Parameter Recovery Error measures normalized recovery error and Extrapolation Gap compares splits; lower PRE is better, and EG ≈ 0 means extrapolation matches interpolation.PRE = 0 denotes perfect faithfulness.Source: §4 · p. 5
- Trackability is reported as the fraction clearing the R² ≥ 0.8 fit gate, while the estimator’s interpretation depends on trackability and identifiability conditions.Low-fit generations are treated as non-physical rather than assigned a parameter estimate.Source: §4 · p. 5
- The in-range faithfulness slope is near 1 for a faithful model and near 0 when conditioning is ignored; Spearman’s ρ captures preservation of request ordering.The rank measure accommodates responses whose scale is off or nonlinear.Source: §4 · p. 5
- Prior-Reversion and Case-Based indices are compared using leave-one-θ-out R² and ΔAIC, and neither is reported if both fail held-out testing.The default and edge values are reported rather than assumed.Source: §4 · p. 5
- The protocol separates bias from within-condition dispersion, uses scale-invariant ratios, and reports 95% bootstrap confidence intervals over parameter-seed pairs.At least five seeds are sampled per condition.Source: §4 · p. 5
4.1 Seed-conditional defaults
- A seed-conditional default models recovered values as a seed-specific constant plus zero-mean noise; a single global prior default is its special case.The crossed design runs every grid value with every seed, making seed independent of the request.Source: §4.1 · p. 6
- Under seed-conditional defaults, the pooled prior-reversion fit tends toward PRI = 1 and its implied default is a population mean; request-only predictors have population R² = 0.The selection rule therefore tends to reject both hypotheses; at high noise, these indices cannot distinguish seed-conditional defaults from unstructured noise.Source: §4.1 · p. 6
- The Seed Determination Index partitions recovered-value variance between requested parameter and seed; on synthetic ground truth, SDI is 0.999 for seed-locking and 0.000 for faithful output or pure noise.The released implementation reports SDI = 0.060 for a single global default and refuses to guess below two seeds.Source: §4.1 · p. 6
5 Suite, pipeline, and protocol
PhysWeep uses deterministic systems and a fixed, label-free estimator to audit frozen generators against known physical parameters. Its protocol separates trackability from parameter recovery and pre-commits how competing outcomes will be evaluated.
5 Suite, pipeline, and protocol
- The minimalist, high-contrast scenes support robust tracking and identifiability, but sit far from the tested models’ training distributions.Five systems supply six axes, with the pendulum contributing two.Source: §5 · pp. 6–7
- Five deterministic renderers provide six sweep axes with closed-form, label-free recovery from tracked statistics.The suite includes ground-truth parameters, and its measurement pipeline uses a fit-quality gate to mark non-physical generations rather than silently dropping them.Source: §5 · p. 6, Table 1 · p. 7, Figure 2 · p. 8
- For each parameter and seed, the pipeline renders conditioning frames, generates a continuation, tracks the motion, and fits the governing law to recover the realized parameter.The generator is the only untrusted stage; a fit with R^2 below 0.8 is excluded as non-physical, with the rate reported alongside results.Source: §5 · p. 7, Figure 2 · p. 8
- Text-specified conditioning names a regime in a prompt, while frame-implied conditioning supplies motion that already encodes the parameter.Only one tested model admits both channels, and the reported results identify which channel each uses.Source: §5 · p. 7
- The evaluation pre-commits four possible outcomes, including faithful or unfaithful conditioning and whether either anticipated failure mechanism fits.It tests three frozen open generators, uses at least five seeds per parameter-model pair, and reports bootstrap intervals, held-out R^2, and ΔAIC.Source: §5 · pp. 7–8
- The reported protocol deviations leave the main results using text-specified conditioning, because the LTX-Video multi-frame pipeline was incoherent after patching and CogVideoX accepts one conditioning image.DynamiCrafter512 replaced Stable Video Diffusion in the tested set.Source: §5 · p. 7
6 Results
Across the estimable cases, none of the three generators detectably honors text-specified physical parameters; two produce well-fit motion locked to seed-specific wrong values. These outputs are reproducible across architectures and systems, while neither prior reversion nor case clamping explains the adjudicated pairs.
6 Results
- The positive control recovers the swept parameter, with PRE(S_in) = 0.0031, β = 1.0001, and mean fit R^2 = 0.986 across 203 measurements.The simulator-video continuations passed through the same tracker and fitter across six axes and both splits.Source: §6 · p. 8
- All five estimable model-axis slopes are indistinguishable from zero and exclude β=1 where the intervals are informative; projectile recovery errors are 0.853, 2.016, and 0.781 for LTX-Video, CogVideoX, and DynamiCrafter.The two inclined-slide intervals also contain β=1, indicating low power rather than evidence for a null; only five of 18 model-axis pairs support slope estimates.Source: §6 · p. 9, Table 2 · p. 9
- CogVideoX and DynamiCrafter produce confident, well-tracked motion whose recovered gravity clusters by sampling seed rather than following the requested value.For CogVideoX, one seed gives ĝ ≈ −11.4 ± 0.03 across requested in-range values 5, 7, 9.8, 12, and 15; another gives −7.1 ± 0.2.Source: §6 · p. 9
- Across bouncing-ball tests, CogVideoX and DynamiCrafter converge to near-unit restitution regardless of the request, reproducing the same wrong-but-confident behavior across two architectures.DynamiCrafter’s recovered restitution is 0.99–1.00 across the full grid, with individual-clip R^2 = 1.000.Source: §6 · p. 10
- DynamiCrafter’s inclined-slide results add a split-default pattern: one seed stays near μ̂ = 1.80, while another forms separate in-range and out-of-range clusters.The second seed’s clusters are μ̂ ≈ 4.7 in-range and ≈ 7.6 out-of-range, so the pattern is neither a single default nor request-following behavior.Source: §6 · p. 10
- All eight adjudicated model-axis pairs select neither prior reversion nor case clamping, although AIC favors prior reversion and PRI is high.Seed-locking is decisively supported by SDI on five pairs, not the remaining three; ten pairs are unadjudicated because of the frame ceiling or near-zero gated yield.Source: §6 · p. 10, Table 3 · p. 11
- The VLM rater’s scores do not track fitted trajectory quality: correlations between fit R^2 and plausibility score are 0.075, 0.019, and −0.164 for LTX-Video, DynamiCrafter, and CogVideoX.A texture-noise clip scored 5/5 while a cleanly tracked fall scored 1/5.Source: §6 · p. 10
- The result persists under tracker swaps, nuisance-parameter changes, and fit-gate thresholds from 0.5 to 0.95, with a tracker-sensitive DynamiCrafter cluster and uninformative CogVideoX short-budget columns.Across variants, LTX-Video slopes range from −0.26 to +1.38 and DynamiCrafter slopes from −0.25 to +0.12.Source: §6 · p. 11
7 Limitations
The evaluation’s conclusions are bounded by limited trackability, incomplete axis coverage, and a suite focused on controllable low-dimensional dynamics. Several model and measurement choices also constrain cross-model comparisons and interpretation of null slopes.
7 Limitations
- Trackability never exceeds 80% and is 0% on ten of eighteen model-axis pairs, leaving behavior on clips that fail the gate unresolved.A contemporaneous study recovered usable trajectories on photorealistic stimuli where this evaluation largely did not.Source: §7 · p. 12
- The tested model set is not current or comprehensive: later releases exist in two tested families, and frequently credited open models were omitted.Wan’s cited 1.3B text-to-video variant may fit the compute budget, but the needed image-to-video checkpoint was not tested.Source: §7 · p. 12
- Four of five slope estimates do not establish equivalence to zero, and native clip length is confounded with model identity, limiting cross-model trackability comparisons.Only LTX-Video/projectile is formally equivalent to zero; the other four estimates are consistent with underpowered tests.Source: §7 · p. 12
- Only 8 of 18 pairs support adjudication; text conditioning dominates, some axes are untestable, and the joint pendulum sweep has 0% trackability.Frame-implied conditioning is untested for LTX-Video and CogVideoX, while DynamiCrafter’s 16-frame output makes three axes untestable.Source: §7 · p. 12
- The plausibility comparison uses one rater and one prompt, while the suite prioritizes identifiability over ecological validity and supports conclusions about low-dimensional controllable dynamics.No human ratings, other prompts, larger VLMs, or VBench-2.0 scores were collected.Source: §7 · p. 12
8 Conclusion
PhysWeep concludes that trackable generations from the tested models do not detectably honor text-specified parameters, with two models instead producing seed-selected wrong values. Its measurement pipeline tests prior reversion and case clamping with held-out selection, while documenting where clamping is difficult to identify.
8 Conclusion
- Conditional on trackable motion, none of the three tested generators detectably honors a text-specified parameter.The paper frames this as parameter faithfulness rather than whether a clip merely appears physical.Source: §8 · p. 12
- Two of three generators converge to a few fixed, wrong values selected by sampling seed rather than request, across model families, axes, and trackers.The seed-locked samples are confidently wrong, so averaging across seeds can hide the failure.Source: §8 · pp. 12–13
A Measurement pipeline and failure-form estimation
- Prior reversion fits a shrinkage line toward a fixed default, whereas case clamping fits a plateau at an in-range boundary.PRI summarizes reversion and CBI summarizes out-of-range variance explained by clamping at the edge.Source: A Measurement pipeline and… · p. 17
- Prior reversion and case clamping are compared by leave-one-parameter-out R^2 and ΔAIC; a hypothesis is selected only with positive held-out R^2.When both fail, the behavior is reported without forcing a mechanism label; all eight adjudicated pairs are reported as neither.Source: A Measurement pipeline and… · p. 17
- When the out-of-range grid lies entirely on one side of the in-range band, the clamp prediction is constant and CBI cannot identify clamping at any sample size.This degeneracy applies to pendulum damping; a two-sided damping grid would be needed to test clamping on that axis.Source: A Measurement pipeline and… · p. 17
- On seed-clustered data, the default clamp edge can yield near-zero CBI, which should be read as clamping not fitting rather than as an identified edge.Clamping is rejected on all eight adjudicated pairs even with a properly chosen edge.Source: A Measurement pipeline and… · p. 18
B Consistency of the recovery estimator: full statement and proof
The appendix states conditions under which the recovery estimator converges to the realized parameter and checks those conditions across the parameterized systems. It also identifies a damping-related bias and uses a fit-quality gate to exclude ill-posed recovery cases.
B Consistency of the recovery estimator: full statement and proof
- The estimator maps tracked observables to an empirical statistic and applies the inverse population statistic to recover the parameter.The statistic is system-specific: projectile height curvature, pendulum zero-crossing interval, or successive bouncing-ball height ratio.Source: B Consistency of the recovery… · p. 18
- Consistency requires trackable observations with shrinking error, an injective statistic with derivative bounded away from zero, and a continuous empirical-statistic functional.These assumptions establish regularity for the recovery estimator, not for the generator.Source: B Consistency of the recovery… · p. 18
- Under trackability, identifiability, and regularity, the stated proposition gives consistency of the recovery estimator.The claim concerns the estimator R, not the generator G.Source: B Consistency of the recovery… · p. 18
- The projectile statistic is linear and identifiable, while the bouncing-ball map from restitution to successive height ratio is injective and smooth.For an undamped pendulum, zero-crossing spacing is monotone in frequency on intervals bounded away from zero.Source: B Consistency of the recovery… · pp. 18–19
- For the damped pendulum, zero-crossing spacing recovers damped rather than natural frequency, producing a biased natural-frequency estimate.The reported bias is below 1.3% on the in-range damping grid, grows with damping, and is materially larger out of range.Source: B Consistency of the recovery… · pp. 18–19
- A fit-quality gate excludes generations with ill-posed recovery, such as when the tracked object morphs or vanishes.Regularity is enforced operationally rather than assumed.Source: B Consistency of the recovery… · p. 19
C Seed-conditional defaults: full statement and proof
Under a crossed design where every requested value is paired with every seed, seed-conditional defaults make request-only faithfulness and mechanism tests fail in predictable ways. The fitted default can be an average that no individual generation realizes, while the leave-one-out rule rejects request-only predictors with a fixed positive margin.
C Seed-conditional defaults: full statement and proof
- The crossing assumption is essential: confounding seeds with grid position made a seed-determined generator appear perfectly faithful in simulation.The confounded simulation returned an estimated slope of 1.006 and PRI = −0.006.Source: C Seed-conditional defaults: full… · p. 19
- With a fixed positive margin, the leave-one-requested-value-out R² selection rule returns neither mechanism with probability tending to one.Every predictor that is a function of the requested parameter alone has population R² = 0 under the model.Source: C Seed-conditional defaults: full… · p. 19
- Under a seed-conditional default, the pooled OLS faithfulness slope converges to zero, while the fitted default converges to the population mean of seed-specific values.When seed-specific values are well separated, that mean need not describe any individual generation.Source: C Seed-conditional defaults: full… · p. 19
- The expected sign of the leave-one-out R² statistic remains unproved; the authors record its apparent non-positive, vanishing behavior as an observation.The stated adjudication instead uses convergence in probability to zero together with a fixed margin.Source: C Seed-conditional defaults: full… · pp. 19–20
- The one-way random-effects variance-share estimate corrects substantial upward bias in the uncorrected estimate under a simulated zero-share condition.The uncorrected estimate was about 0.18 where the truth was 0; the corrected estimate was about 0.07.Source: C Seed-conditional defaults: full… · p. 20
D System grids, seed counts, and rendering
The systems use fixed rendering conditions and seeded initial states, varying only the swept parameter and initial condition within each system. The appendix also documents prompt and guidance variants, run timing, and GPU-hour accounting.
D System grids, seed counts, and rendering
- Each system uses a deterministic 2D renderer with fixed spatial and temporal scales, while the swept parameter and seeded initial condition vary.Scenes use 24 frames at 24 fps, 256 × 256 resolution, a high-contrast disk, and a fixed ground line.Source: D System grids, seed counts, and… · p. 20
- Table A1 assigns one swept axis to each row and uses at least five seeded initial conditions per grid point.The supplied caption does not give the individual grid values.Source: Table A1 · p. 21
- The prompt ablation compares per-system, per-parameter terse templates with verbose templates that add physical-accuracy framing to the default prompt.Both variants are evaluated against the default template.Source: D System grids, seed counts, and… · p. 21
- Classifier-free guidance values of 1.0, 1.5, 4.5, and 9.0 were swept for LTX-Video and CogVideoX on the projectile system, but not DynamiCrafter.DynamiCrafter’s guidance scale was not exposed as a pass-through parameter.Source: D System grids, seed counts, and… · p. 21
- The reported GPU-hour total is an estimate of about 57 hours and excludes unreconstructable retry and gap-fill overhead.54.4 hours came from logged runs; the remaining successful work and plausibility-scoring pass were estimated separately.Source: D System grids, seed counts, and… · pp. 21–22
E Reproducibility checklist
The reproducibility checklist records procedural data generation, frozen public models, seeded runs, statistical procedures, and released code and raw outputs. The accompanying related-work discussion distinguishes parameter-faithfulness measurement from prior mechanisms, property-readout methods, plausibility judgments, and prediction or planning benchmarks.
E Reproducibility checklist
- The checklist reports procedural data, public frozen models, explicit per-clip seeds, statistical procedures, released code, and raw outputs used to recompute reported numbers.Runs use at least five seeds per grid point, with 95% paired-bootstrap intervals and Holm correction across six extrapolation tests.Source: E Reproducibility checklist · p. 22
F Extended related work
- The extended related-work discussion links case-based behavior to Kang et al. [30] and distinguishes parameter faithfulness from property readout, VLM judging, prediction, planning, and visual-quality metrics.The cited approaches measure different targets, including encoder properties, plausibility, prediction or planning, and visual fidelity.Source: F Extended related work · pp. 22–23
- Table A2 records seeds and clip yields by model-axis pair, including targeted increases to out-of-range seeds near the mechanism-selection threshold.LTX-Video/pendulum-ω was extended to 55 seeds per point yet yielded only one gated clip across 110 out-of-range attempts; CogVideoX/bouncing-ball was extended to 33.Source: Table A2 · p. 24
E Reproducibility checklist
- The checklist discloses that model revisions were not explicitly pinned, so each model resolved to its repository’s default-branch head at run time.This is identified as a gap against bit-for-bit reproducibility.Source: Table A3 · p. 25
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