Is the Future Compatible? Diagnosing Dynamic Consistency in World Action Models
1. Paper overview
In one sentence: Agreement among imagined futures can improve action selection without a value model, but predictable or widely shared futures can still represent failure. e02e03e05e07e09e10e19
| At a glance | What to know |
|---|---|
| Research problem | Source description Visual plausibility and terminal success do not establish that an imagined future is compatible with its associated actions. The paper asks whether action-state consistency separates successful from failed executions, when that relationship reverses, and whether consistency can guide decisions without a learned value head. Crucially, checking a prediction against execution and selecting before execution require different information. e02e03e07 |
| Core mechanism | |
| A key reported result | RoboTwin 2.0 Easy bimanual manipulation: Consistency-Consensus 93.0%. Average success rate (%). LingBot-VA reimplementation; Easy setting, fixed initial configuration within each task, 10 trials per task; N = 8. Main text says over 50 tasks; Appendix C refers to 50. Baseline 90.2%, a gain of 2.8 percentage points. Evidence extends the selector to this inverse-dynamics model in simulation. Exploring is absent because the benchmark lacks a state-saving API. These results do not test randomized initial configurations or physical deployment. e08e10e13 |
| Reading caution | Source description Background collapse can preserve appearance while missing task-relevant interactions. It occurs even under consistency-guided selection and can also affect aligned tasks. Appendix G’s mitigation comparison uses execution-based exploration; it does not isolate an early-only consensus intervention. e05e17e18 |
Core contributions
Figure 6. Three selectors differ in where their ranking evidence comes from. Original paper, p. 6 ↗
Excerpt from the authors’ paper; cropped without altering the figure or table.
How to read it. Read each panel from the yellow observed state downward. Blue boxes represent predicted actions; red boxes represent their predicted futures. Panel (a) adds green value predictions and keeps the branch with the largest value. Panel (b) follows every action into an observed state, compares that state with its prediction using the black consistency arrows, and retains the highest score. Panel (c) ranks predictions by agreement with a shared future, then follows the selected blue action into execution. Its weights illustrate ranking: Algorithm 2 takes their argmax. Equation (7) defines an unweighted future mean, and Equation (9) retains one action branch. e07e15e16
What it supports. The deployable change is the source of comparison. Consensus can score all sampled futures before any candidate executes; Exploring needs environment resets and candidate executions. The branch-2 choices and score labels here are schematic examples, not benchmark measurements. Neither agreement nor the selected branch is guaranteed to be successful.
Where the evidence stops. The source has notation slips: p. 7 calls Exploring Figure 6(a), although it is panel (b); the consensus inset labels its center a, unlike Equation (7)’s mean future. Follow Equations (7)–(9) and Algorithm 2; do not infer weighted action averaging from the inset.
2. Motivation
2.1 The problem and the proposed response
Visual plausibility and terminal success do not establish that an imagined future is compatible with its associated actions. The paper asks whether action-state consistency separates successful from failed executions, when that relationship reverses, and whether consistency can guide decisions without a learned value head. Crucially, checking a prediction against execution and selecting before execution require different information. e02e03e07
2.2 What this reading follows
A robot can imagine a plausible scene while choosing actions that never produce it. This paper makes that mismatch measurable: compare the predicted future with what the environment shows after the associated actions execute. It then asks how to use the idea before execution, when the true outcome is unavailable. Its answer is to choose the candidate future closest to the sampled consensus and execute that candidate’s action. The evidence spans two existing WAMs and two simulation benchmarks. Read the gains alongside the background-collapse examples: agreement is useful for ranking, but does not by itself establish progress, correct physics or task completion. e02e03e05e07e09e10e19
3. Research context
We place the paper in the collection through its world–action interface. The catalog labels and the reading’s assessment are shown separately.
| Catalog dimension | Recorded classification |
|---|---|
| Major category | Evaluation metrics |
| Architecture | Not applicable |
| Prediction paradigm | Not applicable |
| Quadrant | Not applicable |
3.1 Evidence-based assessment
Supports the recorded classification
The recorded evaluation-metrics classification fits a paper centered on a consistency score, diagnostic protocols and inference-time selection. It examines both joint-prediction and inverse-dynamics WAMs; it does not propose one architecture whose components justify a One Model/Two Models quadrant. The catalog’s architecture, paradigm and quadrant marked Not applicable are therefore appropriate for this contribution. e02e03e07
This is the collection’s architectural analysis, not a new related-work survey. Benchmark comparisons and their protocols appear in Section 6.
4. Problem formulation
4.1 Inputs and outputs
| Inputs | Outputs |
|---|---|
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4.2 Equations and their role
5. Method in detail
5.1 First measure the consequence of an action
Begin with an observation history, proprioception and a task instruction. The existing WAM generates an action/future pair, either jointly or by predicting a future and inferring its action. Execute the action sequence and compare the realized observation with the prediction at the same horizon. Equation (3) converts latent MSE into a score that rises as the two agree. This measurement therefore needs an executed outcome; it is not available merely because a video looks plausible. For diagnostic analysis, the authors normalize episode scores within each task and compare outcome groups. Their classifier is evaluated only on tasks with the expected direction of separation. That restriction matters: a favorable AUC describes the selected diagnostic regime, while the background-collapse examples expose where the regime fails. e02e03e04e05
5.2 Replace the unavailable outcome with a consensus reference
At a real decision point, testing every candidate would require executing competing actions and restoring the initial state. The paper’s Exploring procedure does this in a resettable environment, but Consensus instead creates a reference from the sampled futures themselves. Equation (7) takes their arithmetic mean; Equations (8)–(9) score agreement and choose one associated action. Algorithm 2’s softmax does not change the winner at positive temperature. Reader interpretation: this replaces external verification with an assumption about the candidate distribution. It is promising when predictions cluster around a useful future, but weak when many samples share the same mistake or represent genuinely different feasible outcomes. The source’s observation notation and latent-distance definition also leave the exact averaging/encoding order unresolved, which matters for implementation and the two-candidate case. e03e07e15e16e18
5.3 Judge the controller through both success and failure structure
The two benchmark tables establish executed task success in simulation, not just image similarity. Consensus improves the reported averages over each reimplemented base model, and Appendix F favors selecting one action over mixing candidates. Yet the diagnostic and controller need separate scrutiny. Background collapse makes an unfinished scene easy to predict, while consensus can reward a common but wrong future. Appendix G studies an exploration-based mitigation and Appendix H still shows failures after selection, so neither warrants claiming that consensus solves collapse. Reader interpretation: the strongest next evaluation would use shared candidate pools, matched initial states and explicit motion strata. This would reveal whether the gain comes from better discrimination among actions, whether it survives low-motion regimes, and whether it remains meaningful under repeated trial seeds. e05e09e10e16e17e18e19
5.4 Training and inference
During training
The selector adds no WAM fine-tuning or reward-model training. The source uses pretrained Cosmos-Policy and LingBot-VA and refers to their official inference/evaluation setups instead of restating their training recipes. e01e02e12
A logistic-regression classifier is trained with five-fold cross-validation for diagnostic analysis. It is separate from the consensus controller. e04e07
During inference
Consistency-Exploring resets to the same state, executes each candidate, scores prediction against realization, resets again, and commits to the winner. The authors treat it as an upper-bound reference requiring privileged environment access; it is not generally available for physical robots. e07e15
Consistency-Consensus uses sampled predictions before committing to one branch. Algorithm 2 inserts softmax weights before argmax; for positive temperature this preserves score ordering. Appendix F confirms winner-takes-all execution rather than weighted action averaging. e07e15e16
5.5 Implementation flow
- Obtain action/future pairs
Cosmos-Policy on RoboCasa represents joint future-observation/action prediction. LingBot-VA on RoboTwin 2.0 represents future prediction followed by inverse-dynamics action inference. Section 2 conditions these predictions on observation history, proprioception and task specification over horizon Δ. This paper studies existing pretrained models; it does not introduce a shared architecture for both. e02
- Measure agreement after execution
Execute the predicted actions and compare the realized observation with the predicted observation at the same horizon. The distance is MSE in the latent space before VAE decoding, converted to a decreasing exponential score. The authors use α = 0.1 and interpret scores as relative rankings, not calibrated success probabilities. e03
- Analyze outcomes and motion
Convert episode-level consistency to a within-task z-score, pool scores, and compare successes with failures. Logistic regression evaluates the diagnostic on consistency-aligned tasks. Separately, temporal latent change measures scene dynamics; small changes can make unsuccessful static predictions deceptively consistent. e03e04e05e14
- Select using a future consensus
Sample N candidate pairs, average their predicted futures, score each future against that mean, and execute the highest-scoring candidate’s action. The mean is written in observation notation, while the distance is defined in latent space; encoding versus averaging order is not fully specified. Repeated decision steps use the current observation as feedback. e03e07e15
6. Experiments & results
This paper measures whether a world action model’s predicted future matches the observation produced by executing its actions, then uses agreement among sampled futures as a value-free selection proxy. Tests on Cosmos-Policy and LingBot-VA show modest simulated control gains, while background collapse demonstrates that predictable futures can still be unsuccessful. The contribution is a reliability diagnostic and inference procedure, rather than a newly trained WAM.
6.1 Read the original evidence
Table 1. Consensus comes close to the value selector in the reported RoboCasa evaluation. Original paper, p. 8 ↗
Excerpt from the authors’ paper; cropped without altering the figure or table.
How to read it. Start below the dashed divider, where the paper compares its reimplemented Cosmos-Policy baseline and three selectors. TTS means test-time scaling; the default is eight candidates. Asterisks mark reimplementations, as stated in the original table caption. The unstarred earlier methods above the divider are values taken from reference [22], not reruns of every method by these authors. The last column is average task success in percent. Section 5.1 specifies 24 kitchen tasks, 50 trials per task and a single Franka Emika Panda arm. Read the Exploring row with its additional environment-execution access in mind. e07e08e09
What it supports. Consensus raises average success from 66.6% to 67.3%, a 0.7-percentage-point gain, and sits 0.1 point below value-based selection. Exploring reaches 68.0%. These numbers support a modest practical benefit for the consensus proxy while showing why the highest row cannot be treated as an equally deployable selector.
Where the evidence stops. No confidence intervals or paired significance tests accompany these rates. Exploring executes competing branches from reset states, so its information budget differs from Consensus. Imported baseline values provide context without establishing a fully controlled comparison across all listed methods.
Table 2. The larger reported gain appears in RoboTwin 2.0’s fixed-configuration Easy setting. Original paper, p. 8 ↗
Excerpt from the authors’ paper; cropped without altering the figure or table.
How to read it. Focus on the two rows below the dashed divider: the starred LingBot-VA reimplementation and the authors’ Consistency-Consensus extension. The star denotes a reimplementation; the earlier baseline values come from reference [25]. TTS marks additional sampling at inference. Section 5.2 uses ten trials per task under Easy, with a fixed initial configuration within each task, and the default selector samples eight candidates. LingBot-VA supplies the inverse-dynamics representative, so this comparison tests the selector with a different action-generation formulation from Cosmos-Policy. There is no Exploring row because RoboTwin 2.0 lacks the state-saving API needed to evaluate it. e02e08e10e13e19
What it supports. Average success rises from 90.2% to 93.0%, a 2.8-percentage-point improvement over the same reimplemented base model. The result supports the usefulness of future consensus in this bimanual simulation setting without a value head. It does not measure performance under randomized initial configurations or physical execution.
Where the evidence stops. The task count is not stated consistently: Section 5.2 says over 50, while Figure 10 refers to 50. Preserve that uncertainty in a reproduction. Ten trials per task and no reported uncertainty interval limit precision.
6.2 Results and evaluation conditions
| Task & protocol | Reported result | Comparison & interpretation |
|---|---|---|
| RoboCasa kitchen manipulation Cosmos-Policy reimplementation; 24 tasks, 50 trials per task, single Franka Emika Panda arm; default N = 8. | Consistency-Consensus 67.3%; Consistency-Exploring 68.0%. Average success rate (%) | Baseline 66.6%; value-based selection 67.4%. Consensus gains 0.7 percentage points over baseline. Consensus approximately matches the value selector numerically. Exploring requires candidate execution/reset access. No uncertainty interval establishes that these small differences are significant; other table baselines are imported from prior work. e08e09 |
| RoboTwin 2.0 Easy bimanual manipulation LingBot-VA reimplementation; Easy setting, fixed initial configuration within each task, 10 trials per task; N = 8. Main text says over 50 tasks; Appendix C refers to 50. | Consistency-Consensus 93.0%. Average success rate (%) | Baseline 90.2%, a gain of 2.8 percentage points. Evidence extends the selector to this inverse-dynamics model in simulation. Exploring is absent because the benchmark lacks a state-saving API. These results do not test randomized initial configurations or physical deployment. e08e10e13 |
| Success/failure classification from consistency Within-task normalized episode scores; logistic regression with five-fold cross-validation on consistency-aligned tasks only. | Cosmos-Policy 0.77; LingBot-VA 0.88. ROC AUC | Chance AUC 0.5; reported distribution effect sizes are Cohen’s d = 0.76 and 0.99 respectively. These are retrospective outcome-separation results, not all-task or pre-execution success guarantees. Figure 11 also excludes misaligned tasks. e03e04e13 |
| Winner-takes-all versus weighted action aggregation Appendix F consensus-selector comparison on Cosmos-Policy and LingBot-VA. | Winner-takes-all: 67.3% and 93.0%, respectively. Average success rate (%) | Softmax-weighted action averaging: 64.9% and 86.4%. The reported ablation favors keeping one motion hypothesis. It does not isolate rotation geometry from multimodal action mixing; the weighted baseline’s temperature is unspecified. e16 |
| Scaling the number of consensus candidates Consistency-Consensus with N = 1, 2, 4, 8 in the two benchmark/model settings. | Cosmos-Policy: 66.6, 66.7, 67.1, 67.3; LingBot-VA: 90.2, 92.0, 92.8, 93.0. Average success rate (%) | N = 1 is the single-candidate reference. The plotted rates increase with N, without error bars or evidence beyond eight candidates. A two-candidate tie under same-space MSE averaging requires an implementation check before attributing every gain to ranking. e03e07e11 |
6.3 Ablations and diagnostic examples
Read component removals and qualitative examples within their stated evaluation conditions.
Figure 3. A failed rollout may score well because its future becomes easy to predict. Original paper, p. 5 ↗
Excerpt from the authors’ paper; cropped without altering the figure or table.
How to read it. Compare the Real and Predicted rows vertically at each labeled time, then move horizontally to the late frames. Both panels depict failures. In panel (a), the blue box marks disagreement while the scene continues to change. In panel (b), an early mismatch is followed by nearly static late imagery, highlighted in red. The labels aligned and misaligned concern whether successful episodes have higher consistency than failures within a task; they are not success labels for these examples. Equation (4) and Figure 4 on this page provide the complementary diagnostic: temporal latent change measures how much the scene evolves. e03e05e14e17e18
What it supports. The visual explains why a high score need not indicate useful behavior. If both prediction and realized scene barely change, agreement becomes easy despite an unfinished task. The negative motion–consistency correlations in Appendix D support the confound, while these selected frames illustrate its appearance rather than quantify its prevalence.
Where the evidence stops. These are illustrative failures, not a controlled estimate of collapse frequency. Appendices H–I show that collapse survives consistency-guided selection and also occurs in aligned tasks. Appendix G’s mitigation uses execution-based exploration, so it does not establish an isolated early-consensus intervention.
Figure 8. Reported success rises with candidate count; both models have a smaller final increment. Original paper, p. 9 ↗
Excerpt from the authors’ paper; cropped without altering the figure or table.
How to read it. Read the horizontal labels as the candidate count N, written n in the plot. The vertical scale and point labels give success rates in percent; its lower bound is 65, not zero. Teal corresponds to Cosmos-Policy and red to LingBot-VA. Compare each line with itself because the two models use different benchmarks and protocols. N = 1 supplies the single-candidate reference, followed by two, four and eight candidates selected through consensus. Section 5 reports parallel GPU evaluation, so candidate count is a sampling budget rather than a direct measurement of latency on a single device. e03e07e08e11e15
What it supports. The plotted Cosmos-Policy sequence is 66.6, 66.7, 67.1 and 67.3%; LingBot-VA is 90.2, 92.0, 92.8 and 93.0%. Larger candidate pools accompany higher reported success, with smaller final increments. The figure supports the tested range only and does not establish gains beyond eight candidates or a compute-optimal setting.
Where the evidence stops. Reader deduction: if averaging and MSE operate in the same latent space, two candidates are equidistant from their mean. The source leaves averaging order and tie-breaking unclear. Thus the N = 2 improvement needs implementation verification; no error bars distinguish sampling variation.
Table 4. Keeping one candidate action outperforms averaging the candidate actions. Original paper, p. 18 ↗
Excerpt from the authors’ paper; cropped without altering the figure or table.
How to read it. Read each model group as a comparison between two ways to turn the same type of consistency score into a command. Weighted refers to Equation (10): transform scores using a temperature-scaled softmax and average candidate actions. WTA means winner-takes-all: retain the action from the highest-scoring branch. It still permits an average of predicted futures for scoring; it avoids averaging actions at the final decision. Appendix F gives two reasons this distinction may matter: rotation-related command components need not be globally Euclidean, and different candidates can encode incompatible approach or grasp hypotheses. e07e15e16
What it supports. For Cosmos-Policy, WTA scores 67.3% against 64.9% for weighted actions; for LingBot-VA, it scores 93.0% against 86.4%. These gaps favor retaining one sampled motion hypothesis in the reported implementations. They also confirm that the paper’s final controller is a branch selector, even though softmax weights appear in its algorithm.
Where the evidence stops. The ablation does not separate geometric invalidity from mixing distinct motion hypotheses. The weighted baseline’s temperature and detailed action representation are not specified here. It therefore does not rule out every geometry-aware or otherwise constrained aggregation method.
7. Analysis & limitations
7.1 What the evidence leaves open
Background collapse can preserve appearance while missing task-relevant interactions. It occurs even under consistency-guided selection and can also affect aligned tasks. Appendix G’s mitigation comparison uses execution-based exploration; it does not isolate an early-only consensus intervention. e05e17e18
The consensus proxy assumes that averaging sampled futures improves the reference prediction. Correlated errors or distinct valid action-conditioned futures can invalidate that assumption. A common prediction is not independent evidence of physically correct dynamics. e07e18
Aligned-task selection limits the AUC’s scope. The source does not say whether task alignment and normalization were recomputed inside each training fold. Figure 5 shows outcome-conditioned value/consistency gaps, not a formal calibration curve, despite the authors’ calibration language. e03e04e06e13
Results omit confidence intervals and repeated evaluation-seed summaries. The RoboTwin task-count wording differs between main text and appendix. Appendix A defers implementation details to external model setups, leaving exact checkpoints, software versions, horizon, latent reductions and episode aggregation insufficiently specified here. e08e09e10e12e13
7.2 Questions for discussion
- Would outcome separation survive evaluation on all tasks with fold-local normalization and task selection?
- When do multiple plausible action-conditioned futures make their mean an unreliable selection target?
- Can consistency and motion diagnostics distinguish productive interaction from predictable stalling without reward supervision?
8. Reproducibility audit
8.1 Requirements and known gaps
Reproduction needs the referenced pretrained models, benchmark versions, task lists, trial seeds and matching observation/action interfaces. The source reports eight NVIDIA RTX 5090 GPUs, N = 8, and about 0.7 ms for score/weight computation only. Its small wall-clock-overhead claim depends on parallel candidate evaluation; serial runtime is said to grow approximately linearly with N. e08e12
Clarify latent encoding/averaging order, tie-breaking and executed action indexing before implementing the selector. Equations (6)/(9) use action index t, while Algorithms 1–2 end at t+Δ after sampling a sequence. A proposed two-candidate unit check and a motion-stratified benchmark comparison are specified in the illustrated edition; neither has been run. e03e07e15
8.2 Proposed reproduction checks
The following checks are proposals motivated by the paper. They have not been run as part of this reading.
Check 1: Resolve the two-candidate tie and averaging domain
Reader-proposed check, not performed: expose two fixed predicted futures from one state and trace exactly where the mean and MSE are computed. If both use the same latent representation, the two scores must tie up to floating-point error. Compare direct argmax, Algorithm 2’s softmax-argmax, reversed candidate order and a uniform tie-breaker. Replay the same candidate pools under each choice. A stable nonzero score gap would falsify that same-space interpretation and require a documented encoding, reduction or scoring difference. Only then compare the reported N = 2 rates with a matched random-candidate control. e03e07e11e15
Check 2: Test whether consensus discriminates progress from predictable stalling
Reader-proposed check, not performed: on paired RoboCasa initial states, sample a shared pool of eight candidates and compare consensus WTA, uniform candidate selection and reset-based Exploring. Record executed success, prediction–realization consistency and temporal latent change. Include every task, define motion strata and alignment using separate development episodes, and report paired uncertainty across evaluation seeds. Consensus should beat the matched random selector within informative strata if it ranks useful actions. Concentrated failures with high agreement and low motion would instead confirm the collapse boundary. This comparison also separates proxy quality from the benefit of privileged candidate execution. e03e04e05e07e08e09e14e17e18
8.3 Reading coverage
Visual audit: The title/authors/version, all 16 figures, all four tables, and Algorithms 1–2 were visually inspected. Pages 2–3 establish the model formulations and metric; 4–7 establish diagnostics and selection; 7–9 establish compute and evaluation; 14–18 cover implementation, task-level analysis, algorithms and ablation; 19–21 cover residual failures and qualitative examples. All six final crops were inspected with their original labels, legends and table entries intact. Long captions are excluded from crops; reimplementation markers and baseline provenance are explained in the reading guides. The Figure 6 panel-reference and inset-notation issues are disclosed above. References and appendix contents were read as text. Separate supplement availability remains unverified.
PDF pages inspected for this edition: 1, 2, 3, 4, 5, 6, 7, 8, 9, 14, 15, 16, 17, 18, 19, 20, 21. Appendix coverage: reviewed.
Original figures and tables remain the work of the source’s authors. Extractions preserve their scientific content; any HTML wrapper layout is disclosed with each figure. The surrounding reading notes are our own.
Text reading scope & known omissions
- Title page and abstract
- 1 Introduction
- 2 World Action Models and Action-State Consistency
- 3 Characterizing Action-State Consistency: 3.1–3.3
- 4 Consistency-Guided Test-Time Selection: 4.1–4.2
- 5 Experimental Evaluation: 5.1–5.3
- 6 Conclusion and Future Work
- References
- Appendix contents
- A Implementation Details
- B Related Work
- C Per-Task Analysis: C.1–C.2
- D Relationship between Motion Change and Consistency Score
- E Algorithms 1–2
- F Why Does Consensus Use Winner-Takes-All Selection?
- G Mitigating Background Collapse
- H Limitations and Failure Analysis
- I Additional Examples of Background Collapse
- J Additional Visualizations
- K Additional Statements: K.1–K.2
Outside the original text pass
- Text extraction does not reconstruct figure images; inspect the retained PDF for figures and equation/table layout.
- Verified edition: arXiv:2605.07514v1 [cs.RO], 8 May 2026. The observed title and all four authors match the catalog; no other revision was supplied or compared.
- Text extraction does not reconstruct figure images; the retained PDF was visually inspected for figures and equation/table layout.
- Separate supplemental material availability has not been fully verified.
- The complete supplied text was read, including references and appendices. Visual inspection covered PDF pages 1–9 and 14–21; reference pages 10–12 and the appendix contents page 13 were read as text.
- Code and external links were not inspected, and experiments were not reproduced.
The visual audit above records the subsequent illustrated pass.
8.4 Traceable evidence
e01PDF p. 1, title block, arXiv margin and Abstract
The title is Is the Future Compatible? Diagnosing Dynamic Consistency in World Action Models; authors are Bo-Kai Ruan, Teng-Fang Hsiao, Ling Lo and Hong-Han Shuai, all credited to National Yang Ming Chiao Tung University. The margin gives arXiv:2605.07514v1 [cs.RO], 8 May 2026. The abstract states no additional training or reward modeling.
Go to primary source ↓e02PDF pp. 2–3, Section 2, Eqs. (1)–(2) and representative-model paragraph
Defines observation history, proprioception, task specification and future horizon; contrasts joint prediction with world-model/inverse-dynamics factorization. Cosmos-Policy pretrained on RoboCasa and LingBot-VA pretrained on RoboTwin 2.0 are the studied representatives.
Go to primary source ↓e03PDF p. 3, Measuring Action-State Consistency, Eq. (3), footnote 1 and Section 3.1
Consistency exponentiates negative latent MSE between predicted and executed observations; alpha defaults to 0.1. Episode scores are normalized within task using means and standard deviations before pooling. The precise episode aggregation is not defined here.
Go to primary source ↓e04PDF p. 4, Section 3.1, Figures 1–2 and footnote 2
Cohen’s d is 0.76/0.99 for Cosmos-Policy/LingBot-VA. Five-fold logistic regression on normalized consistency is restricted to tasks whose successful episodes have higher mean consistency; AUCs are 0.77/0.88.
Go to primary source ↓e05PDF pp. 4–5, Section 3.2, Figures 3–4 and Eq. (4)
Defines aligned/misaligned tasks and background collapse. Figure 3 contrasts two failed trajectories; Figure 4 compares latent change by alignment and outcome. Equation (4) defines change as distance between temporal latents.
Go to primary source ↓e06PDF p. 6, Section 3.3 and Figure 5
Cosmos-Policy value and consistency are plotted as success-minus-failure gaps across time. Authors use their similar trends to motivate utility relevance and describe value calibration, without showing a calibration curve.
Go to primary source ↓e07PDF pp. 6–7, Section 4, Figure 6 and Eqs. (5)–(9)
Exploring executes/reset-evaluates candidate branches; Consensus averages predicted futures and selects the maximal agreement score. The mean’s accuracy is an assumption. Figure 6 labels Exploring as panel (b), although the p. 7 text references (a); the consensus inset uses an ambiguous central a label.
Go to primary source ↓e08PDF p. 7, Section 5, Setup
Reports eight NVIDIA RTX 5090 GPUs, default N = 8, parallel candidate evaluation, approximately linear serial scaling, and approximately 0.7 ms for selection weights and consistency scoring.
Go to primary source ↓e09PDF p. 8, Section 5.1 and Table 1, Cosmos-Policy and three TTS rows
RoboCasa uses 24 kitchen tasks and 50 trials per task with a single Panda arm. Average SR is 66.6 baseline, 67.4 value, 67.3 consensus and 68.0 exploring. Stars mark reimplementations; other prior-method values are taken from reference [22].
Go to primary source ↓e10PDF p. 8, Section 5.2 and Table 2, LingBot-VA/Consistency-Consensus rows
RoboTwin 2.0 uses Easy fixed initial configurations and 10 trials per task. Average SR rises from 90.2 to 93.0. Main text describes over 50 tasks. Exploring is not evaluated because no state-saving API is available; prior baselines come from [25].
Go to primary source ↓e11PDF p. 9, Section 5.3 and Figure 8
For N = 1, 2, 4, 8, Cosmos-Policy SR is 66.6, 66.7, 67.1, 67.3 and LingBot-VA SR is 90.2, 92.0, 92.8, 93.0. The plot has no error bars.
Go to primary source ↓e12PDF p. 14, Appendix A and footnotes 3–4
Implementation details defer to official Cosmos-Policy and LingBot-VA inference/evaluation setups and reiterate N = 8; external repository references do not specify exact versions in this PDF.
Go to primary source ↓e13PDF pp. 15–16, Appendix C, Figures 10–11 and captions
Figure 10 shows 23/24 RoboCasa and 23/50 RoboTwin tasks, excluding full-success tasks. Examples include reversal for CoffeePressButton and place mouse pad. Figure 11 explicitly includes only tasks with higher successful-episode consistency.
Go to primary source ↓e14PDF pp. 16–17, Appendix D, Table 3 and Figure 12
Latent change and consistency have Pearson/Spearman correlations of −0.47/−0.46 for Cosmos-Policy and −0.35/−0.30 for LingBot-VA. Both quantities are normalized within model for visualization.
Go to primary source ↓e15PDF pp. 16–17, Appendix E, notation and Algorithms 1–2
Both algorithms sample candidate action sequences and future observations. Exploring resets before each evaluation and final execution. Consensus averages futures, computes scores and softmax weights, and uses argmax. Final action notation is t+Δ; exact execution-chunk semantics and tie-breaking are not specified.
Go to primary source ↓e16PDF p. 18, Appendix F, Eq. (10) and Table 4
The authors motivate WTA by non-Euclidean action components and distinct motion hypotheses. Weighted action aggregation versus WTA yields 64.9/67.3 for Cosmos-Policy and 86.4/93.0 for LingBot-VA. Equation (10) specifies positive temperature but no chosen value.
Go to primary source ↓e17PDF p. 18, Appendix G and Figure 13
Compares higher-success consistency-guided exploration rollouts with lower-success vanilla rollouts and plots differences in latent change and consistency. Authors interpret later-stage behavior as mitigation of background collapse.
Go to primary source ↓e18PDF pp. 19–20, Appendices H–I, Figures 14–15
Selection cannot guarantee removal of collapse; authors suggest training-time interventions. Examples involve stalled behavior and objects leaving view. Collapse also occurs in aligned tasks, with greater frequency in misaligned settings.
Go to primary source ↓e19PDF pp. 9 and 20–21, Figure 7, Appendix J, Figure 16; p. 20, Appendix K
Qualitative comparisons juxtapose predicted/real trajectories under original and consensus selection. Appendix K describes the work as foundational rather than a deployed robotic system. The examples do not constitute physical-robot validation.
Go to primary source ↓8.5 Primary sources
Is the Future Compatible? Diagnosing Dynamic Consistency in World Action Models ↗
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