LeapBot-JAC: Joint Action Chains for World Action Models

Chenhao Zhu; Baibing Liu; Pei Liu; Rui Yang; Lang Zhang; Mingyue Feng; Yaonong Wang; Jun Ma

cs.ROOct 9, 2026

LeapBot-JAC: Joint Action Chains for World Action Models

Authors: Chenhao Zhu, Baibing Liu, Pei Liu, Rui Yang, Lang Zhang, Mingyue Feng, Yaonong Wang, Jun Ma

Organizations: Leapmotor · The Hong Kong University of Science and Technology (Guangzhou) · The Hong Kong University of Science and Technology

Abstract

Action-chunking policies predict future controls but execute only a prefix before replanning. How should a fixed action-token budget be organized to support execution? We propose LeapBot-JAC, which allocates sparse future-control references and dense execution targets within one shared action expert. Both chains are supervised by demonstrated controls and jointly denoised at aligned physical-time positions. Directed attention allows execution tokens to read reference tokens subject to the host's visibility constraints. JAC adds neither action tokens nor parameters and retains the host's world-modeling process; both chains participate in inference, while only the execution chain drives the robot. On LIBERO, JAC reaches 98.3% success with FastWAM and LeapBot-WA, up from 97.0% and 97.3%. On zero-shot LIBERO-Plus, their success rates improve from 53.5% to 70.4% and from 73.1% to 77.3%, respectively. RoboTwin and two real-world tasks provide additional deployment evaluations. Single-training-seed comparisons and FastWAM ablations support action-role organization as a practical design choice under a fixed action-token budget.

Figures & tables

Panel of Figure 1 — Figure 1: Motivation and budget allocation. (a) A conceptual illustration of how future placement constraints may affect current grasp choices; it is not an experimental rollout or a validated feasible solution. (b) JAC reallocates 32 action tokens into sparse reference and dense execution targets on the same control-time axis. Only the execution prefix drives the robot.

Figure 2: Overview of LeapBot-JAC. A shared action expert jointly denoises a sparse reference chain

R

and a dense execution chain

A

within a fixed action-token budget. Physical-time encoding aligns the two chains, while a directed attention mask allows

A

to attend to

R

and blocks direct reverse access, subject to the host’s visibility constraints. Both chains are supervised by demonstrated controls, and only the execution prefix drives the robot. The illustrated

K=10

is used on LIBERO/LIBERO-Plus; RoboTwin uses

K=16

. The host’s world-modeling objectives and sampling schedule are preserved.

Panel of Figure 3 — Figure 3: Time alignment and directed visibility. (a) At $s=3$ , reference indices span 0–45 and execution indices span 0–15. (b) The outlined $16\times 16$ block prevents $R$ queries from reading $A$ keys. $S_{c},S_{f}$ denote current/future semantics. The full matrix illustrates an action-independent semantic context; non-action restrictions are inherited from the host. General visibility follows Eq. ( 8 ).

	LIBERO					RoboTwin 2.0
Model	Spa.	Obj.	Goal	Long	Avg.	Clean	Random	Avg.
Literature reports
$\pi_{0}$ ( Yuan et al., 2026 )	96.8	98.8	95.8	85.2	94.1	65.92	58.40	62.16
$\pi_{0.5}$ ( Yuan et al., 2026 )	98.8	98.2	98.0	92.4	96.9	82.74	76.76	79.75
Motus ( Bi et al., 2025 )	96.8	99.8	96.6	97.6	97.7	88.66	87.02	87.84
LingBot-VA ( Li et al., 2026a )	98.5	99.6	97.2	98.5	98.5	92.93	91.55	92.24

Table 1: Standard manipulation. Success rates (%) on LIBERO and RoboTwin 2.0. Cited results and reproduced Base/JAC pairs are separated. Parentheses denote the improvement in average success rate (pp); bold indicates the better result within each reproduced pair. Our RoboTwin experiments execute 16 actions from each predicted action chunk; literature protocols are retained as reported. Motus LIBERO scores are reported by Yuan et al. (2026) ; its RoboTwin scores follow Bi et al. (2025) .

Model	Cam.	Robot	Lang.	Light	Bg.	Noise	Layout	Agg.
Literature reports
$\pi_{0}$ ( Li et al., 2026b )	13.8	6.0	58.8	85.0	81.4	79.0	68.9	53.6
LAWA ( Li et al., 2026b )	69.2	66.7	62.8	96.2	64.5	85.5	78.6	74.4
ImageWAM (FLUX.2 4B) ( Zhang et al., 2026 )	80.8	50.3	91.4	98.1	85.5	93.8	80.5	83.1 †
Our reproductions
FastWAM	19.2	46.2	69.9	82.0	53.7	41.0	62.8	53.5

Table 2: Zero-shot LIBERO-Plus. Success rates (%) without Plus adaptation. Aggregate is full-test Total for our rows and trial-level aggregation for

\pi_{0}

/LAWA;

\dagger

retains ImageWAM’s reported Avg.; equivalence to our episode-weighted Total is not assumed. Parentheses denote changes in aggregate success rate (pp); bold indicates gains.

Information flow	$A$ reads $R$	$R$ reads $A$	Spatial	Object	Goal	Long	Avg.
$R\leftrightarrow A$	$\surd$	$\surd$	98.0	99.4	97.0	95.4	97.5
$R\rightarrow A$	$\surd$	$\times$	98.0	99.6	98.2	97.2	98.3
No direct cross-chain	$\times$	$\times$	98.6	100.0	98.0	94.2	97.7
$A\rightarrow R$	$\times$	$\surd$	98.4	99.6	98.0	96.6	98.2

Table 3: G1: visibility on LIBERO at

s=3

\lambda_{R}=0.2

. Permissions refer to direct attention before host intersection. Padded targets are loss-masked but remain attention-visible.

Panel of Figure 4 — Figure 4: LIBERO sensitivity on FastWAM. (a) Reference-supervision weight at $s=2$ , with the no-JAC baseline shown as a separate dashed reference. (b) Reference stride at $\lambda_{R}=0.2$ ; the observed range is 0.3 pp. Measured settings share a restricted vertical scale; lines aid readability. Results use one training seed, without error bars.

Backbone	Configuration	Total params	Denoising	Latency	Peak memory
		(M)	steps	(ms)	(GB)
FastWAM	Base ( $32A$ )	11701.62	10	337.0	25.00
	JAC ( $16R+16A$ )	11701.62	10	337.7	25.00
LeapBot-WA	Base ( $32A$ )	8428.11	10	438.6	26.13
	JAC ( $16R+16A$ )	8428.11	10	435.1	26.13

Table 4: Computational cost within each host. Model-call latency is wall-clock p50, including host-side scheduling and CPU–GPU transfers within inference. Measurements use the training hardware and configuration. Memory is peak allocated GPU memory.

Panel of Figure 5 — Figure 5: Real-world task sequences, ordered left to right. (a) Apple-to-green-basket placement precedes green-block-to-blue-basket placement. (b) Drawer closing with possible cabinet displacement during contact.

Appendix figures & tables2 assets

Supplementary material from the paper’s appendix.

Appendix

Evaluation	Host	Stride	Reference weight
Main performance	FastWAM; LeapBot-WA	3	0.2
G1 visibility	FastWAM	3	0.2
G2 weight	FastWAM	2	0.1, 0.2, 0.5, 1
G3 stride	FastWAM	2, 3, 4	0.2

Appendix

Table 5: Reported JAC configuration groups. All use FastWAM unless another host is listed. All results use one training seed.

Parameter	Setting
Host training hyperparameters	Original FastWAM / LeapBot-WA recipes
Training and evaluation hardware	Eight NVIDIA H100 GPUs
Training seeds	One common seed across local experiments
Action-token budget	Base: $32A$ ; JAC: $16R+16A$
Default reference settings	$s=3$ , $\lambda_{R}=0.2$ , $R\rightarrow A$
Weight sweep	$s=2$ ; $\lambda_{R}\in\{0.1,0.2,0.5,1\}$

Appendix

Table 6: Implementation and evaluation settings. Host-specific hyperparameters follow the cited original papers; execution settings below apply to our paired comparisons.

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World Action Models (WAMs) have emerged as a powerful paradigm for embodied intelligence, yet the prevailing reliance on pixel-level video generation creates a fundamental bottleneck. Forcing models to reconstruct task-irrelevant visual details dissipates representational capacity and renders policies vulnerable to visual distractors. In this paper, we propose LeapBot-WA, which establishes a novel Predictive-Latent paradigm for WAMs by operationalizing the Joint-Embedding Predictive Architecture (JEPA) as a World-Anchor. Departing from the traditional reliance on visual synthesis, LeapBot-WA shifts the core of world modeling to Predictive Semantic Alignment, extracting abstract physical dynamics directly within a latent foundation space. To bridge the modality gap between non-Gaussian predictive features and diffusion priors, we introduce the Isotropic Semantic Autoencoder (ISAE), which reshapes the anchor's latent space into a diffusion-friendly manifold to prevent off-manifold drift. Furthermore, we design an Asymmetric Mixture-of-Transformers (MoT) architecture. During training, an Anchor Diffusion Transformer acts as a privileged dynamics expert to guide the Action Diffusion Transformer; at inference, this heavy dynamics branch is pruned, enabling zero-overhead execution. LeapBot-WA achieves state-of-the-art performance among predictive models on LIBERO and matches top-tier generative WAMs on RoboTwin 2.0 without requiring large-scale trajectory pre-training. It further demonstrates superior zero-shot robustness to unseen environments and successful real-world transfer, establishing a highly efficient and robust latent-centric paradigm for scalable robotic control. Code: https://github.com/LeapWM/leapbot-wa.

Abstract

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