BridgeWAM: Bridging Video and Action Experts via Latent Bridge Queries

Jiaxin Fang; Ruitong Liu; Zeyu Lai; Xingyu Wang; Mingyuan Jia; Pingyue Sheng; Weikang Wan; Mengdi Xu

cs.ROOct 10, 2026

BridgeWAM: Bridging Video and Action Experts via Latent Bridge Queries

Authors: Jiaxin Fang, Ruitong Liu, Zeyu Lai, Xingyu Wang, Mingyuan Jia, Pingyue Sheng, Weikang Wan, Mengdi Xu,

Organizations: Fudan University · CocoMatrix · Peking University · University of California, San Diego ·

Abstract

World Action Models (WAMs) commonly couple video and action experts through dense, layer-wise Mixture-of-Transformers (MoT) attention, exposing action prediction to irrelevant visual features while enforcing matched expert depths. This coupling hinders selective visual-feature use and introduces redundant computation. We introduce Bridge-of-Experts (BoE), instantiated as BridgeWAM, which aggregates visual, language, and proprioceptive priors through 32 Latent Bridge Queries (LBQs) into a shared, limited latent space. Joint video-action supervision lets action gradients guide the Video Expert toward representations better suited for action prediction, while the Action Expert learns to selectively retrieve task-relevant information through cross-attention. Decoupling expert depths and reusing LBQs during action denoising yield a 5.06B model with a 30-layer Video Expert and a two-layer Action Expert, achieving 98.25% success on LIBERO and 72.24% pooled success on LIBERO-Plus, demonstrating effective task execution and strong generalization with an ultralight action head. Ablations support joint representation adaptation and unified LBQ conditioning. Relative to Fast-WAM, BridgeWAM reduces single-sample inference FLOPs by 28.72% in simulation profiling and real-robot latency to 77.1 ms (a 2.47-fold speedup). Qualitative experimental results further indicate preferential attention to target objects and task destinations across perturbations.

Figures & tables

Figure 1: Overview of BridgeWAM . Left: LIBERO-Plus success rates and parameters. Center: LBQs replace dense MoT video K/V, linking 30-layer Video and two-layer Action Experts. Right: real-world manipulation tasks, success rates, and measured inference latency in evaluated settings.

Figure 2: Comparison of the BoE and MoT architectures. BoE uses Latent Bridge Queries (LBQs) to transfer action-relevant visual features between experts. Unlike MoT’s matched-depth coupling, BoE decouples expert depths, enabling BridgeWAM to use only two layers in the Action DiT while retaining the full Video Expert for learning rich representations of visual information.

Figure 3: Overview of the BridgeWAM architecture. Latent Bridge Queries ( LBQs ) first aggregate action-relevant representations from the Video DiT and are read by the Action DiT to guide action prediction, forming a compact bridge through which information flows from video to action.

Method	Embodied PT.	LIBERO					LIBERO-Pro (zero-shot)
		Spatial $\uparrow$	Object $\uparrow$	Goal $\uparrow$	Long $\uparrow$	Avg. $\uparrow$	Spatial $\uparrow$	Object $\uparrow$	Goal $\uparrow$	Long $\uparrow$	Avg. $\uparrow$
OpenVLA Kim et al. (2024)	✓	84.7	88.4	79.2	53.7	76.5	40.5	32.0	25.0	20.5	29.5
$\pi_{0}$ Black et al. (2024)	✓	96.8	98.8	95.8	85.2	94.1	52.0	33.2	54.6	33.2	43.2 *
$\pi_{0.5}$ Physical Intelligence et al. (2025)	✓	98.8	98.2	98.0	92.4	96.9	55.6	52.2	48.0	57.0	53.0 *
LingBot-VA Li et al. (2026b)	✓	98.5	99.6	97.2	98.5	98.5	–	–	–	–	–
Motus Bi et al. (2025)	✓	96.8	99.8	96.6	97.6	97.7	–	–	–	–	–

Table 1: Results on LIBERO and LIBERO-Pro. We report task success rates (%) on the Spatial, Object, Goal, and Long-Horizon suites, together with the average success rate across these suites.

Task	$\pi_{0}$		$\pi_{0.5}$		Motus		Motus from WAN2.2		LingBot-VA		Fast-WAM		Faster-WAM		BridgeWAM (Ours)
	Black et al. (2024)		Physical Intelligence et al. (2025)		Bi et al. (2025)		Bi et al. (2025)		Li et al. (2026b)		Yuan et al. (2026)		Zhao et al. (2026)
	C	R	C	R	C	R	C	R	C	R	C	R	C	R	C	R
Adjust Bottle	99	95	100	99	89	93	99	97	90	94	100	100	100	99	100	99
Blocks Ranking RGB	80	63	92	85	99	97	92	88	99	98	100	100	100	100	99	99
Click Bell	71	48	99	66	100	100	100	100	100	100	100	100	100	100	99	99
Grab Roller	98	94	100	100	100	100	100	100	100	100	100	100	100	100	100	100

Table 3: Fine-grained task comparison on RoboTwin 2.0 Simulation (Clean vs. Randomized).

Figure 4: Real-world manipulation tasks and results. We compare $\pi_{0.5}$ , Fast-WAM, and BridgeWAM on closing a laptop and picking and placing a drink, a pen, and a mouse. The task panels show the original settings and variations in object height, type, color, position, or count, together with success rates (%). The right panels summarize success rates and inference latency (ms).

Method	LIBERO					LIBERO-Plus (Zero-shot)
	Spatial $\uparrow$	Object $\uparrow$	Goal $\uparrow$	Long $\uparrow$	Avg. $\uparrow$	Spatial $\uparrow$	Object $\uparrow$	Goal $\uparrow$	Long $\uparrow$	Avg. $\uparrow$
Fast-WAM	97.8	99.4	95.4	94.6	96.80	52.21	69.06	37.32	40.06	49.54
BridgeWAM (Ours)	99.0	100.0	97.4	96.6	98.25	77.64	84.47	60.29	67.17	72.24
Frozen-Video-DiT	87.8	97.0	84.8	72.4	85.50	32.22	38.52	15.44	16.36	25.48
MoT-readout	98.0	99.4	97.4	96.4	97.80	71.57	80.70	36.09	54.78	60.48
Text-state-LBQ-input	96.4	99.2	97.0	96.2	97.20	64.70	79.98	44.11	49.07	59.29

Table 4: LIBERO and LIBERO-Plus ablations of the interface and video–action coupling. Task success rates (%) are reported.

Method	LIBERO					LIBERO-Plus (Zero-shot)
	Spatial $\uparrow$	Object $\uparrow$	Goal $\uparrow$	Long $\uparrow$	Avg. $\uparrow$	Spatial $\uparrow$	Object $\uparrow$	Goal $\uparrow$	Long $\uparrow$	Avg. $\uparrow$
Fast-WAM	97.8	99.4	95.4	94.6	96.80	52.21	69.06	37.32	40.06	49.54
BridgeWAM (Ours)	99.0	100.0	97.4	96.6	98.25	77.64	84.47	60.29	67.17	72.24
4 layers	97.2	100.0	97.2	96.6	97.75	67.44	84.63	54.30	60.26	66.56
8 layers	97.4	99.8	97.6	96.8	97.90	70.69	83.76	49.83	58.44	65.50
16 layers	98.8	99.4	96.2	93.4	96.95	67.94	80.70	52.37	62.13	65.66

Table 5: Ablations of action-head depth and LBQ sequence length on LIBERO and LIBERO-Plus.

Method	Layers		FLOPs (T)			Acceleration		Peak Mem. (MiB)
	Video	Action	Video $\downarrow$	Action $\downarrow$	Total $\downarrow$	Latency (ms) $\downarrow$	Speedup $\uparrow$
Fast-WAM Yuan et al. (2026)	30	30	1.005	1.043	2.048	405.91	$1.00\times$	23,773.86
Faster-WAM Zhao et al. (2026)	30	30	2.745	0.476	3.221	321.16	1.26 $\times$	22,962.00
BridgeWAM	30	2	1.432	0.028	1.460	88.05	4.61 $\times$	21,912.21

Table 6: Inference efficiency on LIBERO with 30-layer Video DiTs, H20Z GPUs, bfloat16 , an action horizon of 32, and 10 denoising steps. Appendix A.8 provides details of the profiling scope.

Method	FID $\downarrow$	FVD 9 $\downarrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR (dB) $\uparrow$
Fast-WAM IDM	11.3573	30.8644	0.771549	0.202842	20.5026
BridgeWAM IDM	11.2093 $-0.1480$	30.7990 $-0.0654$	0.773735 $+0.002186$	0.199942 $-0.002900$	20.5471 $+0.0445$

Table 7: Zero-shot IDM video-generation quality on LIBERO-Plus (1,415,986 shared action-replay clips; nine frames with the reconstructed conditioning frame included). Bold: better score; gray: BridgeWAM minus Fast-WAM.

Figure 5: Action-conditioned visual routing maps. The top row shows Fast-WAM attention averaged across Video-DiT layers, while the bottom row shows the LBQ-mediated routing at the final Video-DiT layer of BridgeWAM . The columns show the models under variations in background texture, lighting, camera viewpoint, robot initial state, object layout, and sensor noise.

Appendix figures & tables24 assets

Supplementary material from the paper’s appendix.

Appendix

Strategy	LBQ frame access	LBQs used for action conditioning
A (original IDM)	First frame only	Extra full-frame prefill on final $x_{0}$ .
B1 (full-video default)	All frames	Final-layer LBQs from the last video denoising forward pass; no extra prefill.
B2	All frames	Extra full-frame prefill on final $x_{0}$ .

Appendix

Table 8: IDM inference strategies. Prefill denotes an additional full-frame Video/LBQ forward pass at

t=0

on the final denoised video latent

x_{0}

. Frame access refers to the video frames visible to LBQ queries during video denoising.

(a) Results by suite and perturbation category
Perturbation	Spatial	Object	Goal	Long	Macro	Pooled	Episodes
Camera viewpoint	65.16	83.59	30.64	46.78	56.54	56.10	1,599
Robot initial state	60.29	62.81	51.83	66.92	60.46	60.39	1,550
Language instruction	86.15	95.76	69.76	88.77	85.11	84.65	1,537
Lighting	97.26	95.29	94.98	81.39	92.23	92.38	1,142
Background texture	84.50	86.29	64.41	41.52	69.18	68.12	1,076

Appendix

Table 9: Detailed LIBERO-Plus results for BridgeWAM . Success rates are reported in percent (higher is better). Panel (a) separates suite-wise rates, their unweighted macro average, and success rates computed by pooling episodes. Panel (b) groups episodes by the recorded difficulty level. All pooled totals include the episodes for which no difficulty level is specified.

Perturbation	Spatial	Object	Goal	Long	Pooled	Successes	Episodes
Language	96.20	99.20	97.80	97.20	97.60	1,952	2,000
Object	96.60	91.80	83.00	56.60	82.00	1,640	2,000
Swap	0.00	0.00	4.60	0.80	1.35	27	2,000
Task	0.00	0.00	10.00	9.80	4.95	99	2,000
Environment	96.40	84.60	96.80	53.00	82.70	1,654	2,000
All perturbations	57.84	55.12	58.44	43.48	53.72	5,372	10,000

Appendix

Table 10: Detailed LIBERO-Pro results for BridgeWAM . Success rates are reported in percent (higher is better). Each suite–perturbation cell contains ten tasks with 50 trials each. Pooled rates combine all episodes in the corresponding row; success and episode counts cover all four suites.

Task	$\pi_{0}$		$\pi_{0.5}$		Motus		Motus from WAN2.2		LingBot-VA		LingBot-VA from WAN2.2		Fast-WAM		Faster-WAM		BridgeWAM (Ours)
	Black et al. (2024)		Physical Intelligence et al. (2025)		Bi et al. (2025)		Bi et al. (2025)		Li et al. (2026b)		Li et al. (2026b)		Yuan et al. (2026)		Zhao et al. (2026)
	C	R	C	R	C	R	C	R	C	R	C	R	C	R	C	R	C	R
Adjust Bottle	99	95	100	99	89	93	99	97	90	94	–	–	100	100	100	99	100	99
Beat Block Hammer	79	84	96	93	95	88	88	90	96	98	–	–	99	97	97	98	99	91
Blocks Ranking RGB	80	63	92	85	99	97	92	88	99	98	–	–	100	100	100	100	99	99
Blocks Ranking Size	14	5	49	26	75	63	38	50	94	96	–	–	94	98	74	93	87	94

Appendix

Table 11: Fine-grained task comparison on RoboTwin 2.0 Simulation (Clean vs. Randomized).

Task	One-point criteria	Max.
Close the laptop	1. Fully close the laptop lid.	1
Pick and place a drink	1. Grasp and lift the drink. 2. Place the drink within the coaster region. 3. Leave the drink stably upright after release.	3
Pick and place a pen	1. Grasp and lift the pen. 2. Bring the lifted pen to the vicinity of the pen holder. 3. Place the pen inside the holder.	3
Pick and place a mouse	1. Grasp and lift the mouse. 2. Maintain a secure grasp during transport without the mouse slipping out. 3. Place the mouse within the mouse-pad region. 4. Set the mouse down stably rather than dropping it.	4

Appendix

Table 12: Task-specific scoring criteria for real-robot evaluation. Each of the numbered criteria contributes one point. Max. denotes the maximum score that can be obtained per trial.

Figure 6: Frame-by-frame comparisons on real-robot tasks. The four panels show closing a laptop and picking and placing a drink, a pen, and a mouse. Each panel compares Fast-WAM (upper row) with BridgeWAM (lower row), with the sequence of frames progressing from left to right.

Figure 7: Additional visual routing examples: bowl placement. The tasks place a black bowl onto a plate from the stove (upper panel) or the cookie box (lower panel). Within each comparison, the results from Fast-WAM are shown above the corresponding results from BridgeWAM .

Figure 8: Additional visual routing examples: basket placement. The tasks place butter (upper panel) or salad dressing (lower panel) into a basket. Within each comparison, the results from Fast-WAM are shown above the corresponding results from BridgeWAM .

Figure 9: Additional visual routing examples: different placement goals. The tasks place ketchup into a basket (upper panel) and a wine bottle on top of a cabinet (lower panel). In each comparison, the Fast-WAM results are shown above the results for BridgeWAM .

Method	0	1	2	3	4	5	6	7	8	9	Avg.
BridgeWAM (2 layers)	100	100	100	100	98	94	100	100	98	100	99.00
4 layers	100	100	100	100	92	84	98	100	98	100	97.20
8 layers	100	98	100	98	96	86	100	100	96	100	97.40
16 layers	100	100	100	100	92	98	100	100	98	100	98.80
30 layers (self-cross)	100	94	100	100	98	92	100	100	94	100	97.80
Frozen-Video-DiT	98	92	98	100	54	80	98	82	86	90	87.80

Appendix

Table 13: Complete LIBERO-Spatial ablation results. Success rates (%) for all ten tasks, with 50 episodes per task. Task IDs follow the zero-based benchmark order; Avg. pools all 500 episodes.

Method	0	1	2	3	4	5	6	7	8	9	Avg.
BridgeWAM (2 layers)	100	100	100	100	100	100	100	100	100	100	100.00
4 layers	100	100	100	100	100	100	100	100	100	100	100.00
8 layers	100	100	100	100	100	100	100	100	98	100	99.80
16 layers	100	100	100	100	96	98	100	100	100	100	99.40
30 layers (self-cross)	100	100	100	100	100	100	100	100	100	100	100.00
Frozen-Video-DiT	98	100	100	94	96	96	98	90	98	100	97.00

Appendix

Table 14: Complete LIBERO-Object ablation results. Success rates (%) for all ten tasks, with 50 episodes per task. Task IDs follow the zero-based benchmark order; Avg. pools all 500 episodes.

Method	0	1	2	3	4	5	6	7	8	9	Avg.
BridgeWAM (2 layers)	98	98	88	96	100	96	100	98	100	100	97.40
4 layers	100	100	88	88	100	100	98	100	98	100	97.20
8 layers	98	100	88	100	98	100	100	98	100	94	97.60
16 layers	98	100	90	92	100	94	98	98	100	92	96.20
30 layers (self-cross)	100	100	94	94	96	98	100	100	100	94	97.60
Frozen-Video-DiT	82	96	96	72	98	96	96	100	98	14	84.80

Appendix

Table 15: Complete LIBERO-Goal ablation results. Success rates (%) for all ten tasks, with 50 episodes per task. Task IDs follow the zero-based benchmark order; Avg. pools all 500 episodes.

Method	0	1	2	3	4	5	6	7	8	9	Avg.
BridgeWAM (2 layers)	98	100	90	100	100	100	86	96	96	100	96.60
4 layers	96	100	98	100	90	98	90	96	98	100	96.60
8 layers	98	100	92	100	92	100	92	96	100	98	96.80
16 layers	96	98	96	100	92	100	82	90	84	96	93.40
30 layers (self-cross)	96	98	96	100	86	100	86	98	88	100	94.80
Frozen-Video-DiT	74	74	96	96	32	90	38	66	88	70	72.40

Appendix

Table 16: Complete LIBERO-Long ablation results. Success rates (%) for all ten tasks, with 50 episodes per task. Task IDs follow the zero-based benchmark order; Avg. pools all 500 episodes.

Perturbation	Spatial	Object	Goal	Long	Pooled	N
BridgeWAM (2 layers)
Camera viewpoint	65.16	83.59	30.64	46.78	56.10	1,599
Robot initial state	60.29	62.81	51.83	66.92	60.39	1,550
Language instruction	86.15	95.76	69.76	88.77	84.65	1,537
Lighting	97.26	95.29	94.98	81.39	92.38	1,142
Background texture	84.50	86.29	64.41	41.52	68.12	1,076

Appendix

Table 17: Complete LIBERO-Plus action-head depth results by suite and perturbation. The entries in this table are task success rates (%); pooled rates use episode counts, not averages of the displayed percentages. N denotes the total number of episodes pooled across all suites.

Perturbation	Spatial	Object	Goal	Long	Pooled	N
Frozen-Video-DiT
Camera viewpoint	3.72	0.51	2.21	0.24	1.63	1,599
Robot initial state	32.00	33.92	8.56	20.36	23.35	1,550
Language instruction	56.41	82.77	39.51	50.65	56.54	1,537
Lighting	53.08	87.88	22.94	15.69	45.80	1,142
Background texture	20.16	6.05	2.85	0.00	6.97	1,076

Appendix

Table 18: Complete LIBERO-Plus interface and coupling results by suite and perturbation. Entries are success rates (%); pooled rates use episode counts, not averages of the displayed percentages. N denotes the total number of episodes across all the evaluated suites.

Perturbation	Level 1	Level 2	Level 3	Level 4	Level 5	Unspec.	Pooled
BridgeWAM (2 layers)
Camera viewpoint	70.85	60.28	56.92	57.89	45.59	–	56.10
Robot initial state	87.50	82.19	64.97	45.26	30.48	–	60.39
Language instruction	90.85	93.57	92.28	85.67	64.62	–	84.65
Lighting	98.20	94.42	94.67	91.74	83.26	97.52	92.38
Background texture	87.55	78.23	69.58	55.21	30.14	–	68.12

Appendix

Table 19: Complete LIBERO-Plus action-head depth results by difficulty. Rates (%) are pooled across all four suites. Unspec. includes 121 lighting episodes without a difficulty annotation; all of these episodes are retained in the pooled totals. – denotes cells with no evaluated episodes.

Perturbation	Level 1	Level 2	Level 3	Level 4	Level 5	Unspec.	Pooled
Frozen-Video-DiT
Camera viewpoint	6.03	2.78	0.62	0.44	0.21	–	1.63
Robot initial state	41.94	34.25	24.33	12.28	9.12	–	23.35
Language instruction	64.02	62.50	59.85	54.27	44.15	–	56.54
Lighting	47.75	44.16	56.56	50.83	41.41	23.14	45.80
Background texture	14.16	7.75	6.46	2.45	0.00	–	6.97

Appendix

Table 20: Complete LIBERO-Plus interface and coupling results by difficulty. Rates (%) are computed by pooling episodes across all four suites. Unspec. includes 121 lighting episodes without a difficulty annotation; these episodes are retained in pooled totals. – denotes no episodes.

LBQs	0	1	2	3	4	5	6	7	8	9	Avg.
LIBERO-Spatial
1	100	98	100	100	96	94	100	100	98	100	98.60
8	100	96	100	100	96	92	98	100	98	100	98.00
16	100	98	100	100	92	98	100	100	96	98	98.20
24	98	100	100	98	100	88	100	100	98	100	98.20
36	100	96	100	100	98	86	100	98	98	100	97.60

Appendix

Table 21: LBQ sequence-length ablations on all LIBERO tasks. Detailed results for the length variants in Table 5 . Entries are success rates (%); each task has 50 episodes. Task IDs follow the zero-based ordering of tasks within each suite. Avg. pools 500 episodes per suite.

Perturbation	Spatial	Object	Goal	Long	Pooled	$N$
1 LBQ
Camera viewpoint	35.37	76.26	17.16	26.25	38.46	1,599
Robot initial state	65.14	66.58	44.99	61.58	59.29	1,550
Language instruction	87.69	95.20	71.71	82.51	83.86	1,537
Lighting	92.12	90.57	88.53	68.98	85.29	1,142
Background texture	68.99	72.58	37.37	24.57	49.63	1,076

Appendix

Table 22: LIBERO-Plus LBQ sequence-length results by suite and perturbation (1/2). Success rates (%) for 1, 8, 16 LBQs. Pooled rates use episode counts;

N

counts episodes across suites.

Perturbation	Spatial	Object	Goal	Long	Pooled	$N$
24 LBQs
Camera viewpoint	71.28	78.03	32.35	41.53	55.22	1,599
Robot initial state	56.00	51.26	47.92	67.68	55.61	1,550
Language instruction	90.51	98.59	71.22	88.51	86.73	1,537
Lighting	98.97	98.65	98.21	81.75	94.57	1,142
Background texture	85.27	83.47	51.60	46.02	65.52	1,076

Appendix

Table 23: LIBERO-Plus LBQ sequence-length results by suite and perturbation (2/2). Success rates (%) for 24, 36, 64 LBQs. Pooled rates use episode counts;

N

counts episodes across suites.

Perturbation	Level 1	Level 2	Level 3	Level 4	Level 5	Unspec.	Pooled
1 LBQ
Camera viewpoint	68.34	43.33	31.69	37.72	27.52	–	38.46
Robot initial state	83.87	78.77	61.50	48.77	31.91	–	59.29
Language instruction	90.24	84.64	88.80	85.37	71.93	–	83.86
Lighting	93.69	92.39	87.70	82.64	74.01	87.60	85.29
Background texture	72.53	62.36	47.53	33.74	10.96	–	49.63

Appendix

Table 24: LIBERO-Plus LBQ sequence-length results by difficulty (1/2). Success rates (%) computed by pooling episodes across suites for 1, 8, 16 LBQs. Unspec. contains 121 lighting episodes without difficulty annotations, retained in all pooled totals. – denotes no episodes.

Perturbation	Level 1	Level 2	Level 3	Level 4	Level 5	Unspec.	Pooled
24 LBQs
Camera viewpoint	70.35	65.83	61.23	51.32	39.01	–	55.22
Robot initial state	85.89	81.85	59.36	37.19	23.36	–	55.61
Language instruction	95.12	88.93	88.42	87.50	74.85	–	86.73
Lighting	99.10	97.46	96.72	95.04	86.34	95.87	94.57
Background texture	83.69	73.80	65.02	52.76	36.30	–	65.52

Appendix

Table 25: LIBERO-Plus LBQ sequence-length results by difficulty (2/2). Success rates (%) computed by pooling episodes across suites for 24, 36, 64 LBQs. Unspec. contains 121 lighting episodes without difficulty annotations, retained in all pooled totals. – denotes no episodes.

Method	FID $\downarrow$	FVD 9 $\downarrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR (dB) $\uparrow$
Spatial (224,794 clips)
Fast-WAM IDM	21.2074	55.7766	0.777271	0.195502	20.9940
BridgeWAM IDM	21.0625	52.6436	0.779674	0.193154	21.0241
Object (293,546 clips)
Fast-WAM IDM	15.7660	70.1651	0.744659	0.204595	20.0774
BridgeWAM IDM	15.5139	69.4985	0.746261	0.201890	20.1057

Appendix

Table 26: IDM video-generation quality by LIBERO-Plus suite. Detailed results for Table 7 , using complete nine-frame clips including the reconstructed conditioning frame. Bold identifies the better score obtained by the two models within each group. The overall result uses all of the 1,415,986 clips; FID and FVD are computed from pooled features, not averaged across suites.

Method	FID $\downarrow$	FVD 9 $\downarrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR (dB) $\uparrow$
Background texture (157,180 clips)
Fast-WAM IDM	20.7995	82.9152	0.712785	0.229659	19.6438
BridgeWAM IDM	19.9457	73.7302	0.713475	0.229351	19.6018
Camera viewpoint (225,967 clips)
Fast-WAM IDM	15.7026	73.4651	0.759493	0.204473	20.0905
BridgeWAM IDM	15.1467	73.1714	0.762843	0.200818	20.1603

Appendix

Table 27: IDM video-generation quality by LIBERO-Plus perturbation. The same two models and complete nine-frame evaluation as Table 7 . Bold marks the better score per perturbation group.

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RealtimeWAM: One-Step Asynchronous World Action Models

Chengtao Lv, Jinyang Du, Shuyi Feng, Yang Yong+6

Nanyang Technological University · Beihang University · Sensetime+1

Efficient Inference for World Action Models Robotic Manipulation Consistency Distillation World Action Models

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World Action Models (WAMs) incorporate visual representations from video generation backbones to guide action prediction. Recent efficient WAMs adopt Mixture-of-Transformers (MoT) architectures and compute video representations once for reuse by the action expert. However, intra-expert iteration (\ie, multi-step action denoising) and inter-expert waiting (\ie, sequential execution of the video and action experts) still limit inference efficiency. To this end, we present RealtimeWAM, an extremely efficient WAM variant with one-step action generation and asynchronous inference, addressing these two bottlenecks. To reduce intra-expert iteration, we propose Teacher-Anchored Consistency Distillation (TACD) to address a local-global error gap: low local consistency error alone does not guarantee accurate final actions. TACD supplements local consistency with explicit supervision from the frozen teacher's multi-step rollout endpoint, enabling accurate one-step action generation. Additionally, we propose Cross-Expert Wavefront Pipelining (CEWP) to eliminate unnecessary expert-level waiting. It overlaps the two experts through block-wise sharing of the video KV cache, synchronizing only immediately before the corresponding action attention consumes it. Extensive experiments across diverse benchmarks (\eg, LIBERO, LIBERO-Plus and RoboTwin) and model variants (\eg, Fast-WAM and Faster-WAM) demonstrate the superiority of RealtimeWAM. Notably, RealtimeWAM maintains near-lossless performance (\ie,

<1\%

drop) across these benchmarks while delivering significant end-to-end speedup (\eg,

\sim25\times

on H100). Our code and checkpoints are available via this link.

Abstract

Vision-Language-Action Models

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