Latent Bridge predicts VLM feature deltas to reduce VLM calls by 50-75% in dual-system VLA models while retaining 95-100% performance and achieving 1.65-1.73x speedup across LIBERO, RoboCasa, and ALOHA benchmarks.
DyQ-VLA: Temporal-dynamic-aware quantization for embodied VLAs
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
HeiSD delivers up to 2.45x faster inference for embodied VLA models by hybridizing speculative decoding with kinematic boundary detection and error-mitigation tricks while preserving task success rates.
RoboECC delivers up to 3.28x speedup for VLA model inference via co-aware segmentation and network-aware adjustment with 2.55-2.62% overhead.
citing papers explorer
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Latent Bridge: Feature Delta Prediction for Efficient Dual-System Vision-Language-Action Model Inference
Latent Bridge predicts VLM feature deltas to reduce VLM calls by 50-75% in dual-system VLA models while retaining 95-100% performance and achieving 1.65-1.73x speedup across LIBERO, RoboCasa, and ALOHA benchmarks.
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HeiSD: Hybrid Speculative Decoding for Embodied Vision-Language-Action Models with Kinematic Awareness
HeiSD delivers up to 2.45x faster inference for embodied VLA models by hybridizing speculative decoding with kinematic boundary detection and error-mitigation tricks while preserving task success rates.
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RoboECC: Multi-Factor-Aware Edge-Cloud Collaborative Deployment for VLA Models
RoboECC delivers up to 3.28x speedup for VLA model inference via co-aware segmentation and network-aware adjustment with 2.55-2.62% overhead.