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 quanti- zation for embodied vision-language-action models
4 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 4representative 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.
ElegantVLA accelerates VLA models up to 3.77x by dynamically scheduling compute across vision, language, and action components without retraining the base model.
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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ElegantVLA: Learning When to Think for Efficient Vision-Language-Action Models
ElegantVLA accelerates VLA models up to 3.77x by dynamically scheduling compute across vision, language, and action components without retraining the base model.
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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.