arXiv (All)
open access
SpikingVLA: Asynchronous Spiking Vision-Language-Action Models
ANN-to-SNN conversion offers a practical route toward energy-efficient spiking Vision-Language-Action (VLA) models by bypassing the substantial cost of training large-scale SNNs from scratch. However, existing methods often require many timesteps to maintain competitive performance, resulting in sub
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- EmbodiedMidtrain: Bridging the Gap between Vision-Language Models and Vision-Language-Action Models via Mid-training#1357146
- Dual-Stream Memory for Vision-Language-Action Policies#1297614
- VLA Foundry: A Unified Framework for Training Vision-Language-Action Models#124125
- FidLIF: Parallel Training of High-Fidelity Spiking Neurons with Exact BPTT#1341806
- DyQ-VLA: Temporal-Dynamic-Aware Quantization for Embodied Vision-Language-Action Models#1200873
Record · ID 1234077
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive