arXiv (All)
open access
Before Parc Fermé: RL-Time Pruning for Efficient Embodied LLMs in Autonomous Driving
Embodied Large Language Models (LLMs) are increasingly used as reasoning modules in robotic control pipelines to improve human-robot interaction, but their memory and generation latency make real-time deployment difficult. Pruning can reduce these costs, but for controllers that undergo multiple pre
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
- SAGE: Symbolic Action-Gating and Editing for LLM Task Planners#1119341
- Forward Dynamics Models#1297469
- Explicit Language Memory for Long-Horizon Planning in Vision-Language-Action Models#429415
- MEMORA: Embodied Action Memory from Egocentric Videos for Reasoning and Planning#800103
- Can a Frontier VLM Serve as a Robot Policy for Memory-Dependent Manipulation?#1342226
Record · ID 1342503
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