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RoboArchitect: Automating the Design of Robotic Agents with Persistent Memory

Paper recorded by Signals 4 on 2026-10-30 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-30 on arXiv · recorded by Signals 4 on 2026-11-02

Category: cs.AI · 人工智能 · first seen 2026-11-02

Abstract

Recent work shows that a frozen robot policy performs far better inside an agent harness, where control code decides when to query the policy, consult vision tools, or command the arm directly. Yet today these harnesses are built by hand and redesigned for each new robot or benchmark. We show that harness design can be automated, offering a strong alternative to finetuning on a novel setup. In Rob

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#43 most recent of 920 cs.AI papers we have recorded · ↑ newer: Hierarchical World Models with Implicit Dynamics · ↓ older: ACCT: Asymmetric Cross-LLM Context Transfer
Cite this page: RoboArchitect: Automating the Design of Robotic Agents with Persistent Memory: the #43 most recent of 920 cs.AI papers we have recorded (as of 2026-10-30). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/roboarchitect-automating-the-design-of-robotic-agents-with-persistent-memory.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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