agent/papers/items/2026-2606-21445-autoras-learning-robust-agentic-systems-with-primitive-representations.md

Paper: AutoRAS: Learning Robust Agentic Systems with Primitive Representations

---

type: paper title: "AutoRAS: Learning Robust Agentic Systems with Primitive Representations" authors: Yang Yue, Xuancheng Zhu, Yuyang Ma, Guoshun Nan, Zihan Dou, Jingru Shan, Congyu Guo, Ji Zhang, et al. year: 2026 venue: arXiv url: https://arxiv.org/abs/2606.21445 code_url: source: arxiv collected_at: 2026-07-08 published_at: 2026-06-19 updated_at: 2026-06-19 status: queued relevance: high topics:

• agent-safety
• multi-agent
• reasoning
• tool-use
• workflow-agent methods:

benchmarks:

models:

datasets:

• cs.AI related_concepts:

related_jobs:

related_experiments:

related_projects:

collection_score: 16 collection_queries: agentic-ai

One-line Takeaway

Auto-collected from arXiv because it matched the Agent collection queries. Needs human skim.

Why Collected

• matched queries: agentic-ai
• inferred topics: agent-safety, multi-agent, reasoning, tool-use, workflow-agent
• arXiv categories: cs.AI
• collection score: 16

Review Checklist

• Does this paper directly inform Agent architecture, evaluation, memory, tools, safety, coding agents, GUI/browser agents, or multi-agent workflows?
• Does it include a benchmark, dataset, code, or reproducible experimental setup?
• Should it be promoted from queued to skimmed or summarized?

Links

• arXiv: https://arxiv.org/abs/2606.21445