agent/papers/items/2026-2606-24626-safari-scaling-long-horizon-agentic-fault-attribution-via-active-investigation.md

Paper: SAFARI: Scaling Long Horizon Agentic Fault Attribution via Active Investigation

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type: paper title: "SAFARI: Scaling Long Horizon Agentic Fault Attribution via Active Investigation" authors: Chenyang Zhu, Jiayu Yao, Kushal Chawla, Youbing Yin, Nathan Wolfe, Pengshan Cai, Jingyu Wu, Spencer Hong, et al. year: 2026 venue: arXiv url: https://arxiv.org/abs/2606.24626 code_url: source: arxiv collected_at: 2026-07-08 published_at: 2026-06-23 updated_at: 2026-06-23 status: queued relevance: high topics:

• agent-evaluation
• memory
• multi-agent
• planning
• reasoning
• tool-use methods:

benchmarks:

models:

datasets:

• cs.AI related_concepts:

related_jobs:

related_experiments:

related_projects:

collection_score: 19 collection_queries: autonomous-agent-llm, multi-agent-llm

One-line Takeaway

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

Why Collected

• matched queries: autonomous-agent-llm, multi-agent-llm
• inferred topics: agent-evaluation, memory, multi-agent, planning, reasoning, tool-use
• arXiv categories: cs.AI
• collection score: 19

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/2606.24626