agent/papers/items/2026-2607-15434-coercion-and-deception-in-ai-to-ai-management-an-agentic-benchmark-of-unprompted.md

Paper: Coercion and Deception in AI-to-AI Management: An Agentic Benchmark of Unprompted Escalation

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type: paper title: "Coercion and Deception in AI-to-AI Management: An Agentic Benchmark of Unprompted Escalation" authors: Jasmine Brazilek, Maheep Chaudhary, Zoe Lu, Miles Tidmarsh year: 2026 venue: arXiv url: https://arxiv.org/abs/2607.15434 code_url: source: arxiv collected_at: 2026-07-27 published_at: 2026-07-22 updated_at: 2026-07-22 status: queued relevance: high topics:

• agent-evaluation
• multi-agent
• tool-use methods:

benchmarks:

models:

datasets:

• cs.MA related_concepts:

related_jobs:

related_experiments:

related_projects:

collection_score: 21 collection_queries: agent-evaluation, ai-agent, multi-agent-llm

One-line Takeaway

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

Why Collected

• matched queries: agent-evaluation, ai-agent, multi-agent-llm
• inferred topics: agent-evaluation, multi-agent, tool-use
• arXiv categories: cs.MA
• collection score: 21

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/2607.15434