AgentSnare: Learning to Delay, Divert, and Defuse Autonomous Penetration Agents
Scanned 9/11/2026
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---
name: agentsnare-learning-to-delay-divert-and-defuse-aut
description: AgentSnare: Learning to Delay, Divert, and Defuse Autonomous Penetration Agents
version: 1.0.0
date: 2026-07-30
arxiv_id: 2607.26998
tags: ["arxiv", "research", "paper", "other"]
activation_keywords: ["agentsnare", "learning", "to", "delay", "divert"]
---
# AgentSnare: Learning to Delay, Divert, and Defuse Autonomous Penetration Agents
**arXiv ID:** 2607.26998
**Utility Score:** 1.00
**Authors:** Ruoyu Wang, Heng Zhao, Renjie Wu
**URL:** https://arxiv.org/abs/2607.26998
## 概述
AgentSnare: Learning to Delay, Divert, and Defuse Autonomous Penetration Agents
这是一篇来自 arXiv 的高价值论文(utility score: 1.00),通过自动追踪系统识别。
## 核心创新
*待研究论文内容后填写*
## 应用场景
*待研究论文内容后填写*
## 实现要点
*待研究论文内容后填写*
## 参考资源
- [arXiv论文](https://arxiv.org/abs/2607.26998)
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Ultra-compressed communication mode. Cuts token usage ~75% by speaking like caveman while keeping full technical accuracy. Supports intensity levels: lite, full (default), ultra, wenyan-lite, wenyan-full, wenyan-ultra. Use when user says "caveman mode", "talk like caveman", "use caveman", "less tokens", "be brief", or invokes /caveman. Also auto-triggers when token efficiency is requested.