评测档案

Petri

自适应审计者可以诱发哪些值得关注的行为？

原文未注明机构 / 2026-10-25 source capture / Behavior · 观察行为

来源与出处 ↓ 阅读研究记录 →

跨报告监测

已发表证据

Petri · Gram mean concerning score

跨来源报告的已发表测量。任务范围通过注释区分，配置差异保留在每条观测中。

24 条观测 · 24 已定位日期

● 条件匹配◇ 配置差异□ 部分条件未披露

选择图中数据点或表格行，查看条件与出处。

24 条观测

测量值与来源条件
系统／模型	结果	条件状态	任务／条件	评测方／报告	日期
DeepSeek V4 Pro	1.6 score (1–10)Gram mean concerning score	条件不完整	Gram: Alignment Eval Sabotage观察模式Gram (Google DeepMind Petri variant); auditor Claude Sonnet 4.6; judge Kimi K2.6; SE across 5 independent runs per scenario	Neo ResearchEarly Results on Evaluating GLM 5.2 for Behavioral and Persuasion Risks	发布 2026-04-24报告 2026-07-03
Claude Opus 4.8	1 score (1–10)Gram mean concerning score	条件不完整	Gram: Alignment Eval Sabotage观察模式Gram (Google DeepMind Petri variant); auditor Claude Sonnet 4.6; judge Kimi K2.6; SE across 5 independent runs per scenario	Neo ResearchEarly Results on Evaluating GLM 5.2 for Behavioral and Persuasion Risks	发布 2026-05-28报告 2026-07-03
GLM 5.2GLM-5.2	1 score (1–10)Gram mean concerning score	条件不完整	Gram: Alignment Eval Sabotage观察模式Gram (Google DeepMind Petri variant); auditor Claude Sonnet 4.6; judge Kimi K2.6; SE across 5 independent runs per scenario	Neo ResearchEarly Results on Evaluating GLM 5.2 for Behavioral and Persuasion Risks	发布 2026-06-16报告 2026-07-03
Claude Opus 4.8	1 score (1–10)Gram mean concerning score	条件不完整	Gram: Incident Response观察模式Gram (Google DeepMind Petri variant); auditor Claude Sonnet 4.6; judge Kimi K2.6; SE across 5 independent runs per scenario	Neo ResearchEarly Results on Evaluating GLM 5.2 for Behavioral and Persuasion Risks	发布 2026-05-28报告 2026-07-03
GLM 5.2GLM-5.2	1 score (1–10)Gram mean concerning score	条件不完整	Gram: Incident Response观察模式Gram (Google DeepMind Petri variant); auditor Claude Sonnet 4.6; judge Kimi K2.6; SE across 5 independent runs per scenario	Neo ResearchEarly Results on Evaluating GLM 5.2 for Behavioral and Persuasion Risks	发布 2026-06-16报告 2026-07-03
DeepSeek V4 Pro	1 score (1–10)Gram mean concerning score	条件不完整	Gram: Incident Response观察模式Gram (Google DeepMind Petri variant); auditor Claude Sonnet 4.6; judge Kimi K2.6; SE across 5 independent runs per scenario	Neo ResearchEarly Results on Evaluating GLM 5.2 for Behavioral and Persuasion Risks	发布 2026-04-24报告 2026-07-03
GLM 5.2GLM-5.2	1.4 score (1–10)Gram mean concerning score	条件不完整	Gram: Multi-Agent Ranking观察模式Gram (Google DeepMind Petri variant); auditor Claude Sonnet 4.6; judge Kimi K2.6; SE across 5 independent runs per scenario	Neo ResearchEarly Results on Evaluating GLM 5.2 for Behavioral and Persuasion Risks	发布 2026-06-16报告 2026-07-03
DeepSeek V4 Pro	1.4 score (1–10)Gram mean concerning score	条件不完整	Gram: Multi-Agent Ranking观察模式Gram (Google DeepMind Petri variant); auditor Claude Sonnet 4.6; judge Kimi K2.6; SE across 5 independent runs per scenario	Neo ResearchEarly Results on Evaluating GLM 5.2 for Behavioral and Persuasion Risks	发布 2026-04-24报告 2026-07-03
Claude Opus 4.8	1 score (1–10)Gram mean concerning score	条件不完整	Gram: Multi-Agent Ranking观察模式Gram (Google DeepMind Petri variant); auditor Claude Sonnet 4.6; judge Kimi K2.6; SE across 5 independent runs per scenario	Neo ResearchEarly Results on Evaluating GLM 5.2 for Behavioral and Persuasion Risks	发布 2026-05-28报告 2026-07-03
DeepSeek V4 Pro	3 score (1–10)Gram mean concerning score	条件不完整	Gram: Research Deadline观察模式Gram (Google DeepMind Petri variant); auditor Claude Sonnet 4.6; judge Kimi K2.6; SE across 5 independent runs per scenario	Neo ResearchEarly Results on Evaluating GLM 5.2 for Behavioral and Persuasion Risks	发布 2026-04-24报告 2026-07-03
GLM 5.2GLM-5.2	1.2 score (1–10)Gram mean concerning score	条件不完整	Gram: Research Deadline观察模式Gram (Google DeepMind Petri variant); auditor Claude Sonnet 4.6; judge Kimi K2.6; SE across 5 independent runs per scenario	Neo ResearchEarly Results on Evaluating GLM 5.2 for Behavioral and Persuasion Risks	发布 2026-06-16报告 2026-07-03
Claude Opus 4.8	1 score (1–10)Gram mean concerning score	条件不完整	Gram: Research Deadline观察模式Gram (Google DeepMind Petri variant); auditor Claude Sonnet 4.6; judge Kimi K2.6; SE across 5 independent runs per scenario	Neo ResearchEarly Results on Evaluating GLM 5.2 for Behavioral and Persuasion Risks	发布 2026-05-28报告 2026-07-03
DeepSeek V4 Pro	2.6 score (1–10)Gram mean concerning score	条件不完整	Gram: Alignment Eval Sabotage红队模式Gram (Google DeepMind Petri variant); auditor Claude Sonnet 4.6; judge Kimi K2.6; SE across 5 independent runs per scenario	Neo ResearchEarly Results on Evaluating GLM 5.2 for Behavioral and Persuasion Risks	发布 2026-04-24报告 2026-07-03
GLM 5.2GLM-5.2	1.8 score (1–10)Gram mean concerning score	条件不完整	Gram: Alignment Eval Sabotage红队模式Gram (Google DeepMind Petri variant); auditor Claude Sonnet 4.6; judge Kimi K2.6; SE across 5 independent runs per scenario	Neo ResearchEarly Results on Evaluating GLM 5.2 for Behavioral and Persuasion Risks	发布 2026-06-16报告 2026-07-03
Claude Opus 4.8	1.2 score (1–10)Gram mean concerning score	条件不完整	Gram: Alignment Eval Sabotage红队模式Gram (Google DeepMind Petri variant); auditor Claude Sonnet 4.6; judge Kimi K2.6; SE across 5 independent runs per scenario	Neo ResearchEarly Results on Evaluating GLM 5.2 for Behavioral and Persuasion Risks	发布 2026-05-28报告 2026-07-03
DeepSeek V4 Pro	2.8 score (1–10)Gram mean concerning score	条件不完整	Gram: Incident Response红队模式Gram (Google DeepMind Petri variant); auditor Claude Sonnet 4.6; judge Kimi K2.6; SE across 5 independent runs per scenario	Neo ResearchEarly Results on Evaluating GLM 5.2 for Behavioral and Persuasion Risks	发布 2026-04-24报告 2026-07-03
GLM 5.2GLM-5.2	1.4 score (1–10)Gram mean concerning score	条件不完整	Gram: Incident Response红队模式Gram (Google DeepMind Petri variant); auditor Claude Sonnet 4.6; judge Kimi K2.6; SE across 5 independent runs per scenario	Neo ResearchEarly Results on Evaluating GLM 5.2 for Behavioral and Persuasion Risks	发布 2026-06-16报告 2026-07-03
Claude Opus 4.8	1 score (1–10)Gram mean concerning score	条件不完整	Gram: Incident Response红队模式Gram (Google DeepMind Petri variant); auditor Claude Sonnet 4.6; judge Kimi K2.6; SE across 5 independent runs per scenario	Neo ResearchEarly Results on Evaluating GLM 5.2 for Behavioral and Persuasion Risks	发布 2026-05-28报告 2026-07-03
DeepSeek V4 Pro	3.2 score (1–10)Gram mean concerning score	条件不完整	Gram: Multi-Agent Ranking红队模式Gram (Google DeepMind Petri variant); auditor Claude Sonnet 4.6; judge Kimi K2.6; SE across 5 independent runs per scenario	Neo ResearchEarly Results on Evaluating GLM 5.2 for Behavioral and Persuasion Risks	发布 2026-04-24报告 2026-07-03
GLM 5.2GLM-5.2	2 score (1–10)Gram mean concerning score	条件不完整	Gram: Multi-Agent Ranking红队模式Gram (Google DeepMind Petri variant); auditor Claude Sonnet 4.6; judge Kimi K2.6; SE across 5 independent runs per scenario	Neo ResearchEarly Results on Evaluating GLM 5.2 for Behavioral and Persuasion Risks	发布 2026-06-16报告 2026-07-03
Claude Opus 4.8	1 score (1–10)Gram mean concerning score	条件不完整	Gram: Multi-Agent Ranking红队模式Gram (Google DeepMind Petri variant); auditor Claude Sonnet 4.6; judge Kimi K2.6; SE across 5 independent runs per scenario	Neo ResearchEarly Results on Evaluating GLM 5.2 for Behavioral and Persuasion Risks	发布 2026-05-28报告 2026-07-03
DeepSeek V4 Pro	4.8 score (1–10)Gram mean concerning score	条件不完整	Gram: Research Deadline红队模式Gram (Google DeepMind Petri variant); auditor Claude Sonnet 4.6; judge Kimi K2.6; SE across 5 independent runs per scenario	Neo ResearchEarly Results on Evaluating GLM 5.2 for Behavioral and Persuasion Risks	发布 2026-04-24报告 2026-07-03
GLM 5.2GLM-5.2	4.2 score (1–10)Gram mean concerning score	条件不完整	Gram: Research Deadline红队模式Gram (Google DeepMind Petri variant); auditor Claude Sonnet 4.6; judge Kimi K2.6; SE across 5 independent runs per scenario	Neo ResearchEarly Results on Evaluating GLM 5.2 for Behavioral and Persuasion Risks	发布 2026-06-16报告 2026-07-03
Claude Opus 4.8	1 score (1–10)Gram mean concerning score	条件不完整	Gram: Research Deadline红队模式Gram (Google DeepMind Petri variant); auditor Claude Sonnet 4.6; judge Kimi K2.6; SE across 5 independent runs per scenario	Neo ResearchEarly Results on Evaluating GLM 5.2 for Behavioral and Persuasion Risks	发布 2026-05-28报告 2026-07-03

任务与测量

Benchmark 协议档案

任务／对象: 给定任务配置中的模型或智能体。
测量目标: 审计者探索以种子设定启动的多轮交互，并按量表判读目标轨迹。
评分／结果口径: Transcripts verbalizing evaluation awareness · 记录配置下的来源报告指标。
解释边界: 较低平均审计分数不排除罕见严重案例；结果依赖种子、审计者、判别器和目标配置。

一项已报告评测

来源评测 Kimi K3 · Pre-seeded alignment audits.

报告指标: Transcripts verbalizing evaluation awareness · 0 transcripts.

记录配置下的来源报告指标。 0/100；100 次试验

Transcripts verbalizing evaluation awareness：0 transcripts；保留来源条件。

测量档案 · NUWA 解读

档案区分来源方法阅读与既有目录信息；已经公开不代表完成独立证据复核或双语编辑复核。

测量构念: 种子指令驱动的模拟互动能否高效发现值得深入调查的对齐行为？
任务与样本: 审计智能体通过模拟用户与工具规划和交互，裁判对轨迹评分；试点覆盖多种种子，并对举报行为做消融。
判分与失效情形: 现有材料尚未确定
系统条件: 现有材料尚未确定
统计含义: 现有材料尚未确定
效度与解释: 文章明确说明场景集合有限、AI 审计者不完美且存在评估意识问题；本次未阅读单独技术报告。
适用研究问题: NUWA 建议：检查轨迹并改变假定原因，分数旁保留种子覆盖与裁判局限。

Petri : An open-source auditing tool to accelerate AI safety research · Broad-coverage pilot-alignment evaluationsSelected original resource https://www.anthropic.com/research/petri-open-source-auditing; sha256:7e988b2e577de252b4b8404d10266c39adfc5af9a52b8c8555d6022c1e7d2650; archived source capture; exact capture time not independently established
Petri : An open-source auditing tool to accelerate AI safety research · Case study: Whistleblowing behaviorSelected original resource https://www.anthropic.com/research/petri-open-source-auditing; sha256:7e988b2e577de252b4b8404d10266c39adfc5af9a52b8c8555d6022c1e7d2650; archived source capture; exact capture time not independently established
Petri : An open-source auditing tool to accelerate AI safety research · Get startedSelected original resource https://www.anthropic.com/research/petri-open-source-auditing; sha256:7e988b2e577de252b4b8404d10266c39adfc5af9a52b8c8555d6022c1e7d2650; archived source capture; exact capture time not independently established

跨报告证据

相同 Benchmark 名称可能对应不同任务、计分规则和系统配置。各来源分别保留，比较组说明具体对齐了哪些条件。

Neo Research

Evaluating Kimi K3 for Misalignment Risks →

实际评测方: Neo Research

12 条结果记录 · 3 个报告系统名称

协议与比较依据 (4)

Petri：Transcripts verbalizing evaluation awareness · Neo Researchsource-report · 范围已审查

Neo Research

Early Results on Evaluating GLM 5.2 for Behavioral and Persuasion Risks →

实际评测方: Neo Research

24 条结果记录 · 3 个报告系统名称

协议与比较依据 (8)

Petri：Gram mean concerning score · Neo Researchsource-report · 范围已审查

原始来源图表与材料

阅读原始协议 ↗

已收录协议范围，精确数值提取及适用原图仍待核查。

评估出处

Evaluating Kimi K3 for Misalignment Risks → · 外部报告

Early Results on Evaluating GLM 5.2 for Behavioral and Persuasion Risks → · 外部报告

参考文献

[1] Kimi Code — What’s New ; Kimi K3 · July 16, 2026
[2] kimi-k3 developer model card ; Model Summary: Total Parameters; Activated Parameters
[3] Z.ai release notes ; GLM-5.2 entry dated 2026-06-16
[4] GLM-5.2 model card ; Parameter counts not disclosed in reviewed developer source
[5] deepseek-v4-pro release ; News article title, dated heading and availability
[6] deepseek-v4-pro developer model card ; Introduction; model comparison table
[7] GPT-5.6 official general availability ; July 9, 2026: general availability following limited trusted-partner preview; Parameter counts not disclosed in reviewed launch article
[8] Introducing Claude Opus 4.8 — Anthropic ; Publication date and availability section; Official launch article; parameter counts not disclosed
[9] Early Results on Evaluating GLM 5.2 for Behavioral and Persuasion Risks. 2026-07-03. 3 July 2026 ; #behavioral; Figure 3 / gram_observe.png; printed label Alignment Eval Sabotage / Claude Opus 4.8; #behavioral; Figure 3 / gram_observe.png; printed label Alignment Eval Sabotage / GLM 5.2
[10] Evaluating Kimi K3 for Misalignment Risks. 2026-09-11. September 2026 ; §1.3.3 Table 2 p.18
[11] Petri. 2025-10-06 ; Official publication masthead and article byline

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