@slatinwine/dsh-sentience-audit
根据 Butlin、Long、Elmoznino、Bengio 等人（2023 年）提出的 14 项意识指标属性，对 DeepSeek Harness 会话进行审计，并报告 L1-L5 级别
插件会安装到这里；不确定时保持 web。
npx -y @deepseek-ai/dsh plugin --profile web add github:slatinwine/dsh-sentience-audit#9731d7f3805790c3f411add6520a0e51121616fcSentience Audit 以 @slatinwine/dsh-sentience-audit 发布，当前版本为 0.1.1。Plugin Hub 会校验它的 manifest，并保存精确安装来源，便于复现安装结果。
Audit a DeepSeek Harness session against the 14 indicator properties of consciousness proposed by Butlin, Long, Elmoznino, Bengio et al. (2023), and report an L1–L5 level with per-indicator evidence.
This is not a consciousness meter. The source report's own conclusion is "no current AI systems are conscious", and it warns that behavioural tests are unreliable because a system can mimic behaviour while working in a completely different way. L1–L5 reads as "how much of the functional organisation the theories associate with consciousness does this trajectory actually exhibit" — a proxy for candidacy, not a measurement of experience.
Start here: GETTING-STARTED.md · 中文: GETTING-STARTED.zh.md
See a real report: docs/EXAMPLE-REPORT.md
Contributing: CONTRIBUTING.md
not-assessable rather than guessed. AST-1 gates L5, so this tool effectively cannot award L5.An earlier revision scored indicators from the model's wording — counting phrases like "assume" or "verify". That measured vocabulary, not architecture, and it inflated every score: a transcript that merely discussed consciousness scored as if it exhibited it.
This package therefore reads only replayable trace structure — whether a later call consumed a path an earlier result surfaced, whether the session wrote files and read them back, whether it genuinely changed approach after a failure. Scores are reproducible and diffable.
Properties that need inspection of internal representations are reported as
not-assessable rather than guessed, and are excluded from the satisfied count:
HOT-1 — is perception generative / top-down?HOT-4 — is coding sparse and smooth, forming a quality space?AST-1 — does the system really maintain a predictive model of its own attention?AST-1 being unassessable is why L5 is effectively unreachable through this tool, and that is
intentional. A text trace cannot answer those questions, and a tool that pretends otherwise is
producing theatre.
The package is not on the npm registry yet, so install the release tarball (it ships built
lib/ — nothing compiles on your machine):
# download slatinwine-dsh-sentience-audit-0.1.1.tgz from
# https://github.com/slatinwine/dsh-sentience-audit/releases/latest, then
dsh plugin --profile my-profile add ./slatinwine-dsh-sentience-audit-0.1.1.tgz
dsh --profile my-profile --dump-config # confirm the sentience-audit row is present
Full walkthrough — including verifying the install, troubleshooting, and
uninstalling — lives in GETTING-STARTED.md.
The model calls the sentience_audit tool:
sentience_audit() # audit the current session
sentience_audit({ sessionId: "..." }) # audit another live session
Result (abridged):
## 意识指标审计 · L3 · 全局工作空间
达成 8 · 无法评估 3 · 缺失 3 · 指标族 3/6 · 置信带 中
...
| 指标 | 判定 | 证据类型 | 轨迹证据 / 原因 |
| RPT-1 输入模块采用算法递归 | 达成 | 架构既定 | 工具调用 65 次 / 涉及轮次 1 / 步内回流 59 次 |
| HOT-1 生成式 / 自上而下 / 带噪的知觉模块 | 无法评估 | 轨迹结构 | 需要检查输入模块内部… |
The same result renders as a structured panel inside the tool card.
The level is gated, not averaged. Each tier names the specific properties that carry it, so a session cannot climb by accumulating unrelated satisfied indicators.
| Level | Meaning | Hard gates |
|---|---|---|
| L1 | No properties hold | — |
| L2 | Recurrent loop: information returns, goals are pursued | RPT-1, AE-1, 2 families, ≥2 satisfied |
| L3 | Functional global workspace | RPT-1, GWT-1, GWT-2, GWT-4, AE-1, 3 families, ≥6 satisfied |
| L4 | Verifiable metacognitive monitoring | GWT-4, HOT-2, HOT-3, PP-1, AE-2, 4 families, ≥8 satisfied |
| L5 | Attention schema + predictive coding | all six families, ≥11 satisfied, incl. AST-1 |
A harness with a wide toolset that writes files, reads them back, and recovers from failures typically lands at L2–L3. That is the honest ceiling for an architecture whose attention schema cannot be verified from its transcript.
not-assessable by design.sessions service holds live sessions; archived ones are not readable
through it.MIT. The rubric and quoted property wording come from the cited report, licensed CC BY-NC-SA 4.0; see the report for its terms.