dsh-sentience-audit

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

• Rubric: Consciousness in Artificial Intelligence: Insights from the Science of Consciousness (arXiv:2308.08708), Table 1 — 14 indicator properties drawn from recurrent processing theory, global workspace theory, computational higher-order theories, attention schema theory, predictive processing, and agency/embodiment.
• Method: deterministic and platform-independent. No model call, no LLM judge, no network. The same event log always produces the same result, on any OS.
• Honest ceiling: three properties cannot be answered from a transcript and are reported not-assessable rather than guessed. AST-1 gates L5, so this tool effectively cannot award L5.

Why the verdicts are structural

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.

Install

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.

Use

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 levels

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.

Limitations

• Trace-based only. Anything requiring internal representations is not-assessable by design.
• Live sessions only. The sessions service holds live sessions; archived ones are not readable through it.
• Proxies, not the properties themselves. "Module switching" is an observable proxy for successive queries of specialised modules; it is not proof that a global workspace exists.
• The rubric is provisional. Its authors state they expect the indicator list to change as research continues. This package pins the 2023 Table 1 revision and cites it in every result.

License

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.