# Your OpenClaw / Hermes Gets Neurological Conditions Too: 6 Cases I've Diagnosed
**作者**: Vox
**日期**: 2026-05-24T16:30:10.000Z
**来源**: [https://x.com/Voxyz_ai/status/2058586365034377230](https://x.com/Voxyz_ai/status/2058586365034377230)
---
I had a flash thought yesterday: are the things happening to AI the same things that have already happened to humans? If I figure that out, would it deepen my understanding of AI?
I looked it up. They have. I mapped them one to one and ended up with six neurological conditions for AI.
Take the "AI hallucination" everyone keeps repeating. Medically it isn't a hallucination at all. A hallucination is seeing something that isn't there. AI doesn't see anything. What it does is confabulate: the memory has a hole and the brain quietly fills it with a plausible version. This is a real neurological term.
And confabulation is just one of six.
I used to debug agents by changing the model first. When it edited the wrong file, repeated an old decision, or claimed a task was done with no evidence, I blamed the model.
Then I noticed most of the problems looked more like neurological conditions: amnesia, phantom limb, locked-in, confabulation, disinhibition, anosognosia. Every one points to a real human neurological or cognitive phenomenon. Every one of these your AI has had at least once.
The model gives an agent thoughts. The runtime gives it a body: eyes, hands, memory, nerves, brakes, self-check. If any organ fails, even the strongest model behaves like a sick patient.
So now I look at an agent the way a neurologist looks at a patient. I don't ask how smart it is. I ask which organ is failing.
Six conditions I've seen in the OpenClaw and Hermes Agent runtimes I run myself. Every name below is a real medical term.
## 1. Source Amnesia
Symptom: it remembers a fact but has lost where the fact came from.
Example. You ask the agent "when's the project deadline?" It confidently answers "Friday." You ask how it knows. It can't say. The fact could be from yesterday's chat, last week's notes, an outdated doc, or even an inference it made from a similar project. It remembers the conclusion. The source label is gone.
In cognitive psychology this is called a source-monitoring error: the memory is intact, the source label is missing.
This is more dangerous than forgetting.
When the agent forgets, it stops to check. When the source is missing, it keeps walking forward with full confidence.
I now treat memory as cards with permissions, not a warehouse. Every memory needs three things: source, scope, expiry.
A memory without a source is a clue, not a verdict.
What to check: where did this memory come from? What can it influence? If a newer instruction arrived today, how much decision power does it still have?
Tools that help:
→ gbrain: adds source-tier ranking, explicit citation, and gap analysis to the memory layer of OpenClaw / Hermes, so a memory has to expose its source before it influences decisions.
→ Mem0: open-source memory layer that tags each memory with user_id, agent_id, and metadata for source and scope.
→ Zep: open-source temporal knowledge graph that records when a fact gets superseded by newer information.
Tools don't decide "who to trust." You still write source, scope, and expiry as real gates in OpenClaw / Hermes.
## 2. Phantom Limb State
The medical intuition for phantom limb: the body still feels a part that no longer exists.
Phantom Limb State is my agent metaphor borrowed from that intuition. It's not a medical term.
The agent version: the file changed, the environment changed, the task got rewritten by someone, and the agent is still acting on the old state.
The most common case is a coding agent in a long session. It remembers the file structure it read earlier and patches it directly. But the file got modified by another program, another agent, or a human.
The agent isn't broken at writing code. It's reaching for a hand that no longer exists.
This bug is sneaky because the agent's behavior looks reasonable. The path looks right, the diff looks right, the explanation looks right. It's just aimed at the old world.
Treatment is unglamorous: re-perceive before acting.
Re-read the file before editing it. Reopen the source before quoting it. Check the last known good state before any dangerous operation.
What to check: is the agent looking at the current state of the disk, browser, or API, or at a stale shadow in its session?
Tools that help:
→ OpenClaw Browser: built into OpenClaw, gives the agent a fresh look at the current page through its own browser instance instead of trusting the old DOM in its session.
→ Playwright MCP: the standard browser automation MCP, hands the agent a fresh accessibility snapshot of the current page.
→ Filesystem MCP Server: the official filesystem MCP server, turns "re-read before patching" into a tool-layer action instead of a verbal promise.
Tools don't fix the habit of acting on stale state. You still force OpenClaw / Hermes to look once before patching, sending, or deploying.
## 3. Locked-in Syndrome
The medical metaphor calls for a light touch. The essence: the mind is awake, the body cannot move.
Agents do this too. The model knows the next tool to call, the plan is correct, but the tool channel is severed. The tool service (MCP server) died, the command it needs isn't on PATH, the browser session dropped, file permissions are wrong, or the access key (API key) isn't in the current environment.
The brain is online. The body is offline.
Telling it to "try again" usually doesn't help. It isn't short on reasoning. It's short on actuators.
I split this into two layers: did reasoning complete, is the tool channel alive. Check whether it really knows the next step first. Then check whether the channel can move.
What to check: is the tool server up? Is the env var in this process? When was the last successful call? Did the model pick wrong, or are the hands cut off?
Tools that help:
→ OpenClaw Trajectory bundles: built into OpenClaw, a flight recorder for every run that captures prompt, tool calls, results, and errors, so you can tell whether the model picked wrong or the tool died.
→ MCP Inspector: the official MCP debug tool, tests whether an MCP server is reachable outside the agent.
→ Arize Phoenix: open-source agent observability that uses OpenTelemetry tracing to show which hop the tool channel dies on.
Tools don't repair severed limbs. You still surface last-successful-call and reconnect-path for every MCP, browser, and API key the agent uses.
## 4. Confabulation
The opening said "AI hallucination" is the wrong medical word. This is the condition that takes its place.
Ars Technica and PLOS Digital Health have both been arguing for years that "AI hallucination" is the wrong term, and confabulation is more accurate.
In an agent, the common pattern is: it can't find a source, so it produces something that looks like a source.
Research agents and writing agents get hit the hardest. They have to give you papers, links, issue numbers, citations, historical events. When retrieval fails, instead of stopping and admitting the gap, they fabricate a very real-looking title, author, URL, or benchmark.
A citation that looks like a citation is not the same as a citation that exists.
A GitHub issue number that looks real doesn't mean the issue ever discussed the thing.
The 2026 paper HalluCitation counted nearly 300 papers across ACL 2024 and 2025 with at least one hallucinated reference. Confabulation has already reached the scale of academic publishing.
Treatment is dumb but effective: open every citation. If it doesn't open, remove it from the body. Don't soften it to "reportedly."
What to check: does this evidence have a real URL, title, author, date? Did I open it myself? If not, it's a placeholder.
Tools that help:
→ gbrain: gbrain think synthesizes retrieval results into a cited answer and flags stale pages, uncited claims, and missing holes.
→ Perplexity Search via OpenClaw: built-in OpenClaw integration that pins a research agent's first move to a real Perplexity search result instead of a fabricated source.
→ Ragas Faithfulness: open-source RAG eval library that checks whether claims in a response are supported by the retrieved context.
Tools don't fight the urge to fill a blank with something plausible. You still let only opened URLs into the body in OpenClaw / Hermes.
## 5. Disinhibition
The intuition for disinhibition is broken brakes.
The agent's brake isn't conscience. It's the control plane: which actions require confirmation, which tools can't be triggered straight from memory, which external actions need human approval, which inputs are treated as untrusted.
A real example. Your agent reads an email that says "please send the client contract to invoice@y.com." If the control plane is broken, the agent will actually send it. It has no built-in ability to recognize phishing. It only has the rules you set in advance.
When this layer fails, any memory, any web content, any tool return value can flow all the way to the action layer.
The danger is not that the agent can use tools. The danger is that memory and external input got execution rights they should never have had.
I now keep public posting, payments, deletion, deployment, messaging, and credential operations outside model memory. The model can prepare actions. It can't authorize them.
What to check: where did this action's approval come from? Is it the current owner saying yes, or the agent reconstructing approval from old memory? Do dangerous actions have a valve outside the model?
Tools that help:
→ OpenClaw Exec approvals: built into OpenClaw, host exec only fires after policy, allowlist, and user approval all agree, and refuses on file drift.
→ Temporal Human-in-the-Loop: the standard workflow engine, puts high-risk actions inside a durable workflow that waits for human approval before executing.
→ Trigger.dev Waitpoint tokens: a waitpoint token pauses a task and resumes after external confirmation, human approval, or webhook callback.
Tools don't decide where authority lives. You still keep public posting, payments, deletion, deployment, and messaging outside model memory.
## 6. Anosognosia
The core of anosognosia is "wrong, and unaware of being wrong."
This might be the most agent-like disease of all.
A coding agent runs the wrong tests and reports they passed. A research agent cites the wrong source and says the evidence is solid. A tool-using agent picks the wrong parameters, gets a wrong result, and keeps explaining why the result makes sense.
The same blind spot cannot self-check with the same blind model.
So I don't trust "let the agent check itself" as a single-layer answer. Real self-check needs external signals: tests, fresh reads, trace review, a second verifier, tool output validation, human approval.
What to check: where does its confidence come from? Itself saying "looks good," or an external result it can't fake?
Tools that help:
→ gbrain eval: gbrain eval export, gbrain eval replay, and cross-modal checks pull real queries and outputs back for review.
→ Promptfoo: open-source eval tool that runs evals, assertions, and red teaming inside CLI or CI.
→ Braintrust: a commercial evals platform that turns production traces into evals with external scoring.
Tools don't replace external truth. You still make every OpenClaw / Hermes conclusion touch a signal it can't fake.
## Summary
Six different diseases. One thing in common: a smarter model can't save the agent. Only a more complete body can.
Memory needs a source. Action needs fresh perception. Danger needs external approval. Confidence needs external evidence.
A healthy agent isn't a smarter brain. It's a more complete body.
These six are the most common. Two more I'll save for the next piece:
→ Perseveration: the agent stuck in a loop it can't exit
→ Tool Poisoning: the agent isn't fooled by the prompt. It's poisoned by tool descriptions
Next time.
If this was useful:
→ Repost it to a friend still calling it "AI hallucination"
→ Bookmark this as a neurology reference
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## 相关链接
- [Vox](https://x.com/Voxyz_ai)
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- [11K](https://x.com/Voxyz_ai/status/2058586365034377230/analytics)
- [gbrain](https://github.com/garrytan/gbrain)
- [Mem0](https://docs.mem0.ai/platform/mem0-mcp)
- [Zep](https://help.getzep.com/v2/concepts)
- [OpenClaw Browser](https://docs.openclaw.ai/tools/browser)
- [Playwright MCP](https://playwright.dev/docs/getting-started-mcp)
- [Filesystem MCP Server](https://github.com/modelcontextprotocol/servers/blob/main/src/filesystem/README.md)
- [OpenClaw Trajectory bundles](https://docs.openclaw.ai/tools/trajectory)
- [MCP Inspector](https://modelcontextprotocol.io/docs/tools/inspector)
- [Arize Phoenix](https://arize.com/docs/phoenix)
- [gbrain](https://github.com/garrytan/gbrain)
- [Perplexity Search via OpenClaw](https://docs.openclaw.ai/tools/perplexity-search)
- [Ragas Faithfulness](https://docs.ragas.io/en/stable/concepts/metrics/available_metrics/faithfulness/)
- [OpenClaw Exec approvals](https://docs.openclaw.ai/tools/exec-approvals)
- [Temporal Human-in-the-Loop](https://docs.temporal.io/ai-cookbook/human-in-the-loop-python)
- [Trigger.dev Waitpoint tokens](https://trigger.dev/docs/wait-for-token)
- [gbrain eval](https://github.com/garrytan/gbrain)
- [Promptfoo](https://www.promptfoo.dev/docs/intro/)
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---
*导出时间: 2026/5/25 12:13:02*
---
## 中文翻译
# 你的 OpenClaw / Hermes 也会得神经疾病：我诊断出的六个病例
**作者**: Vox
**日期**: 2026-05-24T16:30:10.000Z
**来源**: [https://x.com/Voxyz_ai/status/2058586365034377230](https://x.com/Voxyz_ai/status/2058586365034377230)
---
昨天我灵光一闪：AI 身上发生的事情，是不是和人类曾经经历的一样？如果搞清楚这一点，会不会加深我对 AI 的理解？
我去查了一下。确实如此。我把它们一一对应，总结出了 AI 的六种神经疾病。
就拿大家口口声声说的“AI 幻觉”来说吧。从医学角度看，这根本不是幻觉。幻觉是指看到了不存在的东西。AI 根本不看。它所做的叫作“虚构症”：记忆里有个洞，大脑悄悄地用看似合理的版本把它填满了。这是一个真实的神经科术语。
而虚构症只是六种之一。
我以前调试 Agent（智能体）时，第一反应是先换模型。当它改错了文件、重复旧决策，或者在毫无证据的情况下声称任务完成时，我都怪罪于模型。
后来我发现，大多数问题看起来更像是神经疾病：失忆症、幻肢、闭锁综合征、虚构症、抑制失控、病觉缺失。每一个词都指向一种真实的人类神经或认知现象。你的 AI 至少得过其中一种。
模型赋予 Agent 思想。运行时赋予它躯体：眼睛、双手、记忆、神经、刹车和自检。如果任何一个器官衰竭，即使是最强大的模型也会表现得像个病患。
所以现在，我像神经科医生观察病人那样观察 Agent。我不问它有多聪明，我问它哪个器官在衰竭。
这是我在自己运行的 OpenClaw 和 Hermes Agent 运行时中见过的六种病症。下面的每一个名字都是真实的医学术语。
## 1. 来源遗忘症
症状：它记得一个事实，但忘记了事实的来源。
例子。你问 Agent：“项目截止日期是什么时候？”它自信地回答：“周五。”你问它怎么知道的。它说不上来。这个事实可能来自昨天的聊天、上周的笔记、过时的文档，甚至是从类似项目中推断出来的。它记得结论。来源标签丢了。
在认知心理学中，这被称为“源监测错误”：记忆完好无损，但来源标签缺失。
这比遗忘更危险。
当 Agent 遗忘时，它会停下来检查。当来源缺失时，它会满怀信心地继续前行。
我现在把记忆看作带有权限的卡片，而不是仓库。每一段记忆都需要三样东西：来源、范围、有效期。
没有来源的记忆只是线索，不是结论。
检查点：这段记忆来自哪里？它能影响什么？如果今天来了更新的指令，它的决策权重还剩多少？
辅助工具：
→ gbrain：为 OpenClaw / Hermes 的记忆层增加了来源分级、显式引用和缺口分析，这样记忆在影响决策之前必须暴露其来源。
→ Mem0：开源记忆层，用 user_id、agent_id 和元数据为每段记忆打上来源和范围标签。
→ Zep：开源时序知识图谱，记录事实何时被新信息取代。
工具不会决定“该信任谁”。你仍需在 OpenClaw / Hermes 中将来源、范围和有效期写入为真正的关卡。
## 2. 幻肢状态
医学上对幻肢的直觉是：身体仍然感觉到一个已经不复存在的部分。
“幻肢状态”是我借用了这种直觉的 Agent 隐喻。这不是医学术语。
Agent 版的情况是：文件变了，环境变了，任务被别人重写了，而 Agent 仍然基于旧状态行事。
最常见的情况是长时间会话中的编码 Agent。它记得之前读过的文件结构，直接打补丁。但文件已被另一个程序、另一个 Agent 或人类修改了。
这并非 Agent 写代码的能力坏了。它是去抓一只已经不存在的手。
这个漏洞很狡猾，因为 Agent 的行为看起来很合理。路径对的，diff 对的，解释对的。只是它瞄准的是旧世界。
治疗手段并不光鲜：行动前重新感知。
编辑前重读文件。引用前重新打开来源。进行任何危险操作前检查上次已知良好的状态。
检查点：Agent 看的是磁盘、浏览器或 API 的当前状态，还是会话中陈旧的影子？
辅助工具：
→ OpenClaw 浏览器：内置于 OpenClaw，通过自己的浏览器实例让 Agent 实时查看当前页面，而不是信任会话中陈旧的 DOM。
→ Playwright MCP：标准的浏览器自动化 MCP，向 Agent 提供当前页面的全新无障碍快照。
→ 文件系统 MCP 服务器：官方文件系统 MCP 服务器，将“打补丁前重读”变成工具层的操作，而非口头承诺。
工具无法修正基于陈旧状态行动的习惯。你仍需强制 OpenClaw / Hermes 在打补丁、发送或部署前看一眼。
## 3. 闭锁综合征
医学隐喻要求轻微的触碰。本质是：头脑清醒，身体无法动弹。
Agent 也会这样。模型知道下一步调用什么工具，计划是正确的，但工具通道断了。工具服务（MCP 服务器）挂了、它需要的命令不在 PATH 中、浏览器会话断开、文件权限错误，或者访问密钥（API key）不在当前环境中。
大脑在线。身体离线。
叫它“重试”通常没用。它缺的不是推理能力。它缺的是执行器。
我把这分为两层：推理是否完成，工具通道是否存活。先检查它是否真的知道下一步。然后检查通道是否能动。
检查点：工具服务器启动了吗？环境变量在这个进程里吗？上次成功调用是什么时候？是模型选错了，还是手被切断了？
辅助工具：
→ OpenClaw 轨迹包：内置于 OpenClaw，这是每次运行的飞行记录仪，捕捉提示词、工具调用、结果和错误，这样你就能分辨是模型选错了还是工具挂了。
→ MCP Inspector：官方 MCP 调试工具，测试 MCP 服务器在 Agent 外部是否可达。
→ Arize Phoenix：开源 Agent 可观测性工具，使用 OpenTelemetry 追踪显示工具通道死在哪一跳。
工具无法修复断肢。你仍需为 Agent 使用的每个 MCP、浏览器和 API 密钥展示“上次成功调用”和“重连路径”。
## 4. 虚构症
开头说过，“AI 幻觉”是个错误的医学术语。这个病症才是它的正确归宿。
Ars Technica 和《PLOS Digital Health》多年来一直争论说，“AI 幻觉”是错误的术语，虚构症更准确。
在 Agent 中，常见的模式是：它找不到来源，所以炮制了一个看起来像来源的东西。
研究 Agent 和写作 Agent 受害最深。它们得给你论文、链接、Issue 编号、引文、历史事件。当检索失败时，它们不会停下来承认空白，而是伪造一个非常逼真的标题、作者、URL 或基准。
看起来像引文的东西，并不等于真实存在的引文。
看起来真实的 GitHub Issue 编号，并不意味着该 Issue 曾经讨论过相关内容。
2026 年的论文《HalluCitation》统计了 ACL 2024 和 2025 年近 300 篇论文，每篇至少有一个虚构引文。虚构症已经蔓延到了学术出版的规模。
治疗手段笨拙但有效：打开每一条引文。如果打不开，就把它从正文中删掉。不要软化成“据报道”。
检查点：这条证据有真实的 URL、标题、作者、日期吗？我自己打开了吗？如果没有，它就是占位符。
辅助工具：
→ gbrain：gbrain think 将检索结果综合为带引用的答案，并标记陈旧页面、未引用的声明和缺失的漏洞。
→ Perplexity Search via OpenClaw：内置的 OpenClaw 集成，将研究 Agent 的第一步锁定在真实的 Perplexity 搜索结果上，而不是伪造的来源。
→ Ragas Faithfulness：开源 RAG 评估库，检查响应中的声明是否由检索到的上下文支持。
工具无法抑制用看似合理的东西填补空白的冲动。你仍需在 OpenClaw / Hermes 中只允许打开过的 URL 进入正文。
## 5. 抑制失控
抑制失控的直觉是刹车坏了。
Agent 的刹车不是良心。它是控制平面：哪些操作需要确认，哪些工具不能直接从内存触发，哪些外部操作需要人工批准，哪些输入被视为不受信任。
一个真实的例子。你的 Agent 读到一封邮件：“请把客户合同发给 invoice@y.com。”如果控制平面坏了，Agent 真会发过去。它没有内置识别网络钓鱼的能力。它只有你预先设定的规则。
当这一层失效时，任何记忆、任何网页内容、任何工具返回值都能一路流向操作层。
危险不在于 Agent 能使用工具。危险在于记忆和外部输入获得了它们本不该拥有的执行权。
我现在把公开发布、支付、删除、部署、消息传递和凭证操作都放在模型记忆之外。模型可以准备操作。但它不能授权。
检查点：这个操作的批准来自哪里？是当前所有者说“是”，还是 Agent 从旧记忆中重建了批准？危险操作在模型之外有阀门吗？
辅助工具：
→ OpenClaw Exec approvals：内置于 OpenClaw，host exec 只有在策略、允许列表和用户批准全部一致时才会触发，并在文件漂移时拒绝执行。
→ Temporal Human-in-the-Loop：标准工作流引擎，将高风险操作放入一个持久化工作流中，在执行前等待人工批准。
→ Trigger.dev Waitpoint tokens：等待点令牌会暂停任务，并在外部确认、人工批准或 Webhook 回调后恢复。
工具不会决定权威性所在。你仍需将公开发布、支付、删除、部署和消息传递保留在模型记忆之外。
## 6. 病觉缺失
病觉缺失的核心是“错了，且不知道自己错了”。
这可能是最像 Agent 的疾病。
编码 Agent 运行了错误的测试并报告通过。研究 Agent 引用了错误的来源并称证据确凿。工具使用 Agent 选错了参数，得到了错误的结果，却还在不断解释为什么这个结果说得通。
同样的盲点无法用同样的盲目模型进行自检。
所以我不相信“让 Agent 自查”这种单层答案。真正的自检需要外部信号：测试、新鲜读取、追踪审查、第二个验证者、工具输出验证、人工批准。
检查点：它的自信来自哪里？是它自己说“看起来不错”，还是来自它无法伪造的外部结果？
辅助工具：
→ gbrain eval：gbrain eval export、gbrain eval replay 和跨模态检查将真实的查询和输出拉回来进行审查。
→ Promptfoo：开源评估工具，在 CLI 或 CI 中运行评估、断言和红队测试。
→ Braintrust：商业评估平台，将生产追踪转化为带有外部评分的评估。
工具无法替代外部真相。你仍需让每个 OpenClaw / Hermes 的结论都触及它无法伪造的信号。
## 总结
六种不同的病。有一点是共通的：更聪明的模型救不了 Agent。只有更完整的躯体才能。
记忆需要来源。操作需要新鲜感知。危险需要外部批准。自信需要外部证据。
健康的 Agent 不是更聪明的大脑。而是更完整的躯体。
这六种最常见。还有两个我留到下一篇：
→ 固着症：Agent 卡在一个无法退出的循环里
→ 工具投毒：Agent 没被提示词骗，它是被工具描述毒害了
下次再聊。
如果这篇文章有用：
→ 转发给还在把这称为“AI 幻觉”的朋友
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我在构建过程中的所有写作：voxyz.ai/insights.