- someone sets up an “ask human as a service mcp”
- demand quickly outstrips offer of humans willing to help bots
- someone else hooks up AI to the “ask human saas”
- we now have a full loop of machines asking machines
Finally, the "AI" turns out to be 700 Indians. We now have the full loop of humans asking machines asking humans pretending to be machines. Civilisation collapses
reasoning models know when they are close to hallucinating because they are lacking context or understanding and know that they could solve this with a question.
this is a streamlined implementation of a interanlly scrapped together tool that i decided to open-source for people to either us or build off of.
> reasoning models know when they are close to hallucinating because they are lacking context or understanding and know that they could solve this with a question.
> reasoning models know when they are close to hallucinating because they are lacking context or understanding and know that they could solve this with a question
You've just described AGI.
If this were possible you could create an MCP server that has a continually updated list of FAQ of everything that the model doesn't know.
this is mainly meant as a way to conversate with the model while you are programming with it. This is not meant to pull questions to a team but more to pair program. a markdown file is best for syntax in an llm prompt and also just easiest to have open and answer questions with. If i had more time and could i would build an extension into cursor.
Why not have the model ask in the chat? It's a lot easier to just talk to it than open a file. The article mentions cursor so it sounds like you're already using cursor?
would probably work better, this is just how i threw it together as an internal tool a long time ago. i just improved it and shipped it to opensource it.
Human in the loop means despite your best efforts at initial prompting (which is what rules are), there will always be the need to say "no, that's wrong, now do this instead". Expecting to be able to write enough rules for the model to work fully autonomously through your problem is indeed wishing for AGI.
In my example, the human would be in the loop in exactly the same way as the technique in the article. The human can tell the model that it's wrong and what to do instead.
Tools like th one in the article are also "rules".
> (problem description) your agent […] makes confident assumptions
> (solution description) when it’s unsure
I read this as a contradiction: in one sentence you describe the problem as an agent being confident while hallucinating and in the next phrase the solution is that the agent can ask you if it’s unsure.
You tool is interesting but you may consider rephrasing that part.
I'll try to give you credit for more than dismissing my question off-hand...
Yes, it may not need to know with perfect certainty when it's unsure or stuck, but even to meet a lower bar of usefulness, it'll need at least an approximate means of determining that its knowledge is inadequate. To purport to help with the hallucination problem requires no less.
To make the issue a bit more clear, here are some candidate components to a stuck() predicate:
- possibilities considered
- time taken
- tokens consumed/generated (vs expected? vs static limit? vs dynamic limit?)
If the unsure/stuck determination is defined via more qualitative prompting, what's the prompt? How well has it worked?
I don't believe[1] any of those are part of the MCP protocol - it's essentially "the LLM decided to call it, with X arguments, and will interpret the results however it likes". It's an escape hatch for the LLM to use to do stuff like read a file, not a monitoring system that acts independently and has control over the LLM itself.
(But you could build one that does this, and ask the LLM to call it and give your MCP that data... when it feels like it)
So you'd be using this by telling the LLM to run it when it thinks it's stuck. Or needs human input.
1: I am not anything even approaching deeply knowledgeable about MCP, so please, someone correct me if I'm wrong! There do seem to be some bi-directional messaging abilities, e.g. notification, but to figure out thinking time / token use / etc you would need to have access to the infrastructure running the LLM, e.g. Cursor itself or something.
You are trying to control a system that is inherently chaotic.
You can probably get some where by indeed running a task 1000 times and looking for outliers in the execution time or token count. But that is of minimal use and anything more advanced than that is akin to water divining.
The system is only nondeterministic (and a model of nondeterminism at that) when it's emitting tokens. It (the system) becomes completely deterministic when it calls a tool and a result is returned from the tool.
This is little different than how I wrote this. Now it is deterministic, when I hit reply.
the reasoning aspect of most llms these days knows when its unsure or stuck, you can get that from its thinking tokens. It will see this mcp and call it when its in that state. Though this could benefit from some rules file to use it, although cursor doesn't quite follow ask for help rules, hence making this.
Does all thinking end up getting replaced by calls to Ask-human-mcp then? Or only thinking that exceeds some limit (and how do you express that limit)?
The same technique can be had by creating a "universal MCP tool" for the LLM to use if it thinks the existing tools aren't up to the job. The MCP language calls these "proxies".
The prompt is (mostly) built using the tool loads in the MCP server. In Python, the @mcp.tool() decorators provide the context of tool to the prompt, which is then submitted (I believe) with each call to the LLM.
If the model responds with an obvious incorrect answer or hallucination, start over. Rephrase your input. Consider what output you are actually after... Adding to original shit output wont help you.
Not certain that your definition of hallucination matches mine precisely. Having said that, this is so simple yet kinda brilliant. Surprised it's not a more popular concept already.
I am running an electronics/medialab in an university, the amount of fires bad electronics advice from LLMs caused already is probably non-zero.
It is amazing how bad LLMs are when it comes to reasoning about simple dynamics within trivial electronic circuits and how eager they are to insist the opposite of how things work in the real world is the secured truth.
Not much is stopping you from buying products from a retailer and selling them at a wholesaler, but you'd lose money in doing so.
a) It doesn't know when it's hallucinating.
b) It can't provide you with any accurate confidence score for any answer.
c) Your library is still useful but any claim that you can make solutions more robust is a lie. Probably good enough to get into YC / raise VC though.
this is a streamlined implementation of a interanlly scrapped together tool that i decided to open-source for people to either us or build off of.
I’m interested. Where can I read more about this?
You've just described AGI.
If this were possible you could create an MCP server that has a continually updated list of FAQ of everything that the model doesn't know.
Over time it would learn everything.
"If you don't know the answer to a question and need the answer to continue, ask me before continuing"
Will you have some other person answer the question?
I like the idea but would rather it use Slack or something if it's meant to ask anyone.
What you are asking for is AGI. We still need human in the loop for now.
Tools like th one in the article are also "rules".
> (problem description) your agent […] makes confident assumptions
> (solution description) when it’s unsure
I read this as a contradiction: in one sentence you describe the problem as an agent being confident while hallucinating and in the next phrase the solution is that the agent can ask you if it’s unsure.
You tool is interesting but you may consider rephrasing that part.
So not at all, but that doesn't mean it's not useful.
Yes, it may not need to know with perfect certainty when it's unsure or stuck, but even to meet a lower bar of usefulness, it'll need at least an approximate means of determining that its knowledge is inadequate. To purport to help with the hallucination problem requires no less.
To make the issue a bit more clear, here are some candidate components to a stuck() predicate:
- possibilities considered
- time taken
- tokens consumed/generated (vs expected? vs static limit? vs dynamic limit?)
If the unsure/stuck determination is defined via more qualitative prompting, what's the prompt? How well has it worked?
(But you could build one that does this, and ask the LLM to call it and give your MCP that data... when it feels like it)
So you'd be using this by telling the LLM to run it when it thinks it's stuck. Or needs human input.
1: I am not anything even approaching deeply knowledgeable about MCP, so please, someone correct me if I'm wrong! There do seem to be some bi-directional messaging abilities, e.g. notification, but to figure out thinking time / token use / etc you would need to have access to the infrastructure running the LLM, e.g. Cursor itself or something.
You can probably get some where by indeed running a task 1000 times and looking for outliers in the execution time or token count. But that is of minimal use and anything more advanced than that is akin to water divining.
This is little different than how I wrote this. Now it is deterministic, when I hit reply.
It is amazing how bad LLMs are when it comes to reasoning about simple dynamics within trivial electronic circuits and how eager they are to insist the opposite of how things work in the real world is the secured truth.