Arguing with Agents
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Arguing with Agents
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Re: Arguing with Agents
#2This is very well written and told. It’s worth reading all the way through.
> If you try to refute it, you’ll just get another confabulation.
> Not because the model is lying to you on purpose, and not because it’s “resistant” or “defensive” in the way a human might be. It’s because the explanation isn’t connected to anything that could be refuted. There is no underlying mental state that generated “I sensed pressure.” There is a token stream that was produced under a reward function that prefers human-sounding, emotionally framed explanations. If you push back, the token stream that gets produced next will be another human-sounding, emotionally framed explanation, shaped by whatever cues your pushback provided.
“It’s because the explanation isn’t connected to anything that could be refuted.” This is one of the key understandings that comes from working with these systems. They are remarkably powerful but there’s no there there. Knowing this I’ve found enables more effective usage because, as the article is describing, you move from a mode of arguing with “a person” to shaping an output.
Re: Arguing with Agents
#3Great article, best insight into autisticneurotypical communication styles.
Couldn’t you have a “communications” LLM massage your prompts to the “main” LLM so that it removes the queues that cause the main LLM to mistakenly infer your state of mind?
Re: Arguing with Agents
#4I got about halfway through this article until I started wondering why it was so long and going in loops. Then I ctrl+f'd.
` just `, (spaces on either side matter), 11 instances, most seem to be `isnt just`, `wasnt just`, `doesnt just` type pattern
`-`, an en dash instead of an emdash but 59 instances.
This article is either from a clanker and I am pissed off at wasting my time reading it, or from someone who writes like a clanker, and I am pissed off at wasting my time reading it.
Re: Arguing with Agents
#5This is very well written and told. It’s worth reading all the way through. > If you try to refute it, you’ll just get another confabulation. > Not because the model is lying to you on purpose, and not because it’s “resistant” or “defensive” in the way a human might be. It’s because the explanation isn’t connected to anything that could be refuted. There is no underlying mental state that generated “I sensed pressure…
Reminds me of https://news.ycombinator.com/item?id=15886728
Do not argue with the LLM, for it is subtle and quick to anger, and finds you crunchy with ketchup.
These are, broadly, all context management issues - when you see it start to go off track, it's because it has too much, too little, or the wrong context, and you have to fix that, usually by resetting it and priming it correctly the next time. This is why it's advantageous not to "chat" with the robots - treat them as an english-to-code compiler, not a coworker.
Chat to produce a spec, save the spec, clear the context, feed only the spec in as context, if there are issues, adjust the spec, rinse and repeat. Steering the process mid-flight is a) not repeatable and b) exacerbates the issue with lots of back and forth and "you're absolutely correct" that dilutes the instructions you wanted to give.
Re: Arguing with Agents
#6Remember when programming was fun?
To me, after the novelty of seeing a computer program execute (more or less) what I ask in plain English wears off, what’s left is the chore of managing a bunch of annoying bots.
I don’t know yet if we’re more productive or not, if the resulting code is as good. But the craft in itself is completely different, much more akin to product managing, psychology, which I never enjoyed as much.
Re: Arguing with Agents
#7I love how much time, money, and energy we are wasting on trying to trick these machines. Each day someone has a new bag of tricks.
Re: Arguing with Agents
#8>A recurring experience: I say something explicit, the other person hears something implicit.
I've experienced this my entire life and have all but given up trying to have actual conversations with people.
Re: Arguing with Agents
#9> I queued the work and let it run. First task came back good. Second came back good. Somewhere around hour four the quality started sliding. By hour six the agent was cutting corners I’d specifically told it not to cut, skipping steps I’d explicitly listed, behaving like I’d never written any of the rules down.
> …
> When I write a prompt, the agent doesn’t just read the words. It reads the shape. A short casual question gets read as casual. A long precise document with numbered rules gets read as… not just the rules, but also as a signal. “The user felt the need to write this much.” “Why?” “What’s going on here?” “What do they really want?”
This is an interesting premise but based on the information supplied, I don’t think it’s the only conclusion. Yet the whole essay seems to assume it is true and then builds its arguments on top of it.
I’ve run into this dilemma before. It happens when there’s a TON of information in the context. LLMs start to lose their attention to all the details when there’s a lot of it (e.g. context rot[0]). LLMs also keep making the same mistakes once the information is in the prompt, regardless of attempts to convey it is undesired[1]
I think these issues are just as viable to explain what the author was facing. Unless this is happening with much less information
Re: Arguing with Agents
#10I recently came across this presentation https://youtu.be/QxkRf-xSfgI, and it changed my view of AI quite significantly. (There is also a paper https://arxiv.org/html/2510.12066v2 .)
The fundamental idea is that "intelligence" really means trying to shorten the time to figure out something. So it's a tradeoff, not a quality. And AI agents are doing it.
Therefore, if that perspective is right, the issues that the OP describes are inherent to intelligent agents. They will try to find shortcuts, because that's what they do, it's what makes them intelligent in the first place.
People with ASD or ADHD or OCD, they are idiot-savants in the sense of that paper. They insist on search for solutions which are not easy to find, despite the common sense (aka intelligence) telling them otherwise.
It's a paradox that it is valuable to do this, but it is not smart. And it's probably why CEOs beat geniuses in the real world.