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wrstoday at 5:34 PM4 repliesview on HN

I think the theoretical answer here is this:

"Agents address the problem from independent angles, other agents try to refute what they found, and the run keeps iterating until the answers converge."

So you will be supplying the "ground truth" (test suite, detailed spec, whatever) and empower an agent to use it to guide the other agents. Currently a lot of people do this sequentially in the form of multiple code-review passes by fresh agent sessions looking at the work of previous sessions.

Adversarial models are a longstanding technique in ML so it makes sense they would try to go this way.


Replies

vadanskytoday at 6:01 PM

I don't know, maybe I'm doing it wrong but I feel LLMs add a slop debt, and each agent pass just exuberates it.

Like I had an LLM implement a spec and said it was done... Except it had a ton of `casts` everywhere. Okay, my bad, I should have been clear "NO CASTS", so I use the LLM to remove the casts, except it just kept making things more and more complicated and ugly.

It took me taking a break and having a shower thought to realize all the ugliness is because one type should have been broken up into 2, which would remove a ton of generics and code. But Claude never suggested that, it was always "we need at least one cast here, or we need 1000 LOC of generic factories". I tried multiple new sessions with various prompts too.

Maybe one day soon LLMs could pay off their own slop debt but at least right now I don't trust them to write code unseen.

Edit: Maybe the correct action should have been to delete everything and make it re-write everything from scratch with the clear "NO CASTS EVER" rule. But still the point is feels like having LLM clean up after an LLM doesn't work well enough to just have keep it in a loop and never look at what it does.

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KronisLVtoday at 8:47 PM

> Currently a lot of people do this sequentially in the form of multiple code-review passes by fresh agent sessions looking at the work of previous sessions.

Up until now I've used a review loop approach, where within a Claude Code session I just tell it to spawn three review sub-agents, each with context of what's going on and instructions to look over all of the changed code in search for serious/critical issues, but otherwise a more fresh look at things. It works really well for the most part (token usage aside): https://news.ycombinator.com/item?id=48277011

Garleftoday at 9:13 PM

Doesn't help if the wrong design is implemented correctly.

tsunamifurytoday at 5:40 PM

Ground truth is not consensus, it has to be graded against what actually works for the original goal. Plenty of scenarios with AI and Humans can result in consensus around incorrectness.

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