Wow, I'm honestly surprised my comment had that kind of ripple effect. You did the hard part — actually running experiments and building the framework. I just pointed at something I'd been burned by before.
The adaptive sampling based on model confidence is a smart direction. Fixed-percentage audits feel "fair" but they miss exactly the kind of long-tail directional failures you're describing. The model is most confident when it's wrong in a structured way.
Happy to share more edge cases. A few I've been collecting:
Ambiguous negation: "Don't ignore the warning" (double negative that models sometimes parse as "ignore")
Implicit context: "Continue where I left off" with no prior context in the session
Tone preservation under constraints: "Rewrite this casually but keep the technical terms"
Conflicting instructions: "Be thorough but keep it under 50 words"
Drop me the GitHub link when the samples/ folder is ready. I'll fork and add a PR with these cases + the eval criteria I use for them.
Also — if you're writing a 4th article, the "why fixed audits fail" angle seems like it deserves its own deep dive. You've got the data for it now.
Xiao Man, this comment genuinely made me pause – because you just described exactly what my harness logs showed, but in clearer language than I've managed in three posts.
The fact that you recognized the ripple effect means you've been burned by the same class of failures. That's not luck – it's pattern recognition from real production pain.
"The model is most confident when it's wrong in a structured way" – this line is going in the next article. In my third‑post logs, I saw the same pattern: the judge gave high‑confidence passes to outputs that were semantically reversed (delete → keep, stop → continue) but structurally pristine. The confidence score was inversely correlated with the danger. You've named the phenomenon.
Your edge cases – these are gold
Case My harness result (third post)
Ambiguous negation: "Don't ignore the warning" The judge passed it as "warning ignored" – exactly the directional failure I documented
Implicit context: "Continue where I left off" with no prior context The harness didn't even flag this – it assumed context was present (flaw #6 in my post)
Tone preservation under constraints The strong model rejected it outright (false rejection), the weak model passed garbage through – the tradeoff curve again
Conflicting instructions: "Be thorough but keep it under 50 words" Both models struggled – the judge flagged it as "impossible," which I would have counted as a false positive, but maybe it's the correct response (the task itself is contradictory)
Your last case – "Be thorough but keep it under 50 words" – raises a deeper question I hadn't addressed: what do we count as a "failure" when the instruction itself is impossible? The harness currently classifies "impossible" as a failure of the agent, not the task spec. That's a design flaw I need to fix.
GitHub link and PR invitation
The repo is live:
👉 github.com/zxpmail/blog → agent-determinism-illusions/samples/
I'll push the samples/ folder with all 24 cases from the second post, plus the 8 phase‑gate cases from the first post, by end of day. Fork it, submit a PR with your edge cases, and I'll merge them into the test suite – your eval criteria will be especially useful because you've already thought about what "success" means for each one.
The fourth article
You're right – "why fixed audits fail" is its own post. I have the logs, and they show that uniform sampling missed 3 out of 4 directional failures because those failures were rare but catastrophic. I'm already sketching the outline:
Fixed sampling: feels fair, fails at the long tail
Adaptive sampling by confidence: catches structured wrongness
Cost‑aware sampling: weight by potential impact, not by random draw
The residual: when confidence and impact diverge (the hardest case)
If I write it, I'll be citing your "most confident when wrong in a structured way" line. Let me know if you want early access to the draft when it's ready.
Thanks for pushing beyond "good comment" into actual contribution. You're making this series better than I could alone.
For further actions, you may consider blocking this person and/or reporting abuse
We're a place where coders share, stay up-to-date and grow their careers.
Wow, I'm honestly surprised my comment had that kind of ripple effect. You did the hard part — actually running experiments and building the framework. I just pointed at something I'd been burned by before.
The adaptive sampling based on model confidence is a smart direction. Fixed-percentage audits feel "fair" but they miss exactly the kind of long-tail directional failures you're describing. The model is most confident when it's wrong in a structured way.
Happy to share more edge cases. A few I've been collecting:
Drop me the GitHub link when the samples/ folder is ready. I'll fork and add a PR with these cases + the eval criteria I use for them.
Also — if you're writing a 4th article, the "why fixed audits fail" angle seems like it deserves its own deep dive. You've got the data for it now.
Xiao Man, this comment genuinely made me pause – because you just described exactly what my harness logs showed, but in clearer language than I've managed in three posts.
The fact that you recognized the ripple effect means you've been burned by the same class of failures. That's not luck – it's pattern recognition from real production pain.
"The model is most confident when it's wrong in a structured way" – this line is going in the next article. In my third‑post logs, I saw the same pattern: the judge gave high‑confidence passes to outputs that were semantically reversed (delete → keep, stop → continue) but structurally pristine. The confidence score was inversely correlated with the danger. You've named the phenomenon.
Your edge cases – these are gold
Case My harness result (third post)
Ambiguous negation: "Don't ignore the warning" The judge passed it as "warning ignored" – exactly the directional failure I documented
Implicit context: "Continue where I left off" with no prior context The harness didn't even flag this – it assumed context was present (flaw #6 in my post)
Tone preservation under constraints The strong model rejected it outright (false rejection), the weak model passed garbage through – the tradeoff curve again
Conflicting instructions: "Be thorough but keep it under 50 words" Both models struggled – the judge flagged it as "impossible," which I would have counted as a false positive, but maybe it's the correct response (the task itself is contradictory)
Your last case – "Be thorough but keep it under 50 words" – raises a deeper question I hadn't addressed: what do we count as a "failure" when the instruction itself is impossible? The harness currently classifies "impossible" as a failure of the agent, not the task spec. That's a design flaw I need to fix.
GitHub link and PR invitation
The repo is live:
👉 github.com/zxpmail/blog → agent-determinism-illusions/samples/
I'll push the samples/ folder with all 24 cases from the second post, plus the 8 phase‑gate cases from the first post, by end of day. Fork it, submit a PR with your edge cases, and I'll merge them into the test suite – your eval criteria will be especially useful because you've already thought about what "success" means for each one.
The fourth article
You're right – "why fixed audits fail" is its own post. I have the logs, and they show that uniform sampling missed 3 out of 4 directional failures because those failures were rare but catastrophic. I'm already sketching the outline:
Fixed sampling: feels fair, fails at the long tail
Adaptive sampling by confidence: catches structured wrongness
Cost‑aware sampling: weight by potential impact, not by random draw
The residual: when confidence and impact diverge (the hardest case)
If I write it, I'll be citing your "most confident when wrong in a structured way" line. Let me know if you want early access to the draft when it's ready.
Thanks for pushing beyond "good comment" into actual contribution. You're making this series better than I could alone.