Hi Harry,

On 5/23/26 16:34, Harry Yoo wrote:

On 5/23/26 11:00 PM, Yunseong Kim wrote:

quoted

I've previously experimented with running DEPT alongside syzkaller fuzzing,
and many hung tasks missed by lockdep are caught by DEPT, but the resulting
high volume of reports makes it easy for issues to get lost in the massive
log output. Sorting through that output manually is a huge bottleneck, so
leveraging a well-crafted AI prompt to triage the warnings and filter out
the false positives would be incredibly valuable.

I mean both 1) detection of deadlock issues AND 2) false positive elimination with AI.

I completely agree.  Implanting DEPT's model into an AI review prompt
is a great idea. As you suggested, the patterns we develop for the AI
could provide valuable feedback to enhance DEPT's itself.

If the review prompt is only used to eliminate DEPT's false positives, I think that would be quite hard to get broad use.

Someone would have to build out-of-tree DEPT, collect the reports, and then feed those back into the AI. I don't think building that kind of pipeline would actually work well in practice.

I also have a huge dept report of DEPT reports, and manually
reviewing all of them is makes me sigh. The constant kernel rebuilds
required for lockup testing every time are also quite expensive.

Thanks for the summary!

Best Regards,
Yunseong