alt Hacker NewsThis is from the first of the caveats that they list:
> Scoped context: Our tests gave models the vulnerable function directly, often with contextual hints (e.g., "consider wraparound behavior"). A real autonomous discovery pipeline starts from a full codebase with no hints. The models' performance here is an upper bound on what they'd achieve in a fully autonomous scan. That said, a well-designed scaffold naturally produces this kind of scoped context through its targeting and iterative prompting stages, which is exactly what both AISLE's and Anthropic's systems do.
That's why their point is what the subheadline says, that the moat is the system, not the model.
Everybody so far here seems to be misunderstanding the point they are making.
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I get what you're saying, but I think this is still missing something pretty critical.
The smaller models can recognize the bug when they're looking right at it, that seems to be verified. And with AISLE's approach you can iteratively feed the models one segment at a time cheaply. But if a bug spans multiple segments, the small model doesn't have the breadth of context to understand those segments in composite.
The advantage of the larger model is that it can retain more context and potentially find bugs that require more code context than one segment at a time.
That said, the bugs showcased in the mythos paper all seemed to be shallow bugs that start and end in a single input segment, which is why AISLE was able to find them. But having more context in the window theoretically puts less shallow bugs within range for the model.
I think the point they are making, that the model doesn't matter as much as the harness, stands for shallow bugs but not for vulnerability discovery in general.
huh, running it over each function in theory but testing just the specific ones here makes sense, but that hint?!
> That's why their point is what the subheadline says, that the moat is the system, not the model.
I'm skeptical; they provided a tiny piece of code and a hint to the possible problem, and their system found the bug using a small model.
That is hardly useful, is it? In order to get the same result , they had to know both where the bug is and what the bug is.
All these companies in the business of "reselling tokens, but with a markup" aren't going to last long. The only strategy is "get bought out and cash out before the bubble pops".
> That's why their point is what the subheadline says, that the moat is the system, not the model.
Can you expand a bit more on this? What is the system then in this case? And how was that model created? By AI? By humans?
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If that's the point they are making, let's see their false positive rate that it produces on the entire codebase.
They measured false negatives on a handful of cases, but that is not enough to hint at the system you suggest. And based on my experiences with $$$ focused eval products that you can buy right now, e.g. greptile, the false positive rate will be so high that it won't be useful to do full codebase scans this way.