You may be relived to know that Unix epoch time does not have this problem. But you may also be horrified to know why.
First, epoch seconds are not the numbers of seconds since 1970/01/01. This is a lie we tell to children. Rather, epoch seconds are the number of days since 1970/01/01 * 86400 plus the number of seconds since midnight.
Leap seconds, to epoch time, don't exist. Or maybe they are double counted. Or maybe we smear them over 12 or 24 hours (but which 12 or 24 hours depends on whether you are Google, Microsoft, or Oracle; I can't even make this stuff up). The point is, it's not defined, and this means implementations do it differently.
A negative leap second might be easier though. The spec suggests (though lack of speaking) that a correct implementation will just skip it since number of seconds stays less than 86400 for that day. But of course the smear-organizations still smear it.
So what if you really want to know how many seconds were between two different epochs? Subtracting epoch seconds is wrong because you need to correct for the number of leap seconds between the two times.
My understanding the problem is that GLONASS is aware of leap seconds at all. It sends messages in UTC, which has this leap second funny business. GPS uses a special "GPS time" (sometimes abbreviated UT) that doesn't have a leap second. For further confusion, the leap second ensures that UTC is never more than 0.9 seconds off of mean solar time, aka UT1.
This type of assumption that was made early in a massive software and hardware project that's now been ossified for ~50 years is going to be hard to change.
In short, yes, the weather, geology, and signicantly, human movement of water via aquifer draining and dam building, as well as glaicial and ice melts, all contribute to unpredictable changes in the earths rotational period, as well as the axis of rotation. The models for this are IIRC trigonometric polynomials of fairly low order, so even if we could model the unpredictability perfectly, truncation error would limit our ability to distribute the model at super high accuracy. The existing models are built in to, eg, satellites, so you can't just make them arbitrarily complex.
Fun fact: leap seconds will stop being a thing soonish. I think they phase out in 2035, with a delay because Russia needed time to update glonass satellites.
(Note: on mobile, this is from memory, details need checking ;))
A quick skim of the article suggests it's a study of the implications of allowing query planning in programs which are notionally finite but may have unbounded computation. Interesting.
Right. Maybe a better and more humble title is "Identifying General Instances of Market Collusion is NP-complete". Not as headline grabbing, but more in line with the actual result.
I'd like to emphasize that coarsening is not just theoretically non-private, a number of attacks that lead to leaking personally identifiable data were demonstrated on the 2010 census. So it's not really a he-said/she-said situation.
I don't know the technical details of this ZKP library, but there is no technical reason that I'm aware of that the ID provider would need to know who you are sharing with. Not to say Google didn't build it this way for business reasons.
This. In my experience, you have to replace peer review with reputation for preprints. That's highly imperfect, and it tends to lead to dismissing of good but work by less well-known researchers as "not peer reviewed", while well-known researchers (or researchers at well-known institutions) basically get a fast track to citations.
Despite the imperfections, I found arXiv indispensable for my research. In particular, mathematics has a slow peer review cycle (it's hard to read and understand, and many referees require that they fully understand a paper to accept it, which imo is a little flawed, but that's the culture). I had several papers that were under review for more than a year (single journal, only one round of revisions), and arXiv was my only showcase. Both works ended up very highly cited, but publication delays would have been an even bigger problem if arXiv wasn't there.
Can you share more details? I ask because my experience suggests that models still require a decent amount of expertise to use for binary analysis (largely inferring because of use on other tasks of this level). I would expect models to always find "something" when you ask for stenographic techniques in the code, but with an extremely high false positive rate.
I'd love for you to try this and report back. My guess is that no models today will successfully run a binary analysis for fingerprinting without a lot of handholding. If you try to use Opus it will almost certainly decline (and fingerprint/ban you).
Yes, defeating this is relatively easy, particularly for sophisticated actors. But it's hard to always defeat all of the tricks. Sort of like how it's expensive and hard and uncertain to defeat all of the tricks when forging money.
Here's an example. Say you have your team use patched binaries. Then CC updates and requires a new patched binary with new tricks. You now have to have a team ready to analyze the binary and begin to address the tricks; meanwhile, unpatched code is now a fingerprint. If some researcher decides to update Claude on their own to access new features, they get fingerprinted.
Defeating a single fingerprinting technique once is easy. Defeating all of the techniques all the time is hard.
This is cool, but note that it doesn't address one of the main (claimed) advantages of Mythos: lower false positive rates. That is, give it files without serious bugs and it will not raise alarms.
If data exfiltration is a danger in your threat model, you need local LLMs (or at least ones you fully control) not just the full chain-of-thought reasoning.
This seems like the pi vs tau argument on steroids. A lot of people who know a bit of math think that tau simplifies things enormously. Professionals are like "not really"; dropping a 2 in places simplifies a few formulas, makes others slightly more complex, and provides zero insight.
The hard problems in math are almost always still hard no matter the notation you choose to use. Sometimes notation makes transmitting ideas a bit easier, but usually faffing around with notation is a sign you aren't able to solve the real problems.
Thanks for the context; how do you think this impacts plausibility? Presumably the fact that he made progress in a well studied passage is cause for skepticism? What's your take?
I believe that the line was constructing exploits for bugs, not bug finding. This seems a reasonable cutoff to me, since bugs are revealed in security patches and pull requests (for open source).
If you are to believe Anthropic, Fable was export controlled for bug finding, not for exploit construction. They seem to be working to make this the "bright line" for LLMs being a national security risk. My guess is that will be the case they take to Washington this week.