What I feel a bit annoyed by, and what I feel obviously LLM-run ablations like this fail to capture, is any kind of reflection around previous research or any kind of proof that this is the best you can do. You don't know, you pulled the lever and you got something, is the best? Can you do better? What is the constraint?
As an individual researcher, you do not have 22M$ to run a massive brute-force search for your problem. You are constrained to your little subscription and you will barely dip your toe in the sea of possible solutions to a problem. So letting Claude run an autoresearch loop on your problem and then having it summarize it for you brings 0 value because you dont know what the downsides and trade-offs of LRU caches were, and how you would possible solve it.
> It didn't work, and why it didn't work turned out to be more interesting than the policy would have been.
Spoken like a true Claude.
Snarking aside, I am glad that our AI agents make it cheap enough to do these experiments and publish these write-ups that people finally bother to publish null findings. Very useful!
I always have a tinge of...perhaps 'sadness' when reading such articles because they are a lot like those large fishing nets that drag along the bottom of the ocean catching and destroying in quantities unneeded. From this list:
- Quietly/Delve/Tapestry/Landscape: they're all lovely words. I love(d) using them.
- "Serves as": I use this all the time too.
- "The result? Devastating". Perhaps it is a bit stilted, but "Outcome? Predictable." could be a nice turn of phrase.
- "It's worth noting": All academics everywhere use this in papers. See also: "Notably", "Essentially", "In general" and other such fillers that we reach for.
- "Think of it as": This is such a useful phrase to explain something. Analogy is how we learn after all.
- The fucking em dashes. I love them, and screw everyone who suggests one should stop using them. One big reason for using linux is the compose key, so the em dash is never far away.
- Same with the unicode decoration. Nah, screw you. I use these symbols all the time: °, ×, →, ⇒ etc. in my own notes. Again, compose key ftw.
Etc. (the list is endless)
I know what people will say, that everything is contextual, one has to study case-by-case etc. But I can't help feeling that someone is pointing a finger at me and accusing me before-the-fact. Just, AI has polluted the joy of writing.
I've seen Douglas Adams use almost exactly this in Dirk Gently's Holistic Detective Agency. A professor (IIRC) is talking to a former student. Quoting from memory
"Did you ever actually finish any papers?"
"No. But the reasons why not were always absolutely fascinating."
So, yeah, for me personally it's not an AI tell. I've seen it in the wild before AI.
LLMs write like some people cook, adding bespoke artisanal Belgian sea salt, truffle oil and weirdly specific cheese without any thought given to how the result will taste.
While AI was clearly used to generate and structure a first draft, I say it has been extensively rewritten by the author. In my experience, AI tends to avoid the first person, instead writing from a passive observer or even using sentences without verbs. The length of the sentences is also more natural. AI had trouble with bind-words in an argument. It just states separate facts and assumes because they are stated in order you have a reasoning. I'm not seeing that here.
Of course, idioms are preserved, and not everything is rewritten.
I quite ok with this style, it isn't grating. I expect this will be a style we'll see more of in science.
I suspect it’s more like Claude etc. optimizing for a tone that gets the green checkmark from RLHF reviewers, probably over and over in many contexts where style is otherwise an incidental concern.
If you're being given an A-B choice between cinnamon-flavored shit and lutfisk-flavored shit, you might convince the experimenter that cinnamon-flavored shit ranks highly in user preferences.
I'm guessing he means overall the kind of circling and concluding that Claude does, like "the actual truth is more interesting than it seems" or "the real smoking gun is not the x, it's the y that was under our noses all this time" type of writing.
I've been building latency-sensitive LLM systems for a while, and I've come to rely heavily on pre-fill-considerate mechanics like ping-pong overlapped async context construction. For interactive mechanics, the worst case, even if rare, is problematic.
Consideration of mutation rate (a sort of temporal Shannon-ish coding/ordering) lives in there (with some RoPE-friendly structuring). Note: That was a vacation project, not the day job, but similar principles apply even with larger models.
I work on inference at a neocloud, but opinions are my own .
The economics and thus tools you can throw at inference change at various scales .
As a “blunt” contrived example , on a gb300 the GPUs communicate super fast over nvlink, and the cards can offload kv cache to dram and then disk, “fast enough “ for these tool heavy agentic workloads.
Which come together to mean that at high enough scale and in the right scenario, we can work
with wild ttls on the kv cache and still comfortably hit SLAs and tokenomics.
LRU seems like the ideal strategy for most things LLM-related. Everything in this realm is about recency bias. I think it is a feature in this context, not a problem.
When I give an agent a piece of corrected information regarding a long running task, the last thing I want it to do is try and statistically compensate for the fact that it is new information. I want this new information to dominate the old information.
This is almost unreadable. The "papers" are never referenced anywhere, so the claims being refuted cannot be evaluated. The whole fact of the TTL doesn't seem relevant at all. There are many, many well-researched admission and eviction policies that this readme doesn't mention. I just don't get why we are reading this.
Interesting! I admit the AI-written text is rough to read, it could have used a pass or two from an actual human. E.g. "Publishing it unresolved rather than tuning until it matches." -- thanks for not lying, I guess?
Fun:
> In my first run, Belady — an offline oracle — lost to LRU. That's not a result, that's a broken harness, and it's worth publishing because I expect it to be common.
> The cause: inserting a long chain into a near-full cache lets a policy evict the very prefix it is currently building. LRU is accidentally immune because just-inserted blocks have the newest timestamp.
There's a growing literature arguing LRU is the wrong eviction policy for agentic LLM serving, because agent sessions idle and LRU can't distinguish a paused session from a dead one. I found the argument convincing and built a simulator to exploit it. Three separate mechanisms, all lost to plain radix-leaf LRU.
The reason turned out to be more useful than the policy. When I measured — policy-independently — where recompute actually comes from on 393 real Claude Code sessions, requests arriving after a gap longer than the 5-minute provider TTL account for 17.5% of it. Requests arriving within 10 seconds account for 33.1%. The dominant waste is tight tool loops whose 88k-token working sets exceed cache capacity, not sessions idling past a TTL. That's a capacity problem, and liveness prediction can't touch it.
it's just a bit disheartening to read Claude output for such a small comment like this.. it'd be great to read your own writings even if it's not as "perfect"
One side effect of extremely-accessible, high-quality English translation and, more importantly, English-grammar-and-idiosyncrasy-obeying AI, is that there will be more and more text that looks like this which comes from foreign, non-native-English countries.
Just FYI.
This will be especially true from non-English cultures where "avoiding shame" is high on the list of motivations.
Consider the upside, though: A much broader range of written perspectives written in high-quality, if slightly annoying, English.
I'm already seeing the benefit of this on X thanks to its autotranslation btw: I follow a few Chinese-language accounts now that I would have never been able to digest otherwise.
As an individual researcher, you do not have 22M$ to run a massive brute-force search for your problem. You are constrained to your little subscription and you will barely dip your toe in the sea of possible solutions to a problem. So letting Claude run an autoresearch loop on your problem and then having it summarize it for you brings 0 value because you dont know what the downsides and trade-offs of LRU caches were, and how you would possible solve it.
Spoken like a true Claude.
Snarking aside, I am glad that our AI agents make it cheap enough to do these experiments and publish these write-ups that people finally bother to publish null findings. Very useful!
[1] https://gist.github.com/ossa-ma/f3baa9d25154c33095e22272c631...
- Quietly/Delve/Tapestry/Landscape: they're all lovely words. I love(d) using them.
- "Serves as": I use this all the time too.
- "The result? Devastating". Perhaps it is a bit stilted, but "Outcome? Predictable." could be a nice turn of phrase.
- "It's worth noting": All academics everywhere use this in papers. See also: "Notably", "Essentially", "In general" and other such fillers that we reach for.
- "Think of it as": This is such a useful phrase to explain something. Analogy is how we learn after all.
- The fucking em dashes. I love them, and screw everyone who suggests one should stop using them. One big reason for using linux is the compose key, so the em dash is never far away.
- Same with the unicode decoration. Nah, screw you. I use these symbols all the time: °, ×, →, ⇒ etc. in my own notes. Again, compose key ftw.
Etc. (the list is endless)
I know what people will say, that everything is contextual, one has to study case-by-case etc. But I can't help feeling that someone is pointing a finger at me and accusing me before-the-fact. Just, AI has polluted the joy of writing.
"Did you ever actually finish any papers?"
"No. But the reasons why not were always absolutely fascinating."
So, yeah, for me personally it's not an AI tell. I've seen it in the wild before AI.
LLMs write like some people cook, adding bespoke artisanal Belgian sea salt, truffle oil and weirdly specific cheese without any thought given to how the result will taste.
Of course, idioms are preserved, and not everything is rewritten.
I quite ok with this style, it isn't grating. I expect this will be a style we'll see more of in science.
*not at all
That's Claude. But I agree and am also glad the author, uh, clauded this up. :)
A toy/simplified version lives here: https://github.com/chaboud/goulash
Consideration of mutation rate (a sort of temporal Shannon-ish coding/ordering) lives in there (with some RoPE-friendly structuring). Note: That was a vacation project, not the day job, but similar principles apply even with larger models.
The economics and thus tools you can throw at inference change at various scales . As a “blunt” contrived example , on a gb300 the GPUs communicate super fast over nvlink, and the cards can offload kv cache to dram and then disk, “fast enough “ for these tool heavy agentic workloads.
Which come together to mean that at high enough scale and in the right scenario, we can work with wild ttls on the kv cache and still comfortably hit SLAs and tokenomics.
When I give an agent a piece of corrected information regarding a long running task, the last thing I want it to do is try and statistically compensate for the fact that it is new information. I want this new information to dominate the old information.
Fun:
> In my first run, Belady — an offline oracle — lost to LRU. That's not a result, that's a broken harness, and it's worth publishing because I expect it to be common.
> The cause: inserting a long chain into a near-full cache lets a policy evict the very prefix it is currently building. LRU is accidentally immune because just-inserted blocks have the newest timestamp.
There's a growing literature arguing LRU is the wrong eviction policy for agentic LLM serving, because agent sessions idle and LRU can't distinguish a paused session from a dead one. I found the argument convincing and built a simulator to exploit it. Three separate mechanisms, all lost to plain radix-leaf LRU.
The reason turned out to be more useful than the policy. When I measured — policy-independently — where recompute actually comes from on 393 real Claude Code sessions, requests arriving after a gap longer than the 5-minute provider TTL account for 17.5% of it. Requests arriving within 10 seconds account for 33.1%. The dominant waste is tight tool loops whose 88k-token working sets exceed cache capacity, not sessions idling past a TTL. That's a capacity problem, and liveness prediction can't touch it.
it's just a bit disheartening to read Claude output for such a small comment like this.. it'd be great to read your own writings even if it's not as "perfect"
Just FYI.
This will be especially true from non-English cultures where "avoiding shame" is high on the list of motivations.
Consider the upside, though: A much broader range of written perspectives written in high-quality, if slightly annoying, English.
I'm already seeing the benefit of this on X thanks to its autotranslation btw: I follow a few Chinese-language accounts now that I would have never been able to digest otherwise.
The best kind of correct.
I'd have thought they'd be busy (using cached prompt-prefixes) until they were finished.