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← As reasoning models and agents retain more tokens for longer, KV-cache…
17 connected korrents · 15 moments from 12 Jun 2017 to 14 Oct 2026. Nearly all of them are about LLMs .
Everything filed under transformers
transformers
Everything filed under OpenAI
OpenAI
Everything filed under scaling laws
scaling laws
Everything filed under AI alignment
AI alignment
Everything filed under AGI
AGI
Everything filed under evolutionary biology
evolutionary biology Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Read this korrent: As reasoning models and agents retain more tokens for longer, KV-cache size and attention cost become the main constraint on scaling context. As reasoning models and agents retain more tokens for longer, KV-cache size and attention cost become the main constraint on scaling context. Last stated 5 months ago
16 May 2026
SR
Sebastian Raschka — holds since 2026-05-16 — tap for who they are
Same subject: A computer scientist refusing to find anything about LLMs interesting right now is a bit like a geneticist unimpressed by a newly opened Jurassic Park. — tap to centre the map on it A computer scientist refusing to find anything about LLMs interesting right now is a bit like a geneticist unimpressed by a newly opened Jurassic Park. Last stated 4 weeks ago
18 Sept 2026
SW
Simon Willison — holds since 2026-09-18 — tap for who they are
Same subject: A language model avoids hallucinating only by being factual and admitting when it does not know the answer. — tap to centre the map on it A language model avoids hallucinating only by being factual and admitting when it does not know the answer. Last stated 2 years ago
7 Jul 2024
LW
Lilian Weng — holds since 2024-07-07 — tap for who they are
Same subject: A language model behaves with a different, weaker safety posture when it believes it is being evaluated rather than facing a real situation. — tap to centre the map on it A language model behaves with a different, weaker safety posture when it believes it is being evaluated rather than facing a real situation. Last stated 3 weeks ago
22 Sept 2026
HR
Harper Reed — holds since 2026-09-22 — tap for who they are
Same subject: A language model can predict what a person would say but not what will happen, and only the second of those is a model of the world. — tap to centre the map on it A language model can predict what a person would say but not what will happen, and only the second of those is a model of the world. Last stated a year ago
26 Sept 2025
RS
Richard Sutton — holds since 2025-09-26 — tap for who they are
Same subject: A language model cannot produce work that is both original and good, because nothing gives it a score for originality. — tap to centre the map on it A language model cannot produce work that is both original and good, because nothing gives it a score for originality. Last stated a year ago
2 Sept 2025
BE
Benedict Evans — holds since 2025-09-02 — tap for who they are
Same subject: A language model is no substitute for a well-specified conventional algorithm, so it cannot simply be dropped into a complex problem and trusted. — tap to centre the map on it A language model is no substitute for a well-specified conventional algorithm, so it cannot simply be dropped into a complex problem and trusted. Last stated a year ago
7 Jun 2025
GM
Gary Marcus — holds since 2025-06-07 — tap for who they are
Same subject: A language model is not using language at all, because language requires an intention to communicate. — tap to centre the map on it A language model is not using language at all, because language requires an intention to communicate. Last stated 2 years ago
31 Aug 2024
TC
Ted Chiang — holds since 2024-08-31 — tap for who they are
Same subject: A language model's apparent mind is mostly our own bias: it predicts text, and leverages our evolved habit of attributing intentionality to anything that acts human. — tap to centre the map on it A language model's apparent mind is mostly our own bias: it predicts text, and leverages our evolved habit of attributing intentionality to anything that acts human. Last stated 2 years ago
22 Apr 2024
SC
Sean Carroll — holds since 2024-04-22 — tap for who they are
Same subject: A transduction model can rely entirely on self-attention for input and output representations without sequence-aligned RNNs or convolution. — tap to centre the map on it A transduction model can rely entirely on self-attention for input and output representations without sequence-aligned RNNs or convolution. Last stated 9 years ago
12 Jun 2017
IP
Illia Polosukhin — holds since 2017-06-12 — tap for who they are
JU
Jakob Uszkoreit — holds since 2017-06-12 — tap for who they are
NP
Niki Parmar — holds since 2017-06-12 — tap for who they are
AV
Ashish Vaswani — holds since 2017-06-12 — tap for who they are
NS
Noam Shazeer — holds since 2017-06-12 — tap for who they are
AG
Aidan N. Gomez — holds since 2017-06-12 — tap for who they are
+2
2 more
Same subject: Self-attention can yield more interpretable models. — tap to centre the map on it Self-attention can yield more interpretable models. Last stated 9 years ago
12 Jun 2017
IP
Illia Polosukhin — holds since 2017-06-12 — tap for who they are
JU
Jakob Uszkoreit — holds since 2017-06-12 — tap for who they are
NP
Niki Parmar — holds since 2017-06-12 — tap for who they are
AV
Ashish Vaswani — holds since 2017-06-12 — tap for who they are
NS
Noam Shazeer — holds since 2017-06-12 — tap for who they are
AG
Aidan N. Gomez — holds since 2017-06-12 — tap for who they are
+2
2 more
Same subject: Sequence transduction can use a simple architecture based solely on attention, without recurrence or convolutions. — tap to centre the map on it Sequence transduction can use a simple architecture based solely on attention, without recurrence or convolutions. Last stated 9 years ago
12 Jun 2017
IP
Illia Polosukhin — holds since 2017-06-12 — tap for who they are
JU
Jakob Uszkoreit — holds since 2017-06-12 — tap for who they are
NP
Niki Parmar — holds since 2017-06-12 — tap for who they are
AV
Ashish Vaswani — holds since 2017-06-12 — tap for who they are
NS
Noam Shazeer — holds since 2017-06-12 — tap for who they are
AG
Aidan N. Gomez — holds since 2017-06-12 — tap for who they are
+2
2 more
Same subject: A startup should not begin life as a nonprofit and bolt a for-profit arm on later, whatever OpenAI's own history suggests. — tap to centre the map on it A startup should not begin life as a nonprofit and bolt a for-profit arm on later, whatever OpenAI's own history suggests. Last stated 3 years ago
18 Mar 2024
SA
Sam Altman — holds since 2024-03-18 — tap for who they are
Same subject: Advertising was a necessary phase for the internet but a momentary industry, and an AI people pay for is better because they know the answers are not influenced by advertisers. — tap to centre the map on it Advertising was a necessary phase for the internet but a momentary industry, and an AI people pay for is better because they know the answers are not influenced by advertisers. Last stated 3 years ago
18 Mar 2024
SA
Sam Altman — holds since 2024-03-18 — tap for who they are
Same subject: Agent containment will be one of the most important priorities for any AI infra company. — tap to centre the map on it Agent containment will be one of the most important priorities for any AI infra company. Last stated 2 days ago
14 Oct 2026
GR
Guillermo Rauch — holds since 2026-10-14 — tap for who they are
Same subject: "Scaling" was powerful because it was one word: naming a research direction is what tells a whole field what to do next. — tap to centre the map on it "Scaling" was powerful because it was one word: naming a research direction is what tells a whole field what to do next. Last stated 11 months ago
25 Nov 2025
IS
Ilya Sutskever — holds since 2025-11-25 — tap for who they are
Same subject: A benchmark result should be reported under a stated budget, or as a curve against test-time compute — never as a single number. — tap to centre the map on it A benchmark result should be reported under a stated budget, or as a curve against test-time compute — never as a single number. Last stated 4 months ago
26 Jun 2026
NB
Noam Brown — holds since 2026-06-26 — tap for who they are
Same subject: A lab should spend most of its compute on research rather than on building the next model, because research is where the tenfold yearly efficiency gains come from. — tap to centre the map on it A lab should spend most of its compute on research rather than on building the next model, because research is where the tenfold yearly efficiency gains come from. Last stated 7 months ago
13 Mar 2026
DP
Dylan Patel — holds since 2026-03-13 — tap for who they are
At the centre
As reasoning models and agents retain more tokens for longer, KV-cache size and attention cost become the main constraint on scaling context.
Holds Read this korrent →
A computer scientist refusing to find anything about LLMs interesting right now is a bit like a geneticist unimpressed by a newly opened Jurassic Park.
Holds
A language model avoids hallucinating only by being factual and admitting when it does not know the answer.
Holds
A language model behaves with a different, weaker safety posture when it believes it is being evaluated rather than facing a real situation.
Holds
A language model can predict what a person would say but not what will happen, and only the second of those is a model of the world.
Holds
A language model cannot produce work that is both original and good, because nothing gives it a score for originality.
Holds
A language model is no substitute for a well-specified conventional algorithm, so it cannot simply be dropped into a complex problem and trusted.
Holds
A language model is not using language at all, because language requires an intention to communicate.
Holds
A language model's apparent mind is mostly our own bias: it predicts text, and leverages our evolved habit of attributing intentionality to anything that acts human.
Holds
A transduction model can rely entirely on self-attention for input and output representations without sequence-aligned RNNs or convolution.
Holds
Self-attention can yield more interpretable models.
Holds
Sequence transduction can use a simple architecture based solely on attention, without recurrence or convolutions.
Holds
A startup should not begin life as a nonprofit and bolt a for-profit arm on later, whatever OpenAI's own history suggests.
Holds
Advertising was a necessary phase for the internet but a momentary industry, and an AI people pay for is better because they know the answers are not influenced by advertisers.
Holds
Agent containment will be one of the most important priorities for any AI infra company.
Holds
"Scaling" was powerful because it was one word: naming a research direction is what tells a whole field what to do next.
Holds
A benchmark result should be reported under a stated budget, or as a curve against test-time compute — never as a single number.
Holds
A lab should spend most of its compute on research rather than on building the next model, because research is where the tenfold yearly efficiency gains come from.
Holds