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← The special reasoning-trace delimiter tokens used by reasoning models…
17 connected korrents · 16 moments on record from 5 Nov 2019 to 15 Sept 2026. Nearly all of them are about LLMs .
Everything filed under AI and jobs
AI and jobs
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measuring intelligence
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scaling laws
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robotics 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: The special reasoning-trace delimiter tokens used by reasoning models are cosmetic and not required for good reasoning performance. The special reasoning-trace delimiter tokens used by reasoning models are cosmetic and not required for good reasoning performance. Last stated 2 months ago
18 Jul 2026
SR
Sebastian Raschka — holds since 2026-07-18 — tap for who they are
Same subject: Tokens are not the true unit of AI inference, because better models yield more problem-solving per token. — tap to centre the map on it Tokens are not the true unit of AI inference, because better models yield more problem-solving per token. Last stated 6 days ago
15 Sept 2026
PG
Paul Graham — holds since 2026-09-15 — tap for who they are
Same subject: As labs sell finished white-collar work rather than reasoning traces, Chinese models will lose the ability to distil their way to parity. — tap to centre the map on it As labs sell finished white-collar work rather than reasoning traces, Chinese models will lose the ability to distil their way to parity. Last stated 6 months ago
13 Mar 2026
DP
Dylan Patel — holds since 2026-03-13 — tap for who they are
Same subject: Large language models do not reason formally: their performance collapses as a problem is made bigger, in the way a calculator's never does. — tap to centre the map on it Large language models do not reason formally: their performance collapses as a problem is made bigger, in the way a calculator's never does. Last stated 2 years ago
11 Oct 2024
GM
Gary Marcus — holds since 2024-10-11 — tap for who they are
Same subject: Without good intuition for models, treat 100k tokens on smaller models and 200k on the frontier ones as the line past which the quality of your results is probably already degrading. — tap to centre the map on it Without good intuition for models, treat 100k tokens on smaller models and 200k on the frontier ones as the line past which the quality of your results is probably already degrading. Last stated 2 months ago
15 Jul 2026
DH
Dex Horthy — holds since 2026-07-15 — tap for who they are
Same subject: The aha moment for reasoning models will come from computer use and robotics rather than from scientific discovery, because those are the infinitely verifiable playgrounds. — tap to centre the map on it The aha moment for reasoning models will come from computer use and robotics rather than from scientific discovery, because those are the infinitely verifiable playgrounds. Last stated 2 years ago
3 Feb 2025
DP
Dylan Patel — holds since 2025-02-03 — tap for who they are
Same subject: Natural-language LLM mathematical reasoners are fundamentally limited by lacking machine-checkable semantics, which blocks autonomous verification needed for open-ended research. — tap to centre the map on it Natural-language LLM mathematical reasoners are fundamentally limited by lacking machine-checkable semantics, which blocks autonomous verification needed for open-ended research. Last stated 2 months ago
8 Jul 2026
TT
Terence Tao — holds since 2026-07-08 — tap for who they are
Same subject: Chain-of-thought explanations from language models can be biased because training does not explicitly reward faithful reasoning. — tap to centre the map on it Chain-of-thought explanations from language models can be biased because training does not explicitly reward faithful reasoning. Last stated a year ago
1 May 2025
LW
Lilian Weng — holds since 2025-05-01 — tap for who they are
Same subject: Frontier language models will remain reasoning models trained with RLVR-style chain-of-thought for the foreseeable future. — tap to centre the map on it Frontier language models will remain reasoning models trained with RLVR-style chain-of-thought for the foreseeable future. Last stated 2 weeks ago
9 Sept 2026
SR
Sebastian Raschka — holds since 2026-09-09 — tap for who they are
Same subject: A company must cut half its engineers to pay for the other half's tokens, and that is what the layoffs are actually about. — tap to centre the map on it A company must cut half its engineers to pay for the other half's tokens, and that is what the layoffs are actually about. Last stated 6 months ago
11 Mar 2026
SY
Steve Yegge — holds since 2026-03-11 — tap for who they are
Same subject: A productivity improvement means fewer people doing the same job, and that should be said plainly by the people defending it as good. — tap to centre the map on it A productivity improvement means fewer people doing the same job, and that should be said plainly by the people defending it as good. Last stated 4 weeks ago
26 Aug 2026
DH
David Heinemeier Hansson — holds since 2026-08-26 — tap for who they are
Same subject: A universal basic income is a dangerous power-sharing arrangement, because it puts everyone's basic needs at the mercy of whoever holds office. — tap to centre the map on it A universal basic income is a dangerous power-sharing arrangement, because it puts everyone's basic needs at the mercy of whoever holds office. Last stated 4 months ago
4 Jun 2026
AI
Alex Imas — holds since 2026-06-04 — tap for who they are
Same subject: A true artificial general intelligence cannot exist without being recognized as a moral subject. — tap to centre the map on it A true artificial general intelligence cannot exist without being recognized as a moral subject. Last stated a year ago
10 Jun 2025
SH
Samuel Hammond — holds since 2025-06-10 — tap for who they are
Same subject: A unit of AI inference needs to be defined, for example via a chain of increasingly hard problems where each consecutive pair is solvable by one model. — tap to centre the map on it A unit of AI inference needs to be defined, for example via a chain of increasingly hard problems where each consecutive pair is solvable by one model. Last stated 6 days ago
15 Sept 2026
PG
Paul Graham — holds since 2026-09-15 — tap for who they are
Same subject: Both the special-purpose-programs view and the blank-slate view of human intelligence are likely incorrect. — tap to centre the map on it Both the special-purpose-programs view and the blank-slate view of human intelligence are likely incorrect. Last stated 7 years ago
5 Nov 2019
FC
François Chollet — holds since 2019-11-05 — 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 10 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 3 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 6 months ago
13 Mar 2026
DP
Dylan Patel — holds since 2026-03-13 — tap for who they are
At the centre
The special reasoning-trace delimiter tokens used by reasoning models are cosmetic and not required for good reasoning performance.
Holds Read this korrent →
Tokens are not the true unit of AI inference, because better models yield more problem-solving per token.
Holds
As labs sell finished white-collar work rather than reasoning traces, Chinese models will lose the ability to distil their way to parity.
Holds
Large language models do not reason formally: their performance collapses as a problem is made bigger, in the way a calculator's never does.
Holds
Without good intuition for models, treat 100k tokens on smaller models and 200k on the frontier ones as the line past which the quality of your results is probably already degrading.
Holds
The aha moment for reasoning models will come from computer use and robotics rather than from scientific discovery, because those are the infinitely verifiable playgrounds.
Holds
Natural-language LLM mathematical reasoners are fundamentally limited by lacking machine-checkable semantics, which blocks autonomous verification needed for open-ended research.
Holds
Chain-of-thought explanations from language models can be biased because training does not explicitly reward faithful reasoning.
Holds
Frontier language models will remain reasoning models trained with RLVR-style chain-of-thought for the foreseeable future.
Holds
A company must cut half its engineers to pay for the other half's tokens, and that is what the layoffs are actually about.
Holds
A productivity improvement means fewer people doing the same job, and that should be said plainly by the people defending it as good.
Holds
A universal basic income is a dangerous power-sharing arrangement, because it puts everyone's basic needs at the mercy of whoever holds office.
Holds
A true artificial general intelligence cannot exist without being recognized as a moral subject.
Holds
A unit of AI inference needs to be defined, for example via a chain of increasingly hard problems where each consecutive pair is solvable by one model.
Holds
Both the special-purpose-programs view and the blank-slate view of human intelligence are likely incorrect.
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