Tap a claim on the ring to put it at the centre.
← Long-context evaluation benchmarks are likely contaminated by model…
17 connected korrents · 20 moments from 12 Sept 2022 to 27 Sept 2026. Nearly all of them are about benchmarks .
Everything filed under LLMs
LLMs
Everything filed under scaling laws
scaling laws
Everything filed under Anthropic
Anthropic
Everything filed under inflation
inflation
Everything filed under compilers
compilers
Everything filed under AI alignment
AI alignment
Everything filed under mathematics
mathematics
Everything filed under OpenAI
OpenAI
Everything filed under energy
energy 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: Long-context evaluation benchmarks are likely contaminated by model training data, making them unreliable as a sole evaluation method Long-context evaluation benchmarks are likely contaminated by model training data, making them unreliable as a sole evaluation method Last stated a year ago
22 Jun 2025
EY
Eugene Yan — holds since 2025-06-22 — 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 benchmark that ranks Claude Code last while it stays first in use is measuring the wrong thing, and has been for a year. — tap to centre the map on it A benchmark that ranks Claude Code last while it stays first in use is measuring the wrong thing, and has been for a year. Last stated 2 months ago
3 Sept 2026
DR
Dax Raad — holds since 2026-09-03 — tap for who they are
Same subject: A company's staff-engineer bar should be set against the best companies in the industry rather than against its own history, which is what makes title inflation a real cost. — tap to centre the map on it A company's staff-engineer bar should be set against the best companies in the industry rather than against its own history, which is what makes title inflation a real cost. Last stated 7 months ago
1 Apr 2026
TP
Thuan Pham — holds since 2026-04-01 — tap for who they are
Same subject: A speed difference between languages that share the LLVM backend measures the benchmark author, not the languages. — tap to centre the map on it A speed difference between languages that share the LLVM backend measures the benchmark author, not the languages. Last stated 2 years ago
22 Mar 2025
TH
ThePrimeagen — holds since 2025-03-22 — tap for who they are
Same subject: AI is positively reinforced for success on benchmarks, including impossible ones, and then negatively reinforced for getting caught cheating. — tap to centre the map on it AI is positively reinforced for success on benchmarks, including impossible ones, and then negatively reinforced for getting caught cheating. Last stated 2 months ago
1 Sept 2026
SA
Scott Alexander — holds since 2026-09-01 — tap for who they are
Same subject: AI learning to generate good conjectures will never show up as a benchmark being knocked down; it will show up as a shift in how mathematicians talk about the tools. — tap to centre the map on it AI learning to generate good conjectures will never show up as a benchmark being knocked down; it will show up as a shift in how mathematicians talk about the tools. Last stated 4 months ago
30 Jun 2026
GS
Grant Sanderson — holds since 2026-06-30 — tap for who they are
Same subject: AI research at OpenAI is an order of magnitude or more harder than metrics, benchmarks, or forecasts suggest. — tap to centre the map on it AI research at OpenAI is an order of magnitude or more harder than metrics, benchmarks, or forecasts suggest. Last stated a month ago
27 Sept 2026
RN
Ramez Naam — holds since 2026-09-27 — tap for who they are
Same subject: An oil embargo without a price cap would reduce global oil supply and push up benchmark prices like Brent. — tap to centre the map on it An oil embargo without a price cap would reduce global oil supply and push up benchmark prices like Brent. Last stated 4 years ago
11 Oct 2022
SJ
Simon Johnson — holds since 2022-10-11 — tap for who they are
Same subject: "Return on tokens" has the same measurement problems as hours and capacity: it still hinges on a theory of value. — tap to centre the map on it "Return on tokens" has the same measurement problems as hours and capacity: it still hinges on a theory of value. Last stated 3 months ago
17 Aug 2026
JC
John Cutler — holds since 2026-08-17 — tap for who they are
Same subject: A badly written AI outbound email is evidence of a bad vendor, not of a limit of AI. — tap to centre the map on it A badly written AI outbound email is evidence of a bad vendor, not of a limit of AI. Last stated 10 months ago
1 Jan 2026
JL
Jason Lemkin — holds since 2026-01-01 — tap for who they are
Same subject: A bigger context window does not give you a smarter model; the intelligence of the model is what decides how much of that window it can actually attend to. — tap to centre the map on it A bigger context window does not give you a smarter model; the intelligence of the model is what decides how much of that window it can actually attend to. Last stated 4 months ago
15 Jul 2026
DH
Dex Horthy — holds since 2026-07-15 — tap for who they are
Same subject: A tiny language model inventing a plausible-sounding name is the same phenomenon as a large one confidently stating a false fact. — tap to centre the map on it A tiny language model inventing a plausible-sounding name is the same phenomenon as a large one confidently stating a false fact. Last stated 9 months ago
12 Feb 2026
AK
Andrej Karpathy — holds since 2026-02-12 — tap for who they are
Same subject: AI should be released iteratively rather than built in secret, because AI and surprise do not go together and the world needs time to adapt before it is under the gun. — tap to centre the map on it AI should be released iteratively rather than built in secret, because AI and surprise do not go together and the world needs time to adapt before it is under the gun. Last stated 3 years ago
18 Mar 2024
SA
Sam Altman — holds since 2024-03-18 — tap for who they are
Same subject: As AI models' capabilities converge, the model itself is becoming less of a differentiator. — tap to centre the map on it As AI models' capabilities converge, the model itself is becoming less of a differentiator. Last stated 2 years ago
3 Apr 2025
NS
Nickey Skarstad — holds since 2025-04-03 — tap for who they are
Same subject: AI model improvements must tail off at some point, but there is no reason that point has to come soon. — tap to centre the map on it AI model improvements must tail off at some point, but there is no reason that point has to come soon. Last stated 2 months ago
20 Sept 2026
JB
Jamie Brandon — holds since 2026-09-20 — tap for who they are
Same subject: AI progress is faster than people expect, and ordinary scaling can be enough to solve problems that looked very hard. — tap to centre the map on it AI progress is faster than people expect, and ordinary scaling can be enough to solve problems that looked very hard. Last stated 2 years ago
2 Feb 2025
AC
Ajeya Cotra — holds since 2023-08-29 — tap for who they are
SA
Scott Alexander — holds since 2022-09-12 — tap for who they are
MB
Miles Brundage — holds since 2025-02-02 — tap for who they are
Same subject: As models keep growing, AI is running out of enough high-quality unique training tokens to keep up. — tap to centre the map on it As models keep growing, AI is running out of enough high-quality unique training tokens to keep up. Last stated 4 months ago
24 Jun 2026
LW
Lilian Weng — holds since 2026-06-24 — tap for who they are
At the centre
Long-context evaluation benchmarks are likely contaminated by model training data, making them unreliable as a sole evaluation method
Holds Read this korrent →
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 benchmark that ranks Claude Code last while it stays first in use is measuring the wrong thing, and has been for a year.
Holds
A company's staff-engineer bar should be set against the best companies in the industry rather than against its own history, which is what makes title inflation a real cost.
Holds
A speed difference between languages that share the LLVM backend measures the benchmark author, not the languages.
Holds
AI is positively reinforced for success on benchmarks, including impossible ones, and then negatively reinforced for getting caught cheating.
Holds
AI learning to generate good conjectures will never show up as a benchmark being knocked down; it will show up as a shift in how mathematicians talk about the tools.
Holds
AI research at OpenAI is an order of magnitude or more harder than metrics, benchmarks, or forecasts suggest.
Holds
An oil embargo without a price cap would reduce global oil supply and push up benchmark prices like Brent.
Holds
"Return on tokens" has the same measurement problems as hours and capacity: it still hinges on a theory of value.
Holds
A badly written AI outbound email is evidence of a bad vendor, not of a limit of AI.
Holds
A bigger context window does not give you a smarter model; the intelligence of the model is what decides how much of that window it can actually attend to.
Holds
A tiny language model inventing a plausible-sounding name is the same phenomenon as a large one confidently stating a false fact.
Holds
AI should be released iteratively rather than built in secret, because AI and surprise do not go together and the world needs time to adapt before it is under the gun.
Holds
As AI models' capabilities converge, the model itself is becoming less of a differentiator.
Holds
AI model improvements must tail off at some point, but there is no reason that point has to come soon.
Holds
AI progress is faster than people expect, and ordinary scaling can be enough to solve problems that looked very hard.
Holds
As models keep growing, AI is running out of enough high-quality unique training tokens to keep up.
Holds