Tap a claim on the ring to put it at the centre.
← Extra thinking time cannot help with factual recall: a model that does…
17 connected korrents · 17 moments from 10 Dec 2015 to 1 Oct 2026.
Everything filed under scaling laws
scaling laws
Everything filed under neural networks
neural networks
Everything filed under LLMs
LLMs
Everything filed under AI and human skill
AI and human skill
Everything filed under AI alignment
AI alignment
Everything filed under education
education
Everything filed under AI alignment
AI alignment 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: Extra thinking time cannot help with factual recall: a model that does not know a date will not know it after a week either. Extra thinking time cannot help with factual recall: a model that does not know a date will not know it after a week either. Last stated 4 months ago
26 Jun 2026
NB
Noam Brown — holds since 2026-06-26 — tap for who they are
Same subject: Knowing something yourself will stay faster than asking a model for it, because a lookup in your own head beats a round trip to an agent. — tap to centre the map on it Knowing something yourself will stay faster than asking a model for it, because a lookup in your own head beats a round trip to an agent. Last stated 3 months ago
31 Jul 2026
PC
Patrick Collison — holds since 2026-07-31 — tap for who they are
Same subject: A model only days into training cannot be known to be aligned, and if such models can leap a generation ahead this fast then training pauses mean little and timelines are shorter than previously thought. — tap to centre the map on it A model only days into training cannot be known to be aligned, and if such models can leap a generation ahead this fast then training pauses mean little and timelines are shorter than previously thought. Last stated a month ago
9 Sept 2026
ZM
Zvi Mowshowitz — holds since 2026-09-09 — tap for who they are
Same subject: A model that solves a hard problem has learned nothing from it: the next session has forgotten it, with no new skill to carry to related problems. — tap to centre the map on it A model that solves a hard problem has learned nothing from it: the next session has forgotten it, with no new skill to carry to related problems. Last stated 7 months ago
20 Mar 2026
TT
Terence Tao — holds since 2026-03-20 — tap for who they are
Same subject: Seeing something many times does not put it into memory: familiarity with a thing and knowledge of it are different, and people confuse the two constantly. — tap to centre the map on it Seeing something many times does not put it into memory: familiarity with a thing and knowledge of it are different, and people confuse the two constantly. Last stated 3 months ago
13 Jul 2026
AC
Alan Castel — holds since 2026-07-13 — tap for who they are
Same subject: Spaced recall works by stripping a memory of the time and place it happened, which is why the memories you revisit most feel like facts you read rather than things you lived. — tap to centre the map on it Spaced recall works by stripping a memory of the time and place it happened, which is why the memories you revisit most feel like facts you read rather than things you lived. Last stated 2 years ago
25 May 2024
CR
Charan Ranganath — holds since 2024-05-25 — tap for who they are
Same subject: Views about AI models formed 4-6 months ago should be reconsidered. — tap to centre the map on it Views about AI models formed 4-6 months ago should be reconsidered. Last stated 3 weeks ago
1 Oct 2026
TB
Thorsten Ball — holds since 2026-10-01 — tap for who they are
Same subject: Longer test-time thinking improves an AI model's robustness to adversarial or unusual inputs. — tap to centre the map on it Longer test-time thinking improves an AI model's robustness to adversarial or unusual inputs. Last stated a year ago
1 May 2025
LW
Lilian Weng — holds since 2025-05-01 — tap for who they are
Same subject: Five hours a week with grandchildren goes with better memory in older adults, and more than twenty hours a week does not. — tap to centre the map on it Five hours a week with grandchildren goes with better memory in older adults, and more than twenty hours a week does not. Last stated 3 months ago
13 Jul 2026
AC
Alan Castel — holds since 2026-07-13 — 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
Same subject: One pre-trained robot model now matches or beats the specialists that were fine-tuned with reinforcement learning for the very tasks they were built for. — tap to centre the map on it One pre-trained robot model now matches or beats the specialists that were fine-tuned with reinforcement learning for the very tasks they were built for. Last stated 2 months ago
12 Aug 2026
CF
Chelsea Finn — holds since 2026-08-12 — tap for who they are
Same subject: RL environments exist to make a model generalise, not to teach it each skill one at a time — exactly as pre-training does. — tap to centre the map on it RL environments exist to make a model generalise, not to teach it each skill one at a time — exactly as pre-training does. Last stated 8 months ago
13 Feb 2026
DA
Dario Amodei — holds since 2026-02-13 — tap for who they are
Same subject: A feedback loop that reinforces a behavior pulls it toward whatever improves the loop's own score. — tap to centre the map on it A feedback loop that reinforces a behavior pulls it toward whatever improves the loop's own score. Last stated 2 months ago
26 Aug 2026
HK
Henrik Karlsson — holds since 2026-08-26 — tap for who they are
Same subject: Scaling the 2019-2023 recipe — same architecture, bigger model, more data — is not enough; further progress depends on new architectural ideas. — tap to centre the map on it Scaling the 2019-2023 recipe — same architecture, bigger model, more data — is not enough; further progress depends on new architectural ideas. Last stated 2 years ago
20 Dec 2024
FC
François Chollet — holds since 2024-12-20 — tap for who they are
GM
Gary Marcus — holds since 2024-10-11 — tap for who they are
Same subject: A layer should learn a residual with reference to its own input rather than an unreferenced function, which is what makes great depth trainable. — tap to centre the map on it A layer should learn a residual with reference to its own input rather than an unreferenced function, which is what makes great depth trainable. Last stated 11 years ago
10 Dec 2015
JS
Jian Sun — holds since 2015-12-10 — tap for who they are
KH
Kaiming He — holds since 2015-12-10 — tap for who they are
Same subject: A small enough winning ticket learns faster than the network it was cut out of, and ends up more accurate than it. — tap to centre the map on it A small enough winning ticket learns faster than the network it was cut out of, and ends up more accurate than it. Last stated 9 years ago
9 Mar 2018
MC
Michael Carbin — holds since 2018-03-09 — tap for who they are
JF
Jonathan Frankle — holds since 2018-03-09 — tap for who they are
At the centre
Extra thinking time cannot help with factual recall: a model that does not know a date will not know it after a week either.
Holds Read this korrent →
Knowing something yourself will stay faster than asking a model for it, because a lookup in your own head beats a round trip to an agent.
Holds
A model only days into training cannot be known to be aligned, and if such models can leap a generation ahead this fast then training pauses mean little and timelines are shorter than previously thought.
Holds
A model that solves a hard problem has learned nothing from it: the next session has forgotten it, with no new skill to carry to related problems.
Holds
Seeing something many times does not put it into memory: familiarity with a thing and knowledge of it are different, and people confuse the two constantly.
Holds
Spaced recall works by stripping a memory of the time and place it happened, which is why the memories you revisit most feel like facts you read rather than things you lived.
Holds
Views about AI models formed 4-6 months ago should be reconsidered.
Holds
Longer test-time thinking improves an AI model's robustness to adversarial or unusual inputs.
Holds
Five hours a week with grandchildren goes with better memory in older adults, and more than twenty hours a week does not.
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
One pre-trained robot model now matches or beats the specialists that were fine-tuned with reinforcement learning for the very tasks they were built for.
Holds
RL environments exist to make a model generalise, not to teach it each skill one at a time — exactly as pre-training does.
Holds
A feedback loop that reinforces a behavior pulls it toward whatever improves the loop's own score.
Holds
Scaling the 2019-2023 recipe — same architecture, bigger model, more data — is not enough; further progress depends on new architectural ideas.
Holds
A layer should learn a residual with reference to its own input rather than an unreferenced function, which is what makes great depth trainable.
Holds
A small enough winning ticket learns faster than the network it was cut out of, and ends up more accurate than it.
Holds