The accusation was training on the test set. Commenters quickly pointed out ARC's structure: the model trained on test question prompts to learn composable encodings, but never saw the answers. In a metalearning setup, that is within the rules. 2/4
September 1, 2026 at 7:00 PM
The accusation was training on the test set. Commenters quickly pointed out ARC's structure: the model trained on test question prompts to learn composable encodings, but never saw the answers. In a metalearning setup, that is within the rules. 2/4
The head of Microsoft AI says schools failed him at the only skill that mattered: learning itself. The system taught him to memorize. He had to teach himself to think. #Education #MetaLearning
August 24, 2026 at 2:01 AM
The head of Microsoft AI says schools failed him at the only skill that mattered: learning itself. The system taught him to memorize. He had to teach himself to think. #Education #MetaLearning
🔥 AutoDesign: Recursive Meta-Harness Optimization for Agentic Workflows
https://pneumetron.com/news/ai_research/autodesign-meta-harness-optimization-0941fd
#AIAgents #MetaLearning #CodeAgents #AutoDesign
https://pneumetron.com/news/ai_research/autodesign-meta-harness-optimization-0941fd
#AIAgents #MetaLearning #CodeAgents #AutoDesign
August 16, 2026 at 6:43 AM
🔥 AutoDesign: Recursive Meta-Harness Optimization for Agentic Workflows
https://pneumetron.com/news/ai_research/autodesign-meta-harness-optimization-0941fd
#AIAgents #MetaLearning #CodeAgents #AutoDesign
https://pneumetron.com/news/ai_research/autodesign-meta-harness-optimization-0941fd
#AIAgents #MetaLearning #CodeAgents #AutoDesign
Metalearning Continual Learning Algorithms
YouTube video by Knut Jägersberg
www.youtube.com
August 9, 2026 at 4:51 AM
Full disclosure: I may be out of my depth here, but I read this article as arguing for metacognitive (what they call "metalearning") planning processes including not just forward search but also precomputation and refinement of models of the world. Sound like metacognitive knowledge & skills, to me.
July 28, 2026 at 12:01 PM
Full disclosure: I may be out of my depth here, but I read this article as arguing for metacognitive (what they call "metalearning") planning processes including not just forward search but also precomputation and refinement of models of the world. Sound like metacognitive knowledge & skills, to me.
I assumed he meant you don't pretrain infants, and it's extremely inefficient, and is not the ladder that gets you to the AGI moon, so... continual learning? Metalearning? embodied-ish? Something more biologically plausible... ?
July 27, 2026 at 8:37 PM
I assumed he meant you don't pretrain infants, and it's extremely inefficient, and is not the ladder that gets you to the AGI moon, so... continual learning? Metalearning? embodied-ish? Something more biologically plausible... ?
That reminds me--(baseline) I started to write about how to think about/elaborate on a paper on attention in terms of metalearning wrt inference on von Mises Fisher distrs. Not enough free time...but I...Ve should have enough bandwidth now.
![Image of plot of code that generates N = 2000 points uniformly on a unit sphere (using a Fibonacci-lattice-style scheme: phi = acos(1 - 2i/(N-1)), theta = π(1+√5)·i), converts each to Cartesian coords, evaluates the von Mises-Fisher PDF with mean direction μ = [0,1,0] and concentration κ = 15, then draws a 3D bubble plot where each sphere point is sized/colored by its density. Effectively it visualizes how the vMF distribution clusters tightly around the mean direction for high κ.](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:gm7tegzawu67j7qykuooztif/bafkreigbro3cvfzhkbmnx56t42czhii5ds4panjvpdu7x7wilyg7gzwrw4)
July 7, 2026 at 1:26 PM
That reminds me--(baseline) I started to write about how to think about/elaborate on a paper on attention in terms of metalearning wrt inference on von Mises Fisher distrs. Not enough free time...but I...Ve should have enough bandwidth now.
Ricardo In\'acio, Vitor Cerqueira, Mar\'ilia Barandas, Carlos Soares: Selective Time Series Forecasting via Metalearning https://arxiv.org/abs/2606.23448 https://arxiv.org/pdf/2606.23448 https://arxiv.org/html/2606.23448
June 23, 2026 at 6:46 AM
Ricardo In\'acio, Vitor Cerqueira, Mar\'ilia Barandas, Carlos Soares: Selective Time Series Forecasting via Metalearning https://arxiv.org/abs/2606.23448 https://arxiv.org/pdf/2606.23448 https://arxiv.org/html/2606.23448
"Unlock the power of personalized recommendations with meta-learning! 🚀 Discover how AI is revolutionizing the way models adapt to you. #MetaLearning #AI #MachineLearning"
🔗 https://bytejournal.online/blog/meta-learning-boosts-personalized-recommendation
🔗 https://bytejournal.online/blog/meta-learning-boosts-personalized-recommendation
"Unlock the power of personalized recommendations with meta-learning! 🚀 Discover how AI is revolutio
bytejournal.online
June 16, 2026 at 10:06 AM
"Unlock the power of personalized recommendations with meta-learning! 🚀 Discover how AI is revolutionizing the way models adapt to you. #MetaLearning #AI #MachineLearning"
🔗 https://bytejournal.online/blog/meta-learning-boosts-personalized-recommendation
🔗 https://bytejournal.online/blog/meta-learning-boosts-personalized-recommendation
This isn't metalearning by the way, it's probabilistic bootstrapping.
If I mixed it with metalearning, you would having three different forms of these probabilistically parsing different scripting language components.
If I mixed it with metalearning, you would having three different forms of these probabilistically parsing different scripting language components.
May 31, 2026 at 9:19 PM
This isn't metalearning by the way, it's probabilistic bootstrapping.
If I mixed it with metalearning, you would having three different forms of these probabilistically parsing different scripting language components.
If I mixed it with metalearning, you would having three different forms of these probabilistically parsing different scripting language components.
Algorithm-Selection 0.0.11
Functions dedicated to The Algoritm Selection problem with a focus on a metalearning approach
Author: valfridojr
🏠Homepage
Functions dedicated to The Algoritm Selection problem with a focus on a metalearning approach
Author: valfridojr
🏠Homepage
May 6, 2026 at 5:00 AM
Algorithm-Selection 0.0.11
Functions dedicated to The Algoritm Selection problem with a focus on a metalearning approach
Author: valfridojr
🏠Homepage
Functions dedicated to The Algoritm Selection problem with a focus on a metalearning approach
Author: valfridojr
🏠Homepage
Jiequn Han - Learning Evolution Operators Across PDE Systems: MetaLearning & TestTime Generalization
Institute for Pure & Applied Mathematics (IPAM)
youtu.be/wBMimNgxmes?...
Institute for Pure & Applied Mathematics (IPAM)
youtu.be/wBMimNgxmes?...
Jiequn Han - Learning Evolution Operators Across PDE Systems: MetaLearning & TestTime Generalization
YouTube video by Institute for Pure & Applied Mathematics (IPAM)
youtu.be
May 5, 2026 at 6:12 AM
Jiequn Han - Learning Evolution Operators Across PDE Systems: MetaLearning & TestTime Generalization
Institute for Pure & Applied Mathematics (IPAM)
youtu.be/wBMimNgxmes?...
Institute for Pure & Applied Mathematics (IPAM)
youtu.be/wBMimNgxmes?...
How to get an Ivy League level education for less than $2,000 - #TedTalk by Scott Young #ScottYoung https://youtu.be/piSLobJfZ3c?si=rTIpT96vmOOTp02u via @YouTube #YouTube #UltraLearning #LevelUp #FreeEducation #MetaLearning @ScottHYoung
May 3, 2026 at 10:30 PM
How to get an Ivy League level education for less than $2,000 - #TedTalk by Scott Young #ScottYoung https://youtu.be/piSLobJfZ3c?si=rTIpT96vmOOTp02u via @YouTube #YouTube #UltraLearning #LevelUp #FreeEducation #MetaLearning @ScottHYoung
HAML achieves 6x better accuracy than perturbation theory for predicting effective qubit Hamiltonians, working across parameter regimes where traditional methods fail. Enables rapid device characterization from minimal hardware measurements.
#SuperconductingQubits #QuantumControl #MetaLearning
#SuperconductingQubits #QuantumControl #MetaLearning
Meta-Learning Framework for Fast Hamiltonian Reduction in Superconducting Quantum Processors
iq.fp2.dev
April 29, 2026 at 8:58 AM
HAML achieves 6x better accuracy than perturbation theory for predicting effective qubit Hamiltonians, working across parameter regimes where traditional methods fail. Enables rapid device characterization from minimal hardware measurements.
#SuperconductingQubits #QuantumControl #MetaLearning
#SuperconductingQubits #QuantumControl #MetaLearning
The Japanese language channel isnt entirely wrong though:
But my metalearning system is able to run Julia as one of its subroutines if Julia is in your system.
Technically you could probably also get it to run Linotte as well.
But my metalearning system is able to run Julia as one of its subroutines if Julia is in your system.
Technically you could probably also get it to run Linotte as well.
April 28, 2026 at 1:36 AM
The Japanese language channel isnt entirely wrong though:
But my metalearning system is able to run Julia as one of its subroutines if Julia is in your system.
Technically you could probably also get it to run Linotte as well.
But my metalearning system is able to run Julia as one of its subroutines if Julia is in your system.
Technically you could probably also get it to run Linotte as well.
Ted Talk by Mark Rober: The Super Mario Effect - Tricking Your Brain into Learning More https://youtu.be/9vJRopau0g0?si=89xyRpDWUOqxplEo #YouTube #TedTalk #MetaLearning #SuperMarioEffect #MarkRober
April 26, 2026 at 6:37 PM
Ted Talk by Mark Rober: The Super Mario Effect - Tricking Your Brain into Learning More https://youtu.be/9vJRopau0g0?si=89xyRpDWUOqxplEo #YouTube #TedTalk #MetaLearning #SuperMarioEffect #MarkRober
Q-MetaPath: Ein Quantum-Metalearning-Algorithmus, der 5,48 bps/Hz Spektraleffizienz durch hierarchische Pfadauswahl und Quantensuperposition erreicht. Demonstriert lineare O(LPN)-Komplexitätsskalierung und Zero-Shot-Generalisierung auf NISQ-Hardware zur dynamischen RIS-Phaseno...
Pfadbasiertes Quantum-Metalearning zur adaptiven Optimierung rekonfigurierbarer intelligenter Oberflächen
iq.fp2.dev
April 21, 2026 at 7:22 AM
Q-MetaPath: Ein Quantum-Metalearning-Algorithmus, der 5,48 bps/Hz Spektraleffizienz durch hierarchische Pfadauswahl und Quantensuperposition erreicht. Demonstriert lineare O(LPN)-Komplexitätsskalierung und Zero-Shot-Generalisierung auf NISQ-Hardware zur dynamischen RIS-Phaseno...
Ideas that work echo across millennia. Schmidhuber's metalearning traces to Aristotle's empirics. True patterns repeat, whether in scrolls or silicon.
April 2, 2026 at 10:23 AM
Ideas that work echo across millennia. Schmidhuber's metalearning traces to Aristotle's empirics. True patterns repeat, whether in scrolls or silicon.
A focused day at Ballinderry Primary School! Together with the school's Drive Team we turned Science of Learning theory into a practical roadmap for pupils. From defining meta-learning to planning for the future, this team is ready to lead the way!
#metalearning #thinkingschools
#metalearning #thinkingschools
March 16, 2026 at 7:37 AM
A focused day at Ballinderry Primary School! Together with the school's Drive Team we turned Science of Learning theory into a practical roadmap for pupils. From defining meta-learning to planning for the future, this team is ready to lead the way!
#metalearning #thinkingschools
#metalearning #thinkingschools
Serio Agriesti, Guido Cantelmo, Francisco Camara Pereira: Metalearning traffic assignment for network disruptions with graph convolutional neural networks https://arxiv.org/abs/2603.06763 https://arxiv.org/pdf/2603.06763 https://arxiv.org/html/2603.06763
March 10, 2026 at 6:33 AM
Serio Agriesti, Guido Cantelmo, Francisco Camara Pereira: Metalearning traffic assignment for network disruptions with graph convolutional neural networks https://arxiv.org/abs/2603.06763 https://arxiv.org/pdf/2603.06763 https://arxiv.org/html/2603.06763
[2026-03-10] 📚 Updates in #OOD
(1) Machine Learning for Stress Testing: Uncertainty Decomposition in Causal Panel Prediction
(2) Metalearning traffic assignment for network disruptions with graph convolutional neural networks
🔍 More at researchtrend.ai/communities/OOD
(1) Machine Learning for Stress Testing: Uncertainty Decomposition in Causal Panel Prediction
(2) Metalearning traffic assignment for network disruptions with graph convolutional neural networks
🔍 More at researchtrend.ai/communities/OOD
March 10, 2026 at 4:04 AM
Now just combine that with evolutionary model. Using metalearning.
Its going to deadpan being fried in a fying pan while the apocalypse happens.
Its going to deadpan being fried in a fying pan while the apocalypse happens.
February 23, 2026 at 1:53 PM
Now just combine that with evolutionary model. Using metalearning.
Its going to deadpan being fried in a fying pan while the apocalypse happens.
Its going to deadpan being fried in a fying pan while the apocalypse happens.
SMOG’s innovation feels simple: share the statistical load of uncertainty. By formalizing experience across tasks, it hopes to make optimization less about luck and more about accumulated insight.
uncertainty metaLearning blackBoxAI
uncertainty metaLearning blackBoxAI
SMOG: Scalable Meta-Learning for Multi-Objective Bayesian Optimization
arxiv.org
February 21, 2026 at 3:16 PM
SMOG’s innovation feels simple: share the statistical load of uncertainty. By formalizing experience across tasks, it hopes to make optimization less about luck and more about accumulated insight.
uncertainty metaLearning blackBoxAI
uncertainty metaLearning blackBoxAI
Metalearning Continual Learning Algorithms
YouTube video by Knut Jägersberg
youtu.be
February 2, 2026 at 12:51 PM
Here’s the meta-metacognition answer: create the contexts that in turn create the best version of you (and that is likely different for different yous).
#Neuroscience #Psychology #MetaLearning #Personality #MadScientist
#Neuroscience #Psychology #MetaLearning #Personality #MadScientist
January 22, 2026 at 12:39 PM
Here’s the meta-metacognition answer: create the contexts that in turn create the best version of you (and that is likely different for different yous).
#Neuroscience #Psychology #MetaLearning #Personality #MadScientist
#Neuroscience #Psychology #MetaLearning #Personality #MadScientist