Machines Of Loving Grace
βI dealt with you know I wrote this essay Machines of Loving Grace a and one of the key questions it asked I mean it was focused on what are all the positive applications of AIβ
What the smart people are recommending. 6242 books, tools, and products endorsed by the thinkers absorb.md tracks. Ranked by how many times each has been recommended across compiled podcasts, papers, posts, and tweets.
βI dealt with you know I wrote this essay Machines of Loving Grace a and one of the key questions it asked I mean it was focused on what are all the positive applications of AIβ
βAiden is one of the co-authors of a of a paper called attention is all you needβ
βthis time we talk a lot about his paper titled on the measure of intelligence that discusses how we might define and measure general intelligence in our computing machineryβ
βThe Adolescence of Technology _Blog post by Dario Amodei_ https://darioamodei.com/essay/the-adolescence-of-technologyβ
βYou and a bunch of co-authors wrote um a blog post titled AI 2027, which is a very compelling read, and we're going to cover some of it, but I'm sure there's there are details there that we're not goiβ¦β
βAnd of course I I reference the bitter lesson all the time as one of the deep fundamental insights to the kind of broader effort of everything that goes on hereβ
βJim is interested in building generally capable autonomous agents and he recently published mind Dojo a massively multiscale benchmarking seite built on Minecraft which was an outstanding paper n we'rβ¦β
βTHERE'S A WHOLE BLOG POST THAT YOUR VIEWERS CAN READ IF THEY WANT MORE DETAILS.β
βI started reading around whether anyone had done it there was this seminal paper in 2012 called on the naturalness of softwareβ
βdownload the CFO's guide to AI and machine learning at netswuite.com/cognitive.β
βWe propose GMM-Anchored JEPA...Code is made available at https://github.com/gioannides/clustering-anchored-jepa.β
βWe provide theoretical justification and experiments on video-based world models, where our resulting planner outperforms existing planning algorithms like the cross-entropy method (CEM) and vanilla gβ¦β
βA closer look at the MET-PREVENT trial and what its null results reveal about aging interventions and trial design. Full article linked below.β
βWe introduce Rectified Distribution Matching Regularization (RDMReg), a sliced two-sample distribution-matching loss that aligns representations to a Rectified Generalized Gaussian (RGG) distribution.β¦β
βThis new Nature paper (using old models) illustrates the point of my latest Substack post on AI interfaces. AI did a good job diagnosing medical issues, but when users had to interact with chatbots thβ¦β
βMy post: https://www.oneusefulthing.org/p/claude-dispatch-and-the-power-ofβ
βFinally, we urge all vulnerable cryptocurrency communities to join the ongoing migration to PQC without delay.β
βIf you're familiar with [neural scaling laws](https://arxiv.org/abs/2001.08361), you can consider this challenge a form of L(N) optimization, where the objective is to optimize the lowest loss given aβ¦β
βI think the thing that captivated me I don't know if you felt this way it was the only white paper that I read end to end where I thought this is one of the most elegantly written pieces of non-techniβ¦β
βI just would really recommend the essay that Jack mentioned earlier by Dario um Machines of loveing Grace um I think it is probably the best positive Vision out there there's really not much literaturβ¦β
βIt is based on [LoRA](https://arxiv.org/abs/2106.09685), a training paradigm where most weights are frozen and only 1-2% of additional weights in the form of low-rank matrix perturbations are trained.β
βso there's this one work last year that I collaborated with Stanford co-authors the paper is called metamorph so it was accepted to iar and this work is about how can we learn a policy that controls nβ¦β
βAnd I read the 1950 paper by Alan Turring where he talks about the Turing test. By the way, how many people know what the Turing test is?β
βIf we go all the way back to 2016 you know before I even worked at openai when I was at Google I wrote a paper called with some colleagues some of whom are now anthropic co-founders concrete problems β¦β
βWe present Marconi, the first system that supports efficient prefix caching with Hybrid LLMs.β
βAnd if that wasn't enough, he also co-founded Google's AI research project, Google Brain, and was one of the earliest pioneers of a new neural network architecture called Transformers. Not sure that oβ¦β
βDespite strong performance on vision-language tasks, Multimodal Large Language Models (MLLMs) struggle with mathematical problem-solving, with both open-source and state-of-the-art models falling shorβ¦β
βI came across this blog that um one of the former YC Partners Daniel gross wrote but it was like How to Build the Next Googleβ
βOne story that a lot of people should know is that if you ask yourself what is the paper in all of science that has gathered the largest number of citations over the last 10 years, that paper was publβ¦β
βIn this work, we ask the question: "Do visual self-supervised approaches lag behind CLIP due to the lack of language supervision, or differences in the training data?" We study this question by trainiβ¦β
βThis paper explores a self-supervised approach that combines internet-scale video data with a small amount of interaction data (robot trajectories), to develop models capable of understanding, predictβ¦β
βThe Urgency of Interpretability _Blog post by Dario Amodei_β
βIt's fun to describe quantum teleportation in terms of particles that move both forward and backward in time. And it's more than just fun: the intuition derived from that description can guide us to nβ¦β
βThis paper rethinks the key building block for conferencing infrastructures -- selective forwarding units (SFUs)... we present Scallop, an SDN-inspired SFU that decouples video-conferencing applicatioβ¦β
βThis architecture is called joint emitting predictive architecture jpa and that's what I've been kind of focusing my efforts over the last several years.β
βGPUs, CPUs, and. . . NICs: Rethinking the Network's Role in Serving Complex AI Pipelinesβ
βIn this work, we systematically evaluate RL and control-based methods on a suite of navigation tasks...planning with a latent dynamics model proves to be a strong approach for handling suboptimal offlβ¦β
βI'm eager to speak with you specifically about this recent essay you wrote on AI and uh so you obviously many people have read this and you are a voice that many people value on this topic among otherβ¦β
β# Cobordism, spin structures, and profinite completionsβ
β# Aragog: Just-in-Time Model Routing for Scalable Serving of Agentic Workflowsβ
βEncouraging news. https://www.nytimes.com/2026/01/10/science/trump-science-budget-cuts-congress.htmlβ
βThere's a paper that's not too old by David Silver, Rich Sutton is a co-author um Donap and a bunch of other people whose title is um reward is enough.β
βhttps://theconversation.com/us-experiencing-largest-measles-outbreak-since-2000-5-essential-reads-on-the-risks-what-to-do-and-whats-coming-next-275164β
βMeasuring progress toward AGI: A cognitive frameworkβ
βWe critically examine the limitations of current AI models in achieving autonomous learning and propose a learning architecture inspired by human and animal cognition.β
β_Blog post by Google DeepMind_ https://deepmind.google/blog/protecting-people-from-harmful-manipulationβ
βMore on our approach to the Model Spec: https://openai.com/index/our-approach-to-the-model-spec/β
βYes: https://simonwillison.net/guides/agentic-engineering-patterns/red-green-tdd/β
βYou should read the red team report: https://red.anthropic.com/2026/mythos-preview/β
βhave you see this paper? https://llm-attacks.orgβ
βItβs Called Silicon Sampling, and Itβs Going to Ruin Public Opinion Polling https://www.nytimes.com/2026/04/06/opinion/ai-polling.htmlβ
βWe prove that, in various tasks, quantum machines can learn from exponentially fewer experiments than those required in conventional experiments.β
βIn this work, we propose a new type of architecture for quantum generative adversarial networks (entangling quantum GAN, EQ-GAN) that overcomes some limitations of previously proposed quantum GANs.β
βI was reading a paper the the Princeton swe paper um where their coding agents can now solve 12.5% of GitHub issues versus I think 3.8% um when it was just ragβ
β6 Takeaways From Trumpβs News Conference on Iran https://www.nytimes.com/2026/04/06/us/politics/trump-iran-news-conference.html?smid=tw-shareβ
βI'm going to quickly summarize, but do read this article. It's not that long and uh it's going to be an important one.β
βAnd then the last one is how does all of that come together to something that a lot of people really want to use it. And that's a combination of you know the utility of all the subplatforms essentiallβ¦β
βDr. Lichtenstein says the process for making these new guidelines was to dispense with politics and stick to the evidence. Well, I think there's no agenda behind these guidelines that they're evidenceβ¦β
βI encourage you to check it out. We will link it in the show notes.β
βFor more insights on the survey, check that out at the information.com.β
βyou mentioned later in this wired article that you stumbled upon the single algorithm Theory popularized by Jeff Hawkinsβ
βthe prevailing opinion was that human intelligence derived from thousands of simple agents working in concert this is what mit's Marvin Minsky called The Society of mindβ
βthere's also a recipe Pioneer by one of my friends Eric Bron and his collaborators sanit called a task-based analysis of jobs that I found to be very usefulβ
βThe Hatsugai-Kohmoto model thus emerges as a minimal, exactly solvable platform for interaction-mediated non-reciprocal many-body dynamics.β
βNew post: Endnotes on 2020: Crypto and Beyondβ
β# Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture _Blog post by Yann LeCun_β
βThanks, Ingrid Fadelli, for this nice article about our recently published paper with @RobertHuangHY and @MSoleimanifar. The paper describes a surprisingly simple protocol for verifying that a many-quβ¦β
βThis one: https://journals.aps.org/prd/pdf/10.1103/PhysRevD.12.3845β
β# Suppressing quantum errors by scaling a surface code logical qubit _ArXiv paper co-authored by Hartmut Neven_β
βThis was all the way back in 2017 when we created this paper called the βUnsupervised Sentiment Neuron.ββ
βWrote about how to help young men, many of whom are failing π https://www.profgalloway.com/boys-to-men/β
βI still remember that year kind of the thrill when I read like a freshly big Alex net paper just like out of nurs right and I really appreciated its Simplicity and minimalismβ
βToday's really good. The real topic, what we'll be discussing, America is rewriting its relationship with news. So we are thrilled to have Pew Research join the show. I've been using their research siβ¦β
βI've discussed supply chain attacks, specifically in the context of the Node.js package manager, NPM ecosystem, in this 2022 update.β
βVentureBeat has a very good article explaining what happened to Axios.β
βI'm very excited about FMA and more generally Foundation models for robotics at the beginning we briefly touch on this statement that the history of AI is a history of unification before alexnet 2012β
βFrom: https://t.co/a5VGTQk18Lβ
βThere's there's another blog post that um perhaps you know about called the intelligence curse. Yes. Um which uh goes over some of this ground as well which which I recommend people look up.β
βThat's Julia Minson, professor at the Harvard Kennedy School, co-author of the HBR article, A Smarter Way to Disagree, and author of the new book, How to Disagree.β
βSee also my posts: * https://t.co/cezOk1ilHyβ
β* https://t.co/wCjLcUoyxYβ
βThe Federal Reserve put out a paper, CalShe and the rise of macro markets. Maybe you can dive into that because that seems right at the sweet spot of where you came from and where you're from.β
βThe other thing that I'll say that is kind of interesting about the Fed paper is three months prior to the Fed releasing this paper, Kalshi Research released a paper. It was our first paper, and it waβ¦β
β* https://t.co/LTMrsxFeuSβ
βyou know we published it in physical review letters and it got a lot of attention and I think we had a little article in Scientific American that was very proud ofβ
βThere are still likely a few weeks before the company files publicly, but PitchBook recently put out a great report looking at the business and its valuation. I want to bring on PitchBook's Franco Graβ¦β
βthis essay called Keep Your Identity Small, I think, by by Paul Graham is his name.β
βThe other research work that I want to highlight is a work from Stanford called the Stanford Smallville where they instantiate 25 agents in like little world and have them kind of interact with withβ¦β
βyosha bju had a very interesting paper on this in the 2000s using neural Nets to do this actually it was probably one of the first anywayβ
βunless you are not paying attention to anything in Quantum there was a huge research paper that IBM came out with it was published in nature can you talk a little bit about the significance when I reaβ¦β
βIn this work, we introduce CSA-Graphs, a privacy-preserving structural dataset.β
βIt is called Smartphones, Online Music Streaming, and Traffic Fatalities.β
βAmanda mole wrote this great piece and Bloomberg highly recommend checking that out.β
β# Self-Supervised Learning of Split Invariant Equivariant Representationsβ
βI hope you read our paper when it's on archive soonβ
βThis repository contains the environments in our paper: > [SECANT: Self-Expert Cloning for Zero-Shot Generalization of Visual Policies](https://linxifan.github.io/secant-site/)β
βI do think it's it's potentially The Next Step there's uh there's this idea that'll give you a link to learn more about that yeah yeah I think read the S4 the S4 paperβ
βI really enjoyed the New Yorker profile by Josh Rothman who I've worked with in the pastβ
βHow to become a supermanager with AI: https://www.lennysnewsletter.com/p/how-to-become-a-supermanager-withβ
βHow custom GPTs can make you a better manager | Hilary Gridley (Head of Core Product at Whoop): https://www.youtube.com/watch?v=xDMkkOC-EhIβ
βAcademic papers ARE recommendations if someone is telling the reader to read them (type=paper).β
β# Need is All You Need: Homeostatic Neural Networks Adapt to Concept Shiftβ
βTo address these limitations, we propose COMLLM, a generative framework that enables foresighted decision-making in MEC systems.β
βIn this work, we derive the non-Markovian dynamics between TLS and qubits during a SWAP-like two-qubit gate and the associated average gate fidelity for frequency-tunable Transmon qubits. This gate deβ¦β
βAnswering the question of existence of efficient quantum algorithms for NP-hard problems require deep theoretical understanding of the properties of the low-energy eigenstates and long-time coherent dβ¦β
βI just published "Governance, Part 2: Plutocracy Is Still Bad": https://t.co/LTMrsxFeuSβ
βWe introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of hybrid quantum-classical models for classical or quantum data.β
βWe present a quantum kernel method for high-dimensional data analysis using Google's universal quantum processor, Sycamore.β
βA combination of self-aggrandizement and elitism has convinced American universities that our services are worth indebting generations of young people, and now risking becoming agents of spread. #nomeβ¦β
βAs the search continues for useful applications of noisy intermediate scale quantum devices, variational simulations of fermionic systems remain one of the most promising directions. Here, we perform β¦β
βHere we explore which heuristic quantum algorithms for combinatorial optimization might be most practical to try out on a small fault-tolerant quantum computer.β
β# Resolving catastrophic error bursts from cosmic rays in large arrays of superconducting qubitsβ
βyou can check out the paper that Michael Nielson had about this ideaβ
βNew blog post on coordination, collusion, decentralization and forking: https://t.co/8aB7T9NsjTβ
βIn this work, we develop intuitive constructions for a large class of these algorithms based on connections to simple dynamics of quantum systems, quantum walks, and classical continuous relaxations.β
βGiven a quantum circuit, a quantum computer can sample the output distribution exponentially faster in the number of bits than classical computers. A similar exponential separation has yet to be estabβ¦β
βSo you recently posted a paper, "STaR: Bootstrapping Reasoning With Reasoning."β
βThe use of quantum computing for machine learning is among the most exciting prospective applications of quantum technologies.β
βand i'd recommend you go to uh on vitalik.ca i have this article called a quadratic payments a primer and highly recommended it's kind of at least my attempt so far and explaining the intuition behindβ¦β
βi'd recommend the casper ffg paperβ
βVery Deep Convolutional Networks for Large-Scale Image Recognition - please cite this paper if you use the VGG models in your work.β
βDeep Residual Learning for Image Recognition - please cite this paper if you use the ResNet model in your work.β
βRethinking the Inception Architecture for Computer Vision - please cite this paper if you use the Inception v3 model in your work.β
β# A hierarchical loss and its problems when classifying non-hierarchicallyβ
βNew monster post: my own current perspective on the recent debates around techno-optimism, AI risks, and ways to avoid extreme centralization in the 21st century. https://vitalik.eth.limo/general/202β¦β
βIn this whitepaper, we introduce the Snake Optimizer for efficiently and quickly solving such optimization problems by leveraging concepts in artificial intelligence, dynamic programming, and graph opβ¦β
β# Focus beyond quadratic speedups for error-corrected quantum advantageβ
βyou had a really good post on your blog saying endnotes on 2020 and it was about a lot more than just cryptoβ
βHere, by measuring the time-dependent evolution and fluctuation of out-of-time-order correlators, we experimentally investigate the dynamics of quantum scrambling on a 53-qubit quantum processor. We eβ¦β
βIn this paper, we report the design and characterization of a prototype cryogenic CMOS integrated circuit that has been optimized for the control of transmon qubits. The circuit has been integrated inβ¦β
β# Implicit Rank-Minimizing Autoencoderβ
β_ArXiv paper co-authored by Hartmut Neven_β
βWe demonstrate the application of the Google Sycamore superconducting qubit quantum processor to combinatorial optimization problems with the quantum approximate optimization algorithm (QAOA).β
βWe propose a quantum algorithm for inferring the molecular nuclear spin Hamiltonian from time-resolved measurements of spin-spin correlators, which can be obtained via nuclear magnetic resonance (NMR)β¦β
βyou know for me i've read the alan turing 1950 paper computing machinery and intelligence paper back before i knew how to code and i remember reading it you know it lays out the turing test but then iβ¦β
βQuantum Neural Networks (QNNs) are a promising variational learning paradigm with applications to near-term quantum processors, however they still face some significant challenges.β
βThis is an updated version of supplementary information to accompany "Quantum supremacy using a programmable superconducting processor", an article published in the October 24, 2019 issue of Nature. Tβ¦β
βSummary of changes since arXiv:1910.11333v1 (submitted 23 Oct 2019)β
β# GLoMo: Unsupervisedly Learned Relational tGraphs as Transferable Representationsβ
βWe introduce a fermion-to-qubit mapping defined on ternary trees, where any single Majorana operator on an $n$-mode fermionic system is mapped to a multi-qubit Pauli operator acting nontrivially on $\β¦β
βFirst of all, Center for Humane Technology, my nonprofit, has a solutions report that's coming out around the time of the film. It's a PDF. It has, I think, seven major solutions. I want everybody to β¦β
βHere we report on experimental observations of relaxation in such simulations, measured on up to 1440 qubits with microsecond resolution. By initializing the system in a state with topological obstrucβ¦β
βHere, we simulate the dynamics of the one-dimensional Fermi-Hubbard model using 16 qubits on a digital superconducting quantum processor. We observe separations in the spreading velocities of charge aβ¦β
βwell I read papers that's what people should be writing uh generally science papers uh that's where the knowledge is and I will that's where the knowledge will likely remainβ
β# Do Robots powered by a Quantum Processor have the Freedom to swerve? _ArXiv paper co-authored by Hartmut Neven_β
β# A quantum algorithm for training wide and deep classical neural networks _ArXiv paper co-authored by Hartmut Neven_β
βQuantum many-body systems display rich phase structure in their low-temperature equilibrium states. However, much of nature is not in thermal equilibrium.β
βHere, we describe a protocol for using a fault-tolerant quantum computer to calculate stopping power from a first-quantized representation of the electrons and projectile. Our approach builds upon theβ¦β
βThese posts from 2016-17 basically advocating for someone to build Uniswap.β
β# Optimizing quantum gates towards the scale of logical qubitsβ
βIf you find the work useful in your research, please cite the DeepTraffic paperβ
βWe introduce Personalized RewardBench, a novel benchmark designed to rigorously assess reward models' capacity to model personalized preferences.β
β_Blog post by Yann LeCun_β
β# Sampling diverse near-optimal solutions via algorithmic quantum annealingβ
βHere, we introduce a quantum-inspired family of nonlocal Nonequilibrium Monte Carlo (NMC) algorithms by developing an adaptive gradient-free strategy that can efficiently learn key instance-wise geomeβ¦β
βauthor of the Hill algorithm for linear systemsβ
βhe's actually the author of the first paper on, on, on barren plateaus.β
βthere are some, you know, we've recently studied quantum convolutional neural networks, and it looks like this might be an interesting useful architecture in the NISQ eraβ
βat one point i read alan turing's 1950 paper called computing machinery and intelligence which is the turing test paperβ
βHere my friend Jens Eisert and I assess the current status and the challenges ahead.β
βand if that sounds interesting to you you may be interested in the abstraction and reasoning corpus which is a kind of intelligence test that's meant to be used by humans and machines that i released β¦β
βHere we give classical sampling algorithms with better total variation distance and Kullback-Leibler divergence than these experiments and a computational cost quadratic in the number of modes.β
βWe observe that the comment of [1, arXiv:2302.07897] is consistent with [2] on key points: i) the microscopic mechanism of the experimentally observed teleportation is size winding and ii) the system β¦β
β# On the duality between contrastive and non-contrastive self-supervised learning _Blog post by Yann LeCun_β
βYann LeCun on a vision to make AI systems learn and reason like animals and humansβ
βFormation of robust bound states of interacting microwave photonsβ
β# Noise-resilient Edge Modes on a Chain of Superconducting Qubits _ArXiv paper co-authored by Hartmut Neven_β
βActive Self-Supervised Learning: A Few Low-Cost Relationships Are All You Needβ
βThe paper that blew my mind was InstructGPT, because it pointed out that you can take the pretrained model, which is autocomplete, and if you just fine-tune it on text that looks like conversations, tβ¦β
βone example of this is that there was a few papers came out of Cornell over the last few months like one of them was they published this after good financial cryptography 2016 workshop that they were β¦β
β# Augmented Language Models: a Survey _Blog post by Yann LeCun_β
β# The SSL Interplay: Augmentations, Inductive Bias, and Generalizationβ
βBest of AI Curation Vol. 3, covers: No-gradient architecture, LLM tool making and mastery (3 papers!), RLHF without RL, an uncensored LLM, an open course, and more. Deep dive with me: π§΅β
βBest of AI Curation Vol. 3, covers: No-gradient architecture, LLM tool making and mastery (3 papers!), RLHF without RL, an uncensored LLM, an open course, and more. Deep dive with me: π§΅β
βBest of AI Curation Vol. 3, covers: No-gradient architecture, LLM tool making and mastery (3 papers!), RLHF without RL, an uncensored LLM, an open course, and more. Deep dive with me: π§΅β
βBest of AI Curation Vol. 3, covers: No-gradient architecture, LLM tool making and mastery (3 papers!), RLHF without RL, an uncensored LLM, an open course, and more. Deep dive with me: π§΅β
βthis is a a paper that Jake Browning who's a philosopher and and and and I published in the noima magazine which is a philosophy magazine about the fact that a system that is purelyβ
β# VICRegL: Self-Supervised Learning of Local Visual Featuresβ
βWe released a new error correcting code it's a new low density parity check code that we actually call the gross code This new code requires orders and magnitude fewer cubits than the surface code andβ¦β
βI wrote about the argument that this weekβs AI regulations represent βregulatory captureβ and make the case that if even Elon Musk is asking the government to do something, we have every right to mandβ¦β
βI read the readings of the works of like Ray Kurt and felt like there was something there and that AI was really going to go somewhereβ
β# Revisiting Feature Prediction for Learning Visual Representations from Videoβ
βHere is a detailed explanation of the account takeover hack that happened on this account this week. https://avc.xyz/anatomy-of-a-twitterx-account-takeover-hackβ
β# Visualizing Dynamics of Charges and Strings in (2+1)D Lattice Gauge Theoriesβ
βIn this work, we identify representation collapse in the model's intermediate layers as a key factor limiting their reasoning capabilities. To address this, we propose Sequential Variance-Covariance Rβ¦β
βIn Ref. [1], Bravyi et al. found examples of Bivariate Bicycle (BB) codes with similar logical performance to the surface code but with an improved encoding rate.β
β# Observation of disorder-free localization using a (2+1)D lattice gauge theory on a quantum processorβ
βin 1969 uh F fets from University of Washington published this beautiful paper called oper and conditioning of cortical unit activity where he was able to record a single unit neuron from a monkey andβ¦β
βIf you still think AGI is sci-fi or too far away to matter, consider checking out [https://www.safe.ai/work/statement-on-ai-risk](https://www.safe.ai/work/statement-on-ai-risk) and who disagree with yβ¦β
β# JEPA as a Neural Tokenizer: Learning Robust Speech Representations with Density Adaptive Attentionβ
βfrom a theoretical complexity point of view I actually find this great uh this paper by Scott Aronson actually established the hardness of this problemβ
βthis is a famous paper by Ed far look at optimization where you can come up with circuits that will be uh potentially useful for these problemsβ
βThere's another Super popular paper that you author call koala cognitive architectures for language agentsβ
βIn this work, we propose Visual-Predictive Instruction Tuning (VPiT) - a simple and effective extension to visual instruction tuning that enables a pretrained LLM to quickly morph into an unified autoβ¦β
βOkay, Scott Aronson has a beautiful sorry beautiful paper that can show random circuits are actually hard to simulate.β
βwhen people thought Trump was a clown and he was just a clown that's all he was so I wrote a blog post someone remember it it was called clown genius so I reframed him as somebody who was a showan andβ¦β
βthis term I heard come out of this paper Alpha codium it basically achieves state-of-the-art kind of like coding performance not necessarily through better models or better prompting strategies but thβ¦β
βThis paper presents METIS, the first RAG system that jointly schedules queries and adapts the key RAG configurations of each query, such as the number of retrieved text chunks and synthesis methods, iβ¦β
βIf you haven't read the GDP eval paper from open AI, it is an interesting paper.β
βWe introduce a Navigation World Model (NWM), a controllable video generation model that predicts future visual observations based on past observations and navigation actions.β