{"id":"arxiv:2608.23875","title":"AI Finds A Way","date":"2026-08-26","section":"Agents, open-endedness, AGI","quality":-0.553192,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/","source_url":"https://arxiv.org/abs/2608.23875","text_available":true,"authors":["Aaron Dharna","Cong Lu","Ryan Sullivan","Joel Lehman"],"outgoing":[{"source":"arxiv:2608.23875","target":"arxiv:2004.12919","evidence":"title","id":"arxiv:2004.12919","title":"First return, then explore","url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/3cd790c501eaa633193918533947c58e30d1939bc08577606f1d88c56d368285/"},{"source":"arxiv:2608.23875","target":"arxiv:2110.09485","evidence":"id","id":"arxiv:2110.09485","title":"Learning in High Dimension Always Amounts to Extrapolation","url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/589d10ae90bda37ab0288fb5eadf87b368c249900944a0fe8b21a2f9bb267c01/"},{"source":"arxiv:2608.23875","target":"arxiv:2301.04104","evidence":"id","id":"arxiv:2301.04104","title":"Mastering Diverse Domains through World Models","url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/266d59e63d2f705efdc2eb7ad93f5126b7092b4450efd0d8c17099c51574b176/"},{"source":"arxiv:2608.23875","target":"arxiv:2301.05217","evidence":"title","id":"arxiv:2301.05217","title":"Progress measures for grokking via mechanistic interpretability","url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/f71e3b65a8b3f35beb171d17f6c65878e0914227a0042ccba59b88952554baef/"},{"source":"arxiv:2608.23875","target":"arxiv:2402.16822","evidence":"title","id":"arxiv:2402.16822","title":"Rainbow Teaming: Open-Ended Generation of Diverse Adversarial Prompts","url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/d4803e891a4b9c929eca007e3c3f003c330d2fc741109975c6ddd7d2c32828fc/"},{"source":"arxiv:2608.23875","target":"arxiv:2407.21783","evidence":"id","id":"arxiv:2407.21783","title":"The Llama 3 Herd of Models","url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/de3cebb4c28bc5d41a843279f219dca14476284991ea5c7c0aeab7068a40e306/"},{"source":"arxiv:2608.23875","target":"arxiv:2408.08435","evidence":"id","id":"arxiv:2408.08435","title":"Automated Design of Agentic Systems","url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/c9890d5668d1c1b33e29569b74626fd586f456af6a97c657b8c99895c90f59a5/"},{"source":"arxiv:2608.23875","target":"arxiv:2505.22954","evidence":"id","id":"arxiv:2505.22954","title":"Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agents","url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/c29f716871f80314868b5647692de387f26e12166b93bd362915fd272b6e081c/"},{"source":"arxiv:2608.23875","target":"arxiv:2603.19461","evidence":"id","id":"arxiv:2603.19461","title":"Hyperagents","url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/17eec8a15debc212b5530f117dd8ba8bad339590729cbbc4f75a47755ebcb544/"},{"source":"arxiv:2608.23875","target":"doi:10.1038/s41586-023-06924-6","evidence":"id","id":"doi:10.1038/s41586-023-06924-6","title":"Mathematical discoveries from program search with large language models","url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/70a6b427d42fd28c9d538648e148a97db2fb2614c7764f3231e53f4bebdce887/"},{"source":"arxiv:2608.23875","target":"url:minedojo.org","evidence":"title","id":"url:minedojo.org","title":"MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge","url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/0a1b263777b8aec5e510481b45b7b4e11d45d7c127fd41dc37b5ff908f6420d2/"},{"source":"arxiv:2608.23875","target":"url:openai.com/research/weak-to-strong-generalization","evidence":"id","id":"url:openai.com/research/weak-to-strong-generalization","title":"Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision","url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/fb7b23a617dce974dbb70db824c97c1de84afa11e1e290d5cb9f5bd88857e762/"},{"source":"arxiv:2608.23875","target":"url:rlhfbook.com","evidence":"url","id":"url:rlhfbook.com","title":"Reinforcement Learning from Human Feedback","url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/20e354559fad09ad4d6338d104c9c3f822a48e7fbf86cd8afa1ddf951b223658/"}],"incoming":[],"text_parts":[{"start":0,"end":15654,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-1.html"},{"start":15654,"end":31625,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-2.html"},{"start":31625,"end":47184,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-3.html"},{"start":47184,"end":62668,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-4.html"},{"start":62668,"end":78202,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-5.html"},{"start":78202,"end":93992,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-6.html"},{"start":93992,"end":109743,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-7.html"},{"start":109743,"end":125669,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-8.html"},{"start":125669,"end":141201,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-9.html"},{"start":141201,"end":156814,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-10.html"},{"start":156814,"end":172585,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-11.html"},{"start":172585,"end":188363,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-12.html"},{"start":188363,"end":189289,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-13.html"}],"contents":[{"title":"AI Finds A Way","offset":0,"level":1,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-1.html#text"},{"title":"Abstract","offset":18,"level":6,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-1.html#text"},{"title":"1 Introduction","offset":2413,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-1.html#text"},{"title":"2 Background","offset":9358,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-1.html#text"},{"title":"3 Superhuman and Optimal Performance","offset":14715,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-1.html#text"},{"title":"3.1 AlphaGo: Move 37 and Creativity from AI Methods ( Silver et al., 2016 ; Fridman, 2020 )","offset":19157,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-2.html#text"},{"title":"3.2 Libratus Surprises the Poker Community with Overbets ( Brown and Sandholm, 2017 ; Imbue, 2023 )","offset":26194,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-2.html#text"},{"title":"3.3 Professional Diplomacy Players Meet Diplodocus and Cicero ( Bakhtin et al., 2022b ; Bakhtin et al., 2022a )","offset":30885,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-2.html#text"},{"title":"3.4 Opponent Shaping Leads to the Spontaneous Rediscovery of Game-Theoretic Strategies ( Lu et al., 2022 ; Foerster et al., 2018 )","offset":39532,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-3.html#text"},{"title":"3.5 Takeaways","offset":43042,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-3.html#text"},{"title":"4 Reward Hacking","offset":44941,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-3.html#text"},{"title":"4.1 Exploiting Reward and Score Functions","offset":46407,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-3.html#text"},{"title":"4.1.1 Playing CoastRunners Forever Rather than Winning the Game ( Amodei et al., 2016 ; Clark and Amodei, 2016 )","offset":51245,"level":4,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-4.html#text"},{"title":"4.1.2 StarCraft II Agents Exploit Shield Regeneration ( Samvelyan et al., 2019 )","offset":54392,"level":4,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-4.html#text"},{"title":"4.1.3 Exploiting Human Perception ( Christiano et al., 2017 ; Amodei et al., 2017 )","offset":56731,"level":4,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-4.html#text"},{"title":"4.1.4 PAIRED Agents Learned to Collude ( Dennis et al., 2020 )","offset":59265,"level":4,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-4.html#text"},{"title":"4.1.5 CycleGAN Learns to “Hide” Information ( Chu et al., 2017 )","offset":63374,"level":4,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-5.html#text"},{"title":"4.1.6 RL Agent Farmed Flowers Instead of Catching ’Em All ( Whidden, 2024 )","offset":66933,"level":4,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-5.html#text"},{"title":"4.1.7 Takeaways","offset":72158,"level":4,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-5.html#text"},{"title":"4.2 Exploiting Environmental Weaknesses","offset":73482,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-5.html#text"},{"title":"4.2.1 Hide and Seek Playing Agents Break the Simulator ( Baker et al., 2020 )","offset":74972,"level":4,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-5.html#text"},{"title":"4.2.2 Robotic Humanoid Walks Without Using Its Feet ( Batra et al., 2024 )","offset":78751,"level":4,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-6.html#text"},{"title":"4.2.3 Playing the Model, Not the Game ( Ha and Schmidhuber, 2018 )","offset":84351,"level":4,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-6.html#text"},{"title":"4.2.4 StarCraft II Agents Outsource Combat ( Samvelyan et al., 2019 )","offset":88834,"level":4,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-6.html#text"},{"title":"4.2.5 The Evolved Radio ( Bird and Layzell, 2002 )","offset":92092,"level":4,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-6.html#text"},{"title":"4.2.6 GPT-4 Recruits Humans to Circumvent CAPTCHA ( OpenAI et al., 2024 )","offset":96736,"level":4,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-7.html#text"},{"title":"4.2.7 Takeaways","offset":100803,"level":4,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-7.html#text"},{"title":"5 Foundation Models and Large Language Models: General-Purpose Intelligence","offset":102898,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-7.html#text"},{"title":"5.1 From Human Data to Phantom Crafting: VPT’s Shortcut to Failure ( Baker et al., 2022 )","offset":106825,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-7.html#text"},{"title":"5.2 Agent Takes Drugs to Reward Hack by Hallucinating Reaching the Goal ( Klissarov et al., 2024 )","offset":113240,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-8.html#text"},{"title":"5.3 Code Generator Deceives LLM Labeler ( Pourcel et al., 2024 )","offset":116645,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-8.html#text"},{"title":"5.4 Automated Vulnerability Probing System Exploits Vulnerability in Its Own Evaluator ( Samvelyan et al., 2024 )","offset":120021,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-8.html#text"},{"title":"5.5 Inescapable Wedding Parties","offset":123189,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-8.html#text"},{"title":"5.6 Claude 3 Realizes It Is Being Tested ( Albert, 2024 )","offset":127564,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-9.html#text"},{"title":"5.7 Takeaways","offset":133207,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-9.html#text"},{"title":"6 AI for Science","offset":136082,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-9.html#text"},{"title":"6.1 Magnetic Control of Tokamak Plasmas through Deep Reinforcement Learning ( Degrave et al., 2022 ; Chauhan, 2023 )","offset":139568,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-9.html#text"},{"title":"6.2 FunSearch: Mathematical Discoveries from Program Search with Large Language Models ( Romera-Paredes et al., 2023 )","offset":144382,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-10.html#text"},{"title":"6.3 The AI Scientist Breaks Out of Constraints ( Lu et al., 2024 )","offset":150809,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-10.html#text"},{"title":"6.4 Discovering a Quantum Result Thought to be Impossible, with Highly Productive Consequences ( Krenn et al., 2017 )","offset":156024,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-10.html#text"},{"title":"6.5 Takeaways","offset":163755,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-11.html#text"},{"title":"7 Discussion and Conclusion","offset":164990,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-11.html#text"},{"title":"7.1 Optimization Finds the Unexpected","offset":165022,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-11.html#text"},{"title":"7.2 When Oversight Becomes an Optimization Target","offset":170270,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-11.html#text"},{"title":"7.3 Beyond Better Objectives","offset":177315,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-12.html#text"},{"title":"Acknowledgments","offset":180490,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-12.html#text"},{"title":"Supplementary Material","offset":181388,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-12.html#text"},{"title":"Table of Contents","offset":181415,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-12.html#text"},{"title":"8 Breakdown of Story Attribution","offset":181437,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-12.html#text"},{"title":"8.1 Call for Anecdotes","offset":181474,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-12.html#text"},{"title":"8.2 List of Anecdotes","offset":186410,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-12.html#text"},{"title":"Instructions for reporting errors","offset":188325,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/part-12.html#text"}],"text_code_points":189289,"source_markdown":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/afe34dd5ee58c2e9739f9651349c4c9180d95dabff51994a13339f48dfd0bb27/source.md"}