CRITICAL//Consensus

Links for 2026-03-31 [Part 2] Quantum and Crypto 1. Google Quantum AI’s new whitepaper argues that quantum attacks on the elliptic-curve cryptography used by Bitcoin and Ethereum may require far fewer resources than many people assumed. For secp256k1, the authors report Shor-algorithm circuits using either about 1,200 logical qubits and 90 million Toffoli gates or about 1,450 logical qubits and 70 million Toffoli gates, and they estimate that under favorable superconducting-hardware assumptions these attacks could run in minutes with fewer than half a million physical qubits. The most novel part is their disclosure method: instead of publishing the full attack optimizations, they provide a zero-knowledge proof showing that they really do possess a circuit of the claimed size for the key elliptic-curve point-addition bottleneck, allowing outsiders to verify the resource estimate without handing would-be attackers a full blueprint. For Bitcoin, the paper’s big implication is that sufficiently fast “fast-clock” quantum machines could eventually enable “on-spend” attacks, where a public key exposed during a transaction broadcast is cracked before confirmation, while older exposed keys and dormant coins remain an even bigger long-term target; the authors therefore argue that migration to post-quantum protections should begin urgently. https://quantumai.google/static/site-assets/downloads/cryptocurrency-whitepaper.pdf 2. Oratomic’s paper makes the complementary claim that the physical-hardware cost of cryptographically relevant Shor attacks may also be much lower than standard surface-code estimates suggest, at least for neutral-atom machines with reconfigurable connectivity and high-rate qLDPC-style error correction. The authors say Shor’s algorithm could in principle run with as few as 10,000 atomic qubits, and that an architecture with roughly 26,000 physical qubits might solve ECC-256 discrete logs in around 10 days under a 1 ms stabilizer-cycle assumption, with RSA-2048 taking much longer. The point is not that Bitcoin can be broken tomorrow—this is still a theoretical resource estimate with major engineering hurdles—but that the hardware gap may be shrinking faster than expected if nonlocal neutral-atom architectures and better codes work as hoped. For Bitcoin and similar systems, that strengthens the broader message from the Google paper: even if neutral atoms are too slow for near-real-time mempool attacks, they could still threaten long-exposed public keys and dormant funds, reinforcing the case for post-quantum migration well before such machines actually exist. https://arxiv.org/abs/2603.28627 3. Automated near-term quantum algorithm discovery for molecular ground states https://arxiv.org/abs/2603.26359 Physics 1. Causality optional? Testing the “indefinite causal order” superposition https://arstechnica.com/science/2026/03/getting-formal-about-quantum-mechanics-lack-of-causality/ 2. “I Built Feynman’s Reverse Sprinkler To Solve 140 Year Old Mystery” https://www.youtube.com/watch?v=G5DzkVI4EQE 3. For decades, the original paper which introduced something called the see-saw mechanism for neutrino mass was overlooked in favour of subsequent papers by more famous physicists. https://blog.inspirehep.net/2013/06/sleeping-beauty/ Psychology & Neuroscience 1. Open Access Book: Decision Making under Deep Uncertainty https://link.springer.com/book/10.1007/978-3-030-05252-2 2. “Slack”—defined as the absence of binding constraints or having free capacity/space—is essential for any system or agent to be effective. https://www.lesswrong.com/posts/nQd64RC5vXyqiFZLD/slack-in-cells-slack-in-brains 3. Thalamic activation of the visual cortex at the single-synapse level https://www.science.org/doi/full/10.1126/science.aec9923 4. A neural algorithm for a fundamental computing problem [published in 2017] https://www.science.org/doi/10.1126/science.aam9868 5. Cerebellum as a kernel machine: A novel perspective on expansion recoding in granule cell layer [published in 2022] https://pmc.ncbi.nlm.nih.gov/articles/PMC9815768/

Links for 2026-03-31 [Part 1] AI The universe is under no obligation to set the clearing price for human labor above 2000 kcal / day. — Arthur B. 1. New paper for Tinsghua and Shenzhen says, what if AI itself runs the harness, rather than defining it in code? Given a natural language SOP of how an agent should orchestrate subagents, memory, compaction, etc., we can just have an LLM execute that logic! (And AI could design that SOP dynamically and depending on the task too) https://arxiv.org/abs/2603.25723 2. Meta-Harness: End-to-End Optimization of Model Harnesses [PDF] https://yoonholee.com/meta-harness/paper.pdf 3. AIRA_2: Overcoming Bottlenecks in AI Research Agents https://arxiv.org/abs/2603.26499 4. PivotRL: High Accuracy Agentic Post-Training at Low Compute Cost https://arxiv.org/abs/2603.21383 5. Effective Strategies for Asynchronous Software Engineering Agents https://arxiv.org/abs/2603.21489 6. Coding Agents are Effective Long-Context Processors https://arxiv.org/abs/2603.20432 7. PRBench: End-to-end Paper Reproduction in Physics Research — All agents exhibit a zero end-to-end callback success rate. https://arxiv.org/abs/2603.27646 8. AI might actually fix the information environment by putting expert knowledge in everyone’s hands https://www.conspicuouscognition.com/p/how-ai-will-reshape-public-opinion 9. Building Political Superintelligence https://freesystems.substack.com/p/building-political-superintelligence 10. “The next intelligence explosion will not be a single silicon brain, but a complex, combinatorial society specializing and sprawling like a city.” https://arxiv.org/abs/2603.20639 11. AI drones might force governments to become police states to survive. https://www.thenewatlantis.com/publications/a-shakeup-is-coming-for-the-nation-state 12. Mathematical methods and human thought in the age of AI https://terrytao.wordpress.com/2026/03/29/mathematical-methods-and-human-thought-in-the-age-of-ai/ 13. HorizonMath: Measuring AI Progress Toward Mathematical Discovery with Automatic Verification https://arxiv.org/abs/2603.15617 14. Eli Lilly reaches $2.75 billion deal with Insilico to bring AI-developed drugs to the global market https://www.cnbc.com/2026/03/29/eli-lilly-reaches-deal-to-bring-ai-developed-drugs-to-global-market.html 15. Microsoft is introducing multi-model intelligence in Researcher https://techcommunity.microsoft.com/blog/microsoft365copilotblog/introducing-multi-model-intelligence-in-researcher/4506011 16. Memristor demonstrates use in fully analog hardware-based neural network https://techxplore.com/news/2026-03-memristor-fully-analog-hardware-based.html 17. ‘I no longer knew how to work without it’: DeepSeek outage cuts off millions https://www.scmp.com/tech/big-tech/article/3348345/deepseek-outage-leaves-millions-cut-and-sparks-complaints-rivals-gain-ground

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Links for 2026-03-18 [Part 2] Neuroscience and Psychology 1. Inside the mind of a top superforecaster This piece profiles Malcolm Murray, a Good Judgment superforecaster, and shows how he structures questions, updates probabilities, and uses base rates to beat intuition. https://goodjudgment.substack.com/p/meet-superforecaster-malcolm-murray 2. Scientists revive activity in frozen mouse brains for the first time https://www.nature.com/articles/d41586-026-00756-w [no paywall: https://archive.is/kUHDL] 3. Belgian Startup ReVision Implant Wins FDA Breakthrough Status for Visual BCI https://insidebci.com/news/2026-03-16-revision-implant-secures-fda-breakthrough-device-designation-for-visual-cortical-prosthesis/ 4. Brain Implants Let Paralyzed People Type Nearly as Fast as Smartphone Users https://singularityhub.com/2026/03/17/brain-implants-let-paralyzed-people-type-with-thought-alone-nearly-as-fast-as-smartphone-users/ 5. Three anesthesia drugs all have the same effect in the brain, MIT researchers find https://news.mit.edu/2026/three-anesthesia-drugs-all-have-same-effect-brain-0317 6. Statement by the President of the Brain Preservation Foundation “regarding the misleading EON Systems “fly upload” video” https://x.com/KennethHayworth/status/2032604687212392562 Miscellaneous 1. Rats and mice exposed to cell phone radiation at doses 10 to 100 times stronger than a mobile phone — nine hours a day, for two years — didn’t get more cancer. They lived longer, and they got less disease. That’s the core finding of an independent reanalysis of the raw data from the US National Toxicology Program’s $30 million studies, the most expensive animal radiation experiments ever conducted. https://github.com/zanekoch/airpods-go-brrrrr 2. Game Theory (Open Access textbook with 165 solved exercises) https://arxiv.org/abs/1512.06808 3. The paradox of derivatives and integrals https://statmodeling.stat.columbia.edu/2026/03/14/the-paradox-of-derivatives-and-integrals/

Links for 2026-03-18 [Part 1] AI 1. Leonard Susskind: Finally I thank the chatbot who gave me the definition of scaffold in section 1.3. It was better than anything I was able to do. https://arxiv.org/abs/2603.12434 2. Letting Claude do Autonomous Research to Improve SAEs https://www.lesswrong.com/posts/rbqJoxFZtae9x93mx/letting-claude-do-autonomous-research-to-improve-saes 3. “How well can AI agents post-train language models? We built a benchmark to find out.” https://posttrainbench.thoughtfullab.com/ 4. Multi-Agent Memory from a Computer Architecture Perspective: Visions and Challenges Ahead https://arxiv.org/abs/2603.10062 5. XSkill: Continual Learning from Experience and Skills in Multimodal Agents https://arxiv.org/abs/2603.12056 6. FALCON: Fast-Weight Attention for Continual Learning https://yifanzhang-pro.github.io/FALCON/ 7. You can’t imitation-learn how to continual-learn https://www.lesswrong.com/posts/9rCTjbJpZB4KzqhiQ/you-can-t-imitation-learn-how-to-continual-learn 8. Learning to Reason without External Rewards https://arxiv.org/abs/2505.19590 9. EvoX: Meta-Evolution for Automated Discovery https://arxiv.org/abs/2602.23413 10. Simple Recipe Works: Vision-Language-Action Models are Natural Continual Learners with Reinforcement Learning https://arxiv.org/abs/2603.11653 11. “We trained Composer to self-summarize through RL instead of a prompt.” https://cursor.com/blog/self-summarization 12. AdaEvolve: Adaptive LLM-Driven Zeroth-Order Optimization https://skydiscover-ai.github.io/blog-adaevolve.html 13. Trajectory-Informed Memory Generation for Self-Improving Agent Systems https://arxiv.org/abs/2603.10600 14. Language Model Teams as Distributed Systems https://arxiv.org/abs/2603.12229 15. Temporal Straightening for Latent Planning https://agenticlearning.ai/temporal-straightening/ 16. Matching Features, Not Tokens: Energy-Based Fine-Tuning of Language Models https://arxiv.org/abs/2603.12248 17. Attention Residuals: Rethinking depth-wise aggregation https://github.com/MoonshotAI/Attention-Residuals/blob/master/Attention_Residuals.pdf 18. Online Experiential Learning for Language Models https://arxiv.org/abs/2603.16856 19. The First Open-Source Agentic AI Physicist https://theinnermostloop.substack.com/p/the-first-open-source-agentic-ai 20. Terence Tao: “Damek Davis and I have launched a "distillation challenge", to see how well the 22 million implications in universal algebra generated by the Equational Theories Project can be condensed down to a single "cheat sheet" prompt that a low-powered LLM can use to answer these questions as accurately as possible.” https://terrytao.wordpress.com/2026/03/13/mathematics-distillation-challenge-equational-theories/ 21. Measuring progress toward AGI: A cognitive framework https://blog.google/innovation-and-ai/models-and-research/google-deepmind/measuring-agi-cognitive-framework/ 22. “How do frontier AI agents fail when given the same task multiple times? We ran Claude Opus 4.5, Gemini 2.5 Pro, and GPT 5.4 on GAIA’s 165 real-world tasks with multiple repetitions per model, then examined cases where agents gave wrong answers, disagreed with themselves, or broke under tool failures and input perturbations. Below are the most instructive examples.” https://hal.cs.princeton.edu/reliability/benchmark/gaia/analysis/ 23. Nvidia and Palantir have partnered to create new “AI operating system” https://www.palantir.com/sovereignaios/ 24. Neural Thickets: Diverse Task Experts Are Dense Around Pretrained Weights https://thickets.mit.edu/ 25. LLMs as Giant Lookup-Tables of Shallow Circuits https://www.lesswrong.com/posts/a9KqqgjN8gc3Mzzkh/llms-as-giant-lookup-tables-of-shallow-circuits 26. Terrified Comments on Corrigibility in Claude’s Constitution https://www.lesswrong.com/posts/K2Ae2vmAKwhiwKEo5/terrified-comments-on-corrigibility-in-claude-s-constitution

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