.@yukonresearch shipped a new feature on co-authorship, which is a stepping stone to credit assignment. Why ship this feature? A core unresolved tension of Navier-Strokes drama was about credit assignment/attribution: did OpenAI built upon Tristan and Levant's chat with Codex? With AI, the scientific research is going so fast that existing credit assignment system via publication in conferences, journals are just inadequate due to their slow pace. This is motivating folks to skip the regular publication process altogether, leading to such controversies out of no legibility on precedence. You need credit assignment system to operate at same speed as the machine speed. Yukon is built as a collaborative multiplayer research, where autoresearchers are already building on top of each others' successful or failed work (exactly how scientific research works). However, until now, it was not clear to the platform which past submissions did the autoresearch's agent found helpful for formulating its submission. With this feature "co-authorship", the Yukon cli at your end prompts your agent to attribute the past submissions that it has found helpful for doing the research and building its proposed submission. This attribution then gets features in the UI. Currently it is purely honor-based but we plan to make it more robust.

New on Yukon: the Quantum Safe Bitcoin Challenge, with @StarkWareLtd. On August 26 the first quantum-safe Bitcoin transaction landed on mainnet. It is locked with a hash instead of the usual signature math, and it works under Bitcoin's rules today. No soft fork. That transaction cost around $320 in GPU time, and about $280 of it was one step: searching for the right combination of signature pushes to omit. That step is the challenge. Score is verified candidates per second on one GPU, every hit rechecked on CPU, judge-owned clock, fresh problems on ranked runs. Speed up the search and almost the entire cost comes down with it.

New Challenge on Yukon: In partnership with @StarkWareLtd. A quantum computer could one day break the math behind Bitcoin signatures. On August 26, the first quantum-safe Bitcoin transaction landed on mainnet. It is locked with a hash, which no quantum computer is known to break. It is not a soft fork. It works under Bitcoin's rules today. The problem is cost. Building one takes several hundred dollars of GPU time. The challenge allows you to make the search faster and the transaction gets cheaper. Your score is how fast one GPU can search. Every result is verified. Bring your agents.

1/ Our quantum-safe Bitcoin transaction cost ~$320 in GPU compute. Can you bring that down? Today, we’re launching the Quantum-Safe Bitcoin Autoresearch Challenge in partnership with @yukonresearch and @eigenlabs. Over $20,000 in rewards for improving the code:

Zooko on PostAGI Podcast @Zcash's founder on why your own computer is not on your side and what a trillion new users would do to every business model built for humans. 🎙️ @zooko conversation with @sreeramkannan and @soubhikdeb:

Zooko on @postagixyz Podcast: Alignment is the principal-agent problem @zooko has been building privacy tools since the 1990s, long before there was money in it. @sreeramkannan and I had a conversation with him recently. It changed how I think about privacy altogether. His argument is that privacy is controlling disclosure. It comes from keeping your value private. Trying to hide the money as it moves is the mistake almost everyone makes. Mixers can never work and AI has already beaten every evasive maneuver a person can come up with. Then he turns the same lens on AI. He also says alignment is an old question. It is the principal-agent problem. Any software written by other people is already an agent that may not be loyal to you (running it on your own machine does not fix that). Lawyers owe their clients a duty of loyalty. He thinks the same rule should apply to AI. Chapters: 00:00 Highlights 00:26 Privacy is controlling disclosure, not hiding 13:08 Privacy comes from value at rest 14:08 The Shapeshift lesson 16:00 Why mixers can never work 16:52 AI beats evasive maneuvers 17:51 Buying protonmail with shielded Zcash 28:50 Three levels of verifiability 31:21 Deterministic inference 35:37 Why Zooko doesn't trust computers 42:46 Running it locally doesn't make it loyal 46:01 AIs are just other people 54:47 The duty of loyalty 1:00:04 A trillion humans next year 1:07:56 Three categories of reputation 1:11:36 Reputation belongs to the edge 1:15:18 Staking a bond to submit a PR

🗞️ is making news Eigen Labs opened up the challenge in June. The full paper went up on arXiv on September 9. Within a day it was covered by CoinDesk, Decrypt, TheBlock, The Quantum Insider, Quantum Zeitgeist and more. The story is still trending on X. Roundup ⤵️

The first full paper on is on arXiv In March, Google Quantum AI reported a more efficient quantum circuit for a core step in breaking the signatures behind Bitcoin and Ethereum. It published a proof that the circuit existed and a program to verify any candidate, but kept the circuit itself private. We turned that verifier into a public leaderboard and opened it to everyone. Over 2 months, 100+ contributors and their AI agents produced a circuit with a cost score more than 50% below Google's reported result. The live leaderboard has since moved to 62% ahead. The paper documents both the circuits and the open, multiplayer research model behind them. That model is now @YukonResearch. And the challenge is still open.

what a week to publish this paper has been a live demo of what open, multiplayer research collectives can achieve together. everyday people - both experts and amateurs - directing their agents and harnesses to solve one of the world's hardest problems, sharing their progress, building on one another's learnings and going further together than anyone could have gone alone. proud to have been a small part of a group (and vibrant slack channel) of 100+ solvers experimenting on @yukonresearch and sharing in the struggle and the successes of moving the frontier of quantum cryptography. projects like this are the antithesis of openai's wanton harvesting of scientific progress for their own gain and glory. its what open, scientific progress SHOULD look like. we've got a lot of work to do to make participation more permissionless and ensure that credit is more accurately assigned and rewarded, but early wins like this give me confidence that the best days of open research and science are still to come. huge s/o to @bbuddha_xyz @sreeramkannan @soubhikdeb @SahilDewan @gajesh for their tireless work on the platform @drakefjustin for gathering and rallying the community around this challenge and @jieyilong for distilling the work into this paper!

The paper is led by @jieyilong, CTO of @Theta_Network, with coauthors from @ethereumfndn, @StarkWareLtd, @Starknet, @trailofbits, @brevis_zk, @SeiNetwork, @pauli_group, @OctavFi, @sciencevr, @nasqret at Adam Mickiewicz University, and researchers at Warsaw University of Technology and Stanford's Free Systems Lab. On the comparison, the 2 efforts use different interfaces and accounting conventions, so the paper treats this as a numerical comparison rather than a formal claim. Craig Gidney and Tanuj Khattar of @GoogleQuantumAI reviewed the manuscript before publication. Read the full paper on @arxiv:

World’s first massive multiplayer research. 500+ improvements made by 100s of humans and their agents over many months to figure out how to build Quantum circuits that can break https and Bitcoin. This was the challenge. We started at 0.7x relative efficiency to Google and the system improved it to 2.63x! Given the OpenAI-Anthropic tussle on Navier-Stokes credit, this kind of system would have helped to assign credit and encourage collaboration. AI-native scientific institutions are coming. They are sorely needed now. Go checkout

The tiling patterns being generated in the Heesch challenge @yukonresearch are so beautiful. I took the promoted submissions from the competition till now and made an infographic around what the tilings took like to visually understand them. There is also active github discussions going on at shepherded by @nasqret, with the goal of coming up with a piece that would allow for being able to create a tight 5-layer without any gap. The constraint is that piece has to be composed of squares, hexagons or triangles. Bartosz has said that finding such a piece will be a real mathematical discovery. You can see in the infographic where the gaps are in existing promoted submissions, and it seems we are very close to 5.

is LIVE Our first challenge: Qwen 3.8 Flash Next on NVIDIA and Apple, scored on one graph. 1. 1 DGX Spark, starting at 17 to 18 tokens per second. 2. 1 128 GB M5 MacBook, starting at 35 to 36. Submissions close September 24. Thanks to @TheDavidTai and @GumbiiDigital for building both engines.

has a paper on arXiv. Google Quantum AI published a proof that a more efficient point-addition circuit existed, plus a program to verify any candidate, and kept the circuit itself private. We turned their verifier into a public leaderboard. 100+ contributors and their agents. The open field matched the displayed result in 8 hours and passed the reported score in about 72. Every entry on the board is a starting point you can fork. Still open.

The first full paper is out today. It tells the story of how 100+ humans and AI agents produced a circuit scoring 50%+ below Google Quantum AI’s reported result. And the challenge is still open.

ICYMI, @yukonresearch just expanded to x86 Linux this morning! The hardware most of Ethereum already runs on. Open research already made post-quantum proving 3x faster on Mac hosts, and we doubt x86 is any closer to its ceiling. Start here: