Theta Labs and @dcunited have agreed to a multi-year partnership that puts a club-trained AI assistant in the hands of Black-and-Red supporters, answering their questions instantly, 24/7, in the club's own voice. blog.thetatoken.org/theta-la…

🚀 Seedance 2.5 and BytePlus frontier AI models are now live on Theta EdgeCloud. Developers can access ByteDance’s latest video, image, speech, and multimodal models, including Seedance 2.5 and Seedream 5.0 Pro, through Theta’s distributed AI infrastructure. Read more 👇 blog.thetatoken.org/seedance…

Congratulations to our partners @imperialcollege, who have beaten Oxford and Cambridge to be named University of the Year by @thetimes. We're proud that the Security & Machine Learning Lab at @ICComputing has chosen Theta EdgeCloud for its AI security research.

@dcunited announces new partnership with @Theta_Network. Read all about it on their website dcunited.com/news/d-c-united…

Last week, our CTO @jieyilong led a paper with that more than halved the estimated quantum hardware needed to break Bitcoin and Ethereum. We asked him a few questions about what it means and why it matters for the migration ahead. blog.thetatoken.org/cto-jiey…

LATEST: 🚨 A group of 100+ researchers from Theta Labs, the Ethereum Foundation, StarkWare, and others cut the estimated quantum computing resources needed to attack Bitcoin and Ethereum by 86% in two months.

For the past three months, a good part of my spare time has gone into ECDSA.fail. What began for me as a chance to explore quantum-circuit optimization with AI agents became a remarkable collaboration with a large group of enthusiastic participants across quantum computing, cryptography, Web3, and systems research. Today, I’m excited to share our arXiv paper documenting that effort and what we learned from it: Paper: arxiv.org/pdf/2609.09582 Full story: eigenlabs.org/blog/open-auto… ECDSA.fail is an open challenge to optimize a quantum circuit for point-addition on secp256k1, the elliptic curve used by Bitcoin and Ethereum. Why does this matter? Point-addition is a major bottleneck in implementing Shor’s algorithm for elliptic curves. A sufficiently capable fault-tolerant quantum computer could use Shor’s algorithm to recover private keys from exposed ECDSA public keys, and forge transactions to steal funds. No existing machine can run this attack today. But migration across blockchains, wallets, custody systems, and smart contracts will take years. So it makes sense to start early and understand how far the quantum resources required by Shor’s algorithm can be pushed down. With this target in mind, in roughly two months, more than 100 participants and their agents joined the challenge, and collectively produced a point-addition circuit for secp256k1. The circuit uses 1,151 logical qubits and 1.30 million Toffoli gates, giving it a Q x T score more than 50% lower than the result Google Quantum AI reported in March 2026. Because the interfaces and accounting conventions differ, this is a numerical comparison rather than a claim of formal dominance. To the best of our knowledge, it was the lowest reported Q x T score among published secp256k1 point-addition constructions as of the paper’s July 26 cutoff. A separate design optimized for width reached 825 logical qubits, exploring a very different point on the time-space tradeoff, though at the cost of many more Toffoli gates. The story began back in March when @GoogleQuantumAI reported significantly improved Shor circuits through a zero-knowledge proof without publishing their implementations. The circuit remained hidden, but the verifier provided something unusual: an objective test of whether any candidate worked and how much it cost. Eigen Labs turned that opportunity into a public benchmark, repository, and leaderboard. This became Open Autoresearch: a paradigm in which humans and AI agents address optimization problems by publishing evaluator-verified improvements to a shared public frontier. Every successful circuit became a new base for others, and documented failures became shared research notes. Initiated by @eigenlabs, the effort grew to include many independent contributors and researchers affiliated with @ethereumfndn, @Starknet, @StarkWareLtd, @Theta_Network, @brevis_zk, @QuantumFDN, @OctavFi, @trailofbits, @pauli_group, @sciencevr, and @SeiNetwork, as well as Adam Mickiewicz University in Poznań, Warsaw University of Technology, and Stanford’s Free Systems Lab. Their work spanned circuit research, validation, technical review, writing, and agent workflows. The arXiv paper explains both what the community built and how the circuits evolved. It presents the leading circuit designs and the key optimizations behind them, including a coherent version of the best-scoring circuit that supports the single-call windowed point-addition interface required by Shor’s algorithm. Building and validating the complete Shor circuit remains future work. Beyond the technical results, the paper formalizes the Open Autoresearch paradigm and distills the lessons learned from the challenge. It provides evidence that when a frontier research problem has a machine-checkable evaluator and a public leaderboard, human insight and agent-scale experimentation can combine across an open community to produce cumulative, verifiable progress. Read the paper and the full story, explore the live frontier, or bring your agent to the challenge: Paper: arxiv.org/pdf/2609.09582 Full story:eigenlabs.org/blog/open-auto… Challenge: ECDSA.fail

What impresses me most here is not that @jieyilong collaborated with hundreds of the most distinguished researchers from some of the top Web3 including Ethereum foundation, nor that they improved upon Google's Quantum AI by over 50% in 3 months. It's that they pioneered a new model of scientific research where thousands of distributed humans and AI agents collaborate and compete to solve really difficult problems, continuously. Traditionally, research productivity is limited by number of researchers, but Autoresearch instead scales with compute, AI models, orchestration and evaluation software harnesses. AI agents perform iterative experimentation 24x7, improvements are verified automatically, and the community continuously builds on the latest result instead of waiting for the next paper. This is game changing. A bleeding edge novel approach to scientific discovery. So, what does this mean for us at @Theta_Network ? This approach creates new demand for: 1- large-scale parallel inference 2- distributed GPU compute 3- automated evaluation infrastructure 4- orchestration across many models Everything changes.