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first_img The Ethereum Foundation launches the better.codes challenge to advance the provable security of hash SNARKs

The open automated research challenge better.codes, created by the Ethereum Foundation's formal verification team in collaboration with Yukon and zkSecurity, is now live. This platform formalizes the self-contained problems from the Proximity Prize in Lean and places the machine-checked reliability bounds of koalaIRS12 on a public leaderboard, allowing anyone to contribute to improvements, advancing hash-based SNARK and post-quantum Ethereum-related security benchmarks.Solvers can bring their own AI agents to prove higher lower bounds of reliability for this Reed-Solomon proximity problem, moving towards a fixed target of 128 bits. The Lean kernel verifies each submission, and the promoted proofs will enhance the public bounds, with new lemmas, proof techniques, and impossibility results being synchronized upstream for reuse by all participants. Most hash SNARKs in production environments rely on related proximity gaps and related conclusion agreements, while the currently provable results remain below the benchmarks trusted by researchers; this challenge aims to narrow this gap in an open, incremental, and verifiable manner.koalaIRS12 originates from related papers and is end-to-end formalized in ArkLib. Participants can log in via GitHub and clone the challenge repository, submitting under fixed theorem statements and verification frameworks; results confirmed by the comparator and Lean kernel are recorded in a public repository, noting the solver and the model used. Today's launch features the reliability challenge that raises the proven lower bound of koalaIRS12 to 128 bits, with more problems potentially added later, subject to project terms.

first_img Stripe's acquisition of OpenRouter for over $8 billion claims that the private model is more suited for the "singularity era," and the IPO may be delayed

According to Axios, payment giant Stripe stated in a letter to investors that January 1 marks "the beginning of a singularity," viewing it as a significant turning point in a long-term trend, and believes that maintaining a private structure is best suited for this critical moment, with the IPO likely to remain on hold. The company reported a 41% year-on-year revenue growth in the first half of the year and a 43% increase in free cash flow; 88% of the companies in Forbes AI 50 (including OpenAI and Anthropic) are building on its platform, with revenue from AI and crypto companies more than doubling year-on-year.Stripe also confirmed the acquisition of the AI routing platform OpenRouter, with the transaction amount not publicly disclosed; Axios learned that the amount exceeds $8 billion and is primarily paid in stock. Stripe stated that remaining private helps advance mergers and acquisitions and long-term investments without diluting shareholders, with its equity count now lower than three years ago, and a compound annual return of about 31% since the D round. The company stated that the total payment volume on its platform is expected to reach $1.9 trillion by 2025, a year-on-year growth of 34%; in February this year, the employee stock purchase valuation was approximately $159 billion. There are also reports that Stripe is in discussions with Advent International to acquire PayPal for about $53 billion.

first_img ByteDance discusses training a model with over 50 trillion parameters, the Seed model team adjusts the architecture

According to LatePost, ByteDance is discussing a large model with training parameters exceeding 50 trillion, surpassing Alibaba's Qwen 3.8-Max (24 trillion) and Moonlight K3 (28 trillion), making it the largest known plan in the country so far. This plan is still in its early stages and does not guarantee a final release. The new model is intended to be led by Xiang Liang, head of Seed Foundation, in collaboration with Shen Ke, who is responsible for the pre-training data of large language models. Seed is reorganizing, dividing responsibilities, and allocating resources based on this.Two weeks ago, ByteDance founder Zhang Yiming held a company-wide meeting with Seed head Wu Yonghui. Zhang reassured the team that training large models is inherently difficult and that it is acceptable to lag behind for a period of time, hoping to aim for the upper limits of intelligence and join the world's top tier. He acknowledged that programming is a key direction at present, advocating for the integration of Volcano Engine, Feishu, and Doubao resources to build computational power and data advantages, while reminding not to be led by a single hot topic. He praised Seedance's differentiated leadership and clearly opposed distillation, believing it is difficult to truly surpass and that AGI barriers should be built from a more fundamental level, stating that the company will continue to increase investment in AI.In the past six months, Seed's multimodal performance has been outstanding, with Seedance 2.0, Seedream, and others driving Volcano Engine MaaS, but the market response to the language model Seed 2.0 has been limited, and its lagging coding capabilities have affected the revenue structure. ByteDance has hired Guo Daye at a high salary to specialize in coding and has consolidated related resources. In the face of the industry's general trend of increasing model sizes, ByteDance hopes to achieve a leapfrog advantage with a larger scale while promoting the elimination of horse racing and breaking down departmental walls to concentrate efforts on tackling challenges.
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