David Tse@dntse · 2026/08/25观点输出RT @babylonlabs_io: We’ll be in Hong Kong for @Bitcoinconfasia from this week🇭🇰
Meet @baby_fisherman, @yijihoon5, and @rngesus_95 to talk…
AI：延续个人公开立场，强调长期主义、执行效率与行业方向判断。
David Tse@dntse · 2026/08/17观点输出SBC is the least academic of the academic conferences. Crypto trade shows sit at one extreme. Closed academic conferences sit at the other. SBC occupies the middle ground. Having no formal proceedings gives technical researchers the freedom to tell the story behind the work.
AI：延续个人公开立场，强调长期主义、执行效率与行业方向判断。
David Tse@dntse · 2026/08/16观点输出One year later, we found a way. Combining garbled circuits with witness encryption ideas producing a concrete improvement in cryptographic proof efficiency.
AI：延续个人公开立场，强调长期主义、执行效率与行业方向判断。
David Tse@dntse · 2026/08/16赛道影响At SBC 2025, I crossed out witness encryption as a dead end. Our garbled circuit solution required 42 gigabytes of data and an on-chain cost of $15,000. Dan Boneh sat in the front row and said "not yet."
AI：偏主题投资表达，释放对基础设施与新叙事的偏多信号。
David Tse@dntse · 2026/08/09赛道影响Congratulations on solving a beautiful problem. This is bringing back old memories ; I still remember first discussing this problem with Sergio Verdu when he was on sabbatical at Berkeley in the late 1990's . With a wink of an eye it's now 25 years later.
Some perspective on this problem from someone who has been around. This problem belongs to the class of problems called "multiuser or MIMO detection", pioneered by Sergio Verdu and received a lot of attention in the late 80's and early 90's, In the late 90's and early 2000's, there was a resurgence of interest in this problem via connections with random matrix theory. I myself have worked on a bunch of these problems, evaluating performance of different MIMO detectors. The problem that AI and @DimitrisPapai
have solved is the ultimate of these problems because the optimal MIMO detector is the joint ML detector. I am very happy to see it solved.
That being said, I have to say that MIMO detection as a whole has made little impact on how actual wireless communication systems are built. Because wireless communication systems, like all communication systems, use coding, while MIMO detection only talks about uncoded systems; the N information bits here in this problem are uncoded. It turns out that much lower SNR can be achieved with coding. Take this problem as an example, with coding, a constant SNR* = 1.32 is sufficient to support an information rate of 1 bit per antenna, in contrast to the growing SNR = 2 log N result just proved. That's one reason why although this problem is beautiful as a mathematical problem, it has not received the full attention of the information theory community even in those days of peak activity. Claude Shannon has already told us in his 1948 information theory paper: to achieve the ultimate limit of communication, one needs to do coding. Uncoded systems are sub-optimal. So SNR = 2 log N is only optimal among uncoded communication systems, but SNR* = 1.32 is optimal among all communication systems.
The problem that Claude Shannon solved in his 1948 paper was the capacity of point to point communication, The problem here is one example (so called point to point MIMO channel), and hence the optimal SNR* = 1.32 can already be computed by Shannon's 1948 theory (no AI needed). Most of the information theorists' attention in the hey days of wireless communications research has been to extend Shannon's theory to network information theory problems, such as the broadcast channel, the relay channel and the interference channels. In those problems , one ask what is the best information rate (or equivalently, the minimum SNR needed) that a network of users can communicate with each other. These problems have been proposed since the 60's and the 70's and to this day none have been solved in their full generality. I'd love to see AI take a crack at some of those open problems.
David Tse@dntse · 2026/08/09赛道影响Congratulations on solving a beautiful problem. This is bringing back old memories ; I still remember first discussing this problem with Sergio Verdu when he was on sabbatical at Berkeley in the late 1990's . With a wink of an eye it's now 25 years later.
Some perspective on this problem from someone who has been around.
This problem belongs to the class of problems called "multiuser or MIMO detection", pioneered by Sergio Verdu and received a lot of attention in the late 80's and early 90's, In the late 90's and early 2000's, there was a resurgence of interest in this problem via connections with random matrix theory. I myself have worked on a bunch of these problems, evaluating performance of different MIMO detectors. The problem that AI and @DimitrisPapail have solved is the ultimate of these problems because the optimal MIMO detector is the joint ML detector. I am very happy to see it solved.
That being said, I have to say that MIMO detection as a whole has made little impact on how actual wireless communication systems are built. Because wireless communication systems, like all communication systems, use coding, while MIMO detection only talks about uncoded systems; the N information bits here in this problem are uncoded. It turns out that much lower SNR can be achieved with coding. Take this problem as an example, with coding, a constant SNR* = 1.32 is sufficient to support an information rate of 1 bit per antenna, in contrast to the growing SNR = 2 log N result just proved. That's one reason why although this problem is beautiful as a mathematical problem, it has not received the full attention of the information theory community even in those days of peak activity. Claude Shannon has already told us in his 1948 information theory paper: to achieve the ultimate limit of communication, one needs to do coding. Uncoded systems are sub-optimal. So SNR = 2 log N is only optimal among uncoded communication systems, but SNR* = 1.32 is optimal among all communication systems,.
The problem that Claude Shannon solved in his 1948 paper was the capacity of point to point communication, The problem here is one example (so called point to point MIMO channel), and hence the optimal SNR* = 1.32 can already be computed by Shannon's 1948 theory (no AI needed). Most of the information theorists' attention in the hey days of wireless communications research has been to extend Shannon's theory to network information theory problems, such as the broadcast channel, the relay channel and the interference channels. In those problems , one ask what is the best information rate (or equivalently, the minimum SNR needed) that a network of users can communicate with each other. These problems have been proposed since the 60's and the 70's and to this day none have been solved in their full generality. I'd love to see AI take a crack at some of those open problems.
David Tse@dntse · 2026/08/08赛道影响@DimitrisPapail isn't this problem NP-hard?
AI：偏主题投资表达，释放对基础设施与新叙事的偏多信号。
David Tse@dntse · 2026/08/08观点输出Doesn't this guy look good?
AI：延续个人公开立场，强调长期主义、执行效率与行业方向判断。
David Tse@dntse · 2026/08/06赛道影响@DimitrisPapail That's a big deal if AI can improve the upper bound by even a tiny bit. @sreeramkannan @chandranair
AI：偏主题投资表达，释放对基础设施与新叙事的偏多信号。
David Tse@dntse · 2026/07/31政策影响I'm a big believer in a principle from @danboneh: "The best crypto is always developed in public."
If you're building, make it public as soon as it's ready so the community can attack it.
This is how you build security that holds up under real-world scrutiny.
AI：偏政策推进，强调合规落地与规则明晰，对监管主线更敏感。
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