# discovery Articoli collegati

Il Centro Notizie HTX fornisce gli articoli più recenti e le analisi più approfondite su "discovery", coprendo tendenze di mercato, aggiornamenti sui progetti, sviluppi tecnologici e politiche normative nel settore crypto.

Tsinghua AI Mathematician Emerges: From Intuition to Theorem, Contributing to an 84-Page Quantum Algorithm Paper

Tsinghua University’s Intelligent Industry Research Institute (AIR) has developed an AI mathematician agent named AIM, designed not just to solve math problems but to actively participate in early-stage research. In a recent study, researchers collaborated with AIM to develop "Sign Embedding Quantum Algorithms," resulting in an 84-page paper on quantum algorithms for matrix equations and functions. The research began with a human researcher's intuition: can rational approximation serve as a design principle for quantum algorithms? AIM helped expand this idea into multiple candidate research directions. Human researchers then filtered and focused on the most promising path. AIM assisted in organizing theorems, generating proof drafts, and performing complexity analysis, while humans maintained oversight, auditing assumptions and refining derivations. This case illustrates a human-AI collaborative workflow: AI rapidly explores and expands research avenues, generates draft materials, and aids in checking derivations; human researchers provide critical judgment on direction, value, and validity. The process emphasizes "high-throughput candidate generation + human value gating + AI-assisted audit and repair + human final integration." The resulting quantum algorithm framework offers a unified approach to several matrix problems, advancing quantum linear algebra under more general conditions. This work suggests AI's role in theoretical research is evolving from task-specific assistance to supporting the entire research lifecycle—enhancing exploration and efficiency while keeping human expertise central to guiding inquiry and ensuring rigor. *Paper & System Links:* - AIM application report: https://arxiv.org/abs/2606.24899 - Quantum algorithm paper: https://arxiv.org/abs/2604.25333 - AIM repository: https://github.com/TheoryFoundry/AIMv2

marsbit07/10 02:53

Tsinghua AI Mathematician Emerges: From Intuition to Theorem, Contributing to an 84-Page Quantum Algorithm Paper

marsbit07/10 02:53

From Pizza to Unit of Account: A Prehistory of Bitcoin Price Discovery

From Pizza to Unit of Account: The Prehistory of Bitcoin Price Discovery This article traces Bitcoin's earliest price discovery mechanisms, focusing on its functional evolution as a unit of account rather than its price trajectory. The analysis centers on a nine-month period from October 2009 to July 2010, identifying three distinct, sequential layers of price formation. The narrative begins with the cost-of-production anchor established by NewLibertyStandard in October 2009, which calculated a unilateral USD/BTC exchange rate based on the electricity cost of mining. This constituted a posted rate, not a market-discovered price. The second layer emerged with peer-to-peer (P2P) discovery mechanisms starting in March 2010. This included Dustin Dollar's Bitcoin Market platform, which introduced a basic public order book, and forum-based实物 trades. The pivotal event in this phase was Laszlo Hanyecz's purchase of two pizzas for 10,000 BTC on May 22, 2010. Critically, the offer was made exclusively in BTC ("10,000 bitcoins for a couple of pizzas"), marking the first documented instance where Bitcoin functionally acted as a unit of account to price another good in a real transaction, 21 days before Hanyecz later provided a USD anchor (~$25). The third layer commenced in July 2010 with Jed McCaleb's launch of the Mt.Gox exchange. Its continuous order book, with last price, highs, lows, and volume, provided the first standardized, externally referenceable format for BTC/USD prices. The article argues this three-stage evolution—production cost anchor → P2P discovery → centralized continuous quotation—exhibits structural similarities to the historical price discovery paths of other asset classes, such as 17th-century VOC shares in Amsterdam and 19th-century grain futures on the Chicago Board of Trade. It posits that all markets follow a coarse-grained path from private bookkeeping to posted quotes to continuous matching, placing Bitcoin's early development within a centuries-long lineage of financial institutional evolution.

marsbit05/19 00:02

From Pizza to Unit of Account: A Prehistory of Bitcoin Price Discovery

marsbit05/19 00:02

In 2026, Has the AI Agent Economy Truly Started to Operate?

The AI Agent economy reached a critical inflection point in January 2026, with three foundational layers—payments, trust, and social collaboration—becoming production-ready. The x402 protocol processed over 20 million transactions, ERC-8004 launched on Ethereum mainnet, and over 1.2 million autonomous agents registered on Moltbook. Infrastructure is now mature, but the product layer is still underdeveloped. While protocols are validated, key gaps remain in discovery mechanisms, capability verification, and middleware connecting trust and payment systems. x402 has stabilized with 89.2% of services priced between $0.01–$0.10, making microtransactions viable. ERC-8004 enables composable on-chain identity, reputation, and verification for agents. Despite a 68% drop in transaction count and 77% in volume, this reflects a shift from artificial volume to organic usage, with the buyer-to-seller ratio improving significantly. The biggest opportunities lie in demand-side solutions: a unified discovery layer (an "App Store for agents"), capability benchmarking beyond payment trust, and trust-gated payment middleware integrating ERC-8004 with x402. Primary paid use cases include trading signals, compute power, and granular data access. The ecosystem is consolidating around Base and Solana. While infrastructure development is concluding, builders must now focus on application-layer products, with a critical 2–3 month window to capitalize on the transition from protocol-ready to product-ready. Key risks include data noise, security vulnerabilities, and unresolved regulatory frameworks.

Odaily星球日报02/04 07:00

In 2026, Has the AI Agent Economy Truly Started to Operate?

Odaily星球日报02/04 07:00

X Introduces Starterpacks to Help New Users Find Top Crypto Accounts

X, formerly known as Twitter, is launching a new feature called ‘Starterpacks’ to help new users discover influential accounts across various interest areas, including Bitcoin and cryptocurrencies. The curated lists are based on recent engagement metrics and include top accounts from sectors like crypto, news, politics, and technology. Product lead Nikita Bier announced that the platform has compiled over 1,000 pre-made packs, with plans to expand to 3,000. The initiative aims to improve user experience by enabling quicker discovery of specialized communities aligned with individual preferences, moving beyond mainstream content.

TheNewsCrypto01/22 07:08

X Introduces Starterpacks to Help New Users Find Top Crypto Accounts

TheNewsCrypto01/22 07:08

Corpses found in 2 parked cars in NYC hours apart: cops

Two deceased individuals were discovered inside two separate parked vehicles in New York City within hours of each other, according to police. The first body was found in the Bronx, and the second was located in Brooklyn. Authorities are investigating the incidents but have not released details regarding the identities of the causes of death.

nypost12/09 00:33

Corpses found in 2 parked cars in NYC hours apart: cops