2026-04-18 Sábado

Centro de Notícias - Página 187

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Is Polymarket's Pricing Wrong? 200 AI Agent Simulation of Crisis Yields Unexpected Answer

An experiment used MiroFish, an open-source multi-agent simulation platform, to model the geopolitical crisis in the Strait of Hormuz and compare the results with Polymarket's prediction market. The system generated 200 AI agents—including government officials, media, energy firms, financial traders, and civilians—and simulated 7 days of social media interaction (Twitter-like environment) based on a 5,800-character background brief. Key findings: - Organic, free-form discussions among agents produced an average probability of 47.9% for the strait reopening by April 2026, significantly higher than Polymarket's market-derived probability of 31%. - When agents were individually questioned in a formal "interview" setting, they converged to overly optimistic responses (60–75% across categories), reflecting a cooperation bias. - The most accurate predictions came from a minority of pessimistic agents (e.g., Iranian officials, financial analysts, academics) who organically expressed probabilities near 22%—aligning closely with market pricing. - The simulation revealed a structural divide: public/official statements tend toward optimism, while genuine risk assessments emerge from unstructured, adversarial discourse. The study suggests that natural interaction among specialized agents can generate valuable signals, but LLM bias and limited context remain constraints. Future work will expand data scope, use stronger models, and increase agent diversity.

marsbit03/18 06:16

Is Polymarket's Pricing Wrong? 200 AI Agent Simulation of Crisis Yields Unexpected Answer

marsbit03/18 06:16

HashKey Accelerates AI Strategy Implementation: From Organizational Efficiency to New-Generation Digital Financial Infrastructure

HashKey Group is accelerating its AI strategy, transitioning from organizational efficiency to building next-generation digital financial infrastructure. The company has established a "Group Technology Steering Committee" to oversee the overall planning and implementation of AI and cutting-edge technologies. According to CTO Devin Zhang, the move marks a shift from fragmented, individual use of AI to a group-level systematic adoption aimed at upgrading organizational capabilities. Key priorities include improving internal operational efficiency—particularly in R&D and non-R&D functions like compliance and finance—and enhancing user experience through intent-driven interactions. Initial AI applications focus on high-repetition, measurable scenarios such as automated development pipelines, threat detection, risk management, and anti-money laundering analysis. Devin emphasized that a robust security framework is essential for financial institutions adopting AI, as agent-based systems require careful management of permissions, resource access, and accountability. HashKey is taking a compliant, risk-aware approach: prioritizing back-end and internal use cases first, while cautiously evaluating customer-facing innovations like automated trading. In the long term, HashKey envisions AI and blockchain converging, with AI agents gaining digital identities and payment capabilities, potentially making blockchain a key infrastructure for managing AI-driven economies. The company aims to boost efficiency near-term, strengthen mid-term technical foundations, and ultimately contribute to the evolution of digital financial infrastructure.

marsbit03/18 06:07

HashKey Accelerates AI Strategy Implementation: From Organizational Efficiency to New-Generation Digital Financial Infrastructure

marsbit03/18 06:07

From Playing with Narratives to Coding: With L2 Fees Dropping Below 1 Cent, How Will Ethereum Make Money Without 'Selling Gas'?

Once the engine of Web3 narratives, Ethereum is undergoing a fundamental shift in 2026, moving from grand visions to pragmatic engineering. Its updated protocol priorities—Scale, Improve UX, and Harden the L1—signal a transition into an "engineering-driven survival" mode, pressured by intense competition. A key development is the upcoming Glamsterdam hard fork, which will drastically increase the mainnet Gas limit and introduce parallel execution. This architectural shift from a single-threaded to a multi-lane "highway" aims to boost throughput but challenges Ethereum's "full node democratization" principle, accelerating state growth and hardware demands. To counter this, the core team is pushing for ~10% of validators to adopt zero-knowledge proof validation ("SNARKing the L1"), a pivotal move towards computational efficiency. This engineering push is a direct response to external threats, notably Solana's Alpenglow upgrade, which slashes finality to under 150ms—a disruptive latency level competitive with traditional web infrastructure. Ethereum's modular architecture, while secure, struggles with the speed required for consumer-grade applications. Internally, Ethereum faces the "L2 paradox." While Layer-2 scaling solutions like Rollups have succeeded, driving transaction costs below $0.001 and vastly improving UX, they risk making the mainnet an invisible backend. This decouples users from Ethereum's core value propositions: decentralization and its validator network. Consequently, ETH's value capture model is being rewritten. The old "ultrasound money" narrative, fueled by gas fee burns, is fading as activity migrates to L2s. ETH is transforming from a retail-facing "gas seller" into a B2B "security and settlement service." Its new foundation rests on staking and restaking yields, making it resemble a digital sovereign bond—a low-yield, high-security asset backing the entire decentralized financial ecosystem. In 2026, Ethereum is proving itself not with stories, but with code. Its success hinges on this engineering pivot and ETH's ability to capture value from its new role as the foundational security layer.

marsbit03/18 05:50

From Playing with Narratives to Coding: With L2 Fees Dropping Below 1 Cent, How Will Ethereum Make Money Without 'Selling Gas'?

marsbit03/18 05:50

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