arXiv:2604.00694v1 Announce Type: cross
Abstract: Autonomous agents increasingly interact with the web, yet most websites remain designed for human browsers — a fundamental mismatch that the emerging “Agentic Web” must resolve. Agents must repeatedly browse pages, inspect DOMs, and reverse-engineer callable routes — a process that is slow, brittle, and redundantly repeated across agents. We observe that every modern website already exposes internal APIs (sometimes called emphshadow APIs) behind its user interface — first-party endpoints that power the site’s own functionality. We present Unbrowse, a shared route graph that transforms browser-based route discovery into a collectively maintained index of these callable first-party interfaces. The system passively learns routes from real browsing traffic and serves cached routes via direct API calls. In a single-host live-web benchmark of equivalent information-retrieval tasks across 94 domains, fully warmed cached execution averaged 950,ms versus 3,404,ms for Playwright browser automation (3.6$times$ mean speedup, 5.4$times$ median), with well-cached routes completing in under 100,ms. A three-path execution model — local cache, shared graph, or browser fallback — ensures the system is voluntary and self-correcting. A three-tier micropayment model via the x402 protocol charges per-query search fees for graph lookups (Tier~3), a one-time install fee for discovery documentation (Tier~1), and optional per-execution fees for site owners who opt in (Tier~2). All tiers are grounded in a necessary condition for rational adoption: an agent uses the shared graph only when the total fee is lower than the expected cost of browser rediscovery.
Assessing nurses’ attitudes toward artificial intelligence in Kazakhstan: psychometric validation of a nine-item scale
BackgroundArtificial intelligence (AI) is increasingly integrated into healthcare, yet the attitudes and knowledge of nurses, who are the key mediators of AI implementation, remain underexplored.


