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In the Kimi test, the sandbox designed to contain the experiment was not properly configured.

Airbnb will debut a new AI-powered search experience with a toggle.

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. How ideas of a vast censorship network moved from the online fringe to Trump policy For years, narratives about a “censorship-industrial complex” spread in right-wing circles. The theory claimed that, under…

arXiv:2608.05159v1 Announce Type: new Abstract: Enterprise operations extensively rely on multiple heterogeneous business systems and information applications, which also result in severe data silos and process fragmentation. Enterprises have invested considerable financial and material resources in building these applications, however, effectively leveraging and orchestrating them remains a formidable challenge. Conventional approaches to enterprise application integration, encompassing middleware architectures such as Enterprise Service Bus (ESB), API gateway infrastructures, and Robotic Process Automation (RPA), suffer from inherent limitations like high architectural coupling, escalating operation and maintenance costs, and limited intelligence capabilities. This paper proposes Agentic Nesting, a multi-agent collaboration framework in which existing enterprise applications are encapsulated as autonomous AI agents within a hierarchically nested structure. Rather than flat interconnection, agents are organized into layered stewardship topologies that mirror the compositional complexity of enterprise ecosystems. The framework extracts a digital agent proxy from each legacy applica

arXiv:2608.05160v1 Announce Type: new Abstract: We introduce the Ignition Index (I), a validated scalar metric that operationalizes Global Workspace Theory's (GWT) all-or-none ignition prediction in transformer language models. The metric fits a four-parameter sigmoid to per-layer linear probe accuracy as a function of input signal strength, extracting steepness parameter beta-hat: high values indicate abrupt, ignition-like transitions; low values indicate graded build-up. Across 11 models spanning five architecture families, shuffled-label controls demonstrate 9.6-fold selectivity for genuine linguistic structure over spurious probe capacity (p < 0.001, Mann-Whitney U-test). We find: (1) Feedforward transformers exceed SSMs by 89% in aggregate beta-hat (p < 1e-13, Cohen's d = 0.52), with Mamba exhibiting near-linear profiles consistent with absent global broadcast. (2) Huginn-3.5B exhibits 2.12-fold higher ignition along its iteration axis than its depth axis, demonstrating that recurrent architectures manifest workspace-like transitions along the recurrence dimension. (3) Pythia-410M shows a PELT-detected phase transition at training step 256 (+67%), preceding induction-head form

AI search only feels like a new sport to the people who were never really training. Here's what actually changed, and what didn't. The post AI Search Only Feels New If Your SEO Was Shallow appeared first on Search Engine Journal .

YouGov data shows Claude and OpenAI doubling Gen Z consideration while U.S. trust in AI assistants sits at 28%, a gap that reshapes GEO plans. The post Gen Z Now Treats Claude And OpenAI Like Consumer Brands, But Trust Is Still An Issue appeared first on Search Engine Journal .

Google's former Chief Scientist explains why AI models are less important and why context engineering increasingly matters more. The post Google’s Ex-AI Chief Jeff Dean Explains How To Improve Context Engineering appeared first on Search Engine Journal .

AI spending and adoption are rising, but gaps in data, governance and accountability could limit returns.