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OpenAI is launching a preview of a sped up version of its latest, most powerful model, in an effort to court enterprise users.

IBM plans to train and certify tens of thousands of consultants on OpenAI's technologies as part of this deal.

When we set out to talk to kids about artificial intelligence, we thought we knew what we’d hear. We expected some to tell us they were using it to cheat a little, the way Millennials and Gen Xers opened up CliffsNotes or programmed formulas into their TI-82s, and others to share inspiring ways they were…

arXiv:2608.11207v1 Announce Type: new Abstract: When two LLM agents with structurally opposed objectives interact across multiple turns, the absence of a shared goal function produces not competition but collapse: the visitor capitulates, the site agent stops varying its approach, and the conversation terminates without achieving either agent's stated objective. This paper asks whether a control-theoretic governance layer can substitute for that missing goal function. The Experience Orchestrator (EO) addresses this in a simulated financial services environment where a site agent guides a visitor toward advisor contact while the visitor maintains psychologically realistic resistance. EO governs the joint trajectory through three mechanisms: a Contextual Bandit (CB) that selects content arms calibrated from real-world web analytics, a PID controller that enforces behavioral consistency via dynamic schema constraints, and a POMDP belief tracker that maintains a probabilistic model of visitor intent. Across 60,000 simulations, EO achieves a +32 percentage point lift in high-intent advisor contact rate (78.1% vs. 46.1% over a naive LLM control), with CB variant selection accounting for

arXiv:2608.11210v1 Announce Type: new Abstract: Bayesian calibration of process-based models requires a prior distribution for each model parameter. Despite decades of methodological work, researchers almost always fall back on uniform priors. The main reason is that building informative priors from scientific literature is slow and needs both domain and statistical expertise. We present \textbf{Distribird}, an agentic web application that automates this process. Given a parameter name, physical description, and domain context, Distribird deploys a multi-agent pipeline that searches the literature, extracts and weights reported values by domain relevance, and fits a probability distribution via AIC model selection. When no literature is available, the system falls back to sensible uninformative alternatives, and clearly reports both the evidence behind and the confidence level of every prior it produces. It is designed for the problems where the models have physically interpretable parameters, where domain knowledge exists in the published literature. We evaluate the tool on 24~parameters across 10 scientific domains comparing three open-weight models (Qwen3.6 27B, Gemma 4 31B, Mis

Practitioners value AI visibility data highly and won't fund the platforms selling it. New survey data on what's actually blocking the category. The post The GEO Trust Gap: SEOs Want The Data, But Not The Platforms Selling It appeared first on Search Engine Journal .

Anthropic has revealed enough about Claude's watermark to identify how it might work. A new research paper may be the clue. The post Why Anthropic’s Claude Watermark May Be A New Text-Marking Method appeared first on Search Engine Journal .

Stable rankings, stable impressions, falling CTR: the signature of AI Overview click loss. Learn to spot it and decide what's worth recovering. The post How Do I Know If AI Overviews Are Taking Clicks From My Site And What Can I Do About It? – Ask An SEO appeared first on Search Engine Journal .

Sees OpenAI partnership as key to accelerating adoption.

Says AI use isn't materially affecting its technology budget.

For a range of attack and reconnaissance purposes.