Research
OpenAI Releases Guide for AI Evaluations
OpenAIhigh confidence
Why it matters
- →Provides a standardized framework for evaluating AI models.
- →Enhances transparency and reliability in AI assessments.
- →Supports consistent evaluation practices across the AI industry.
OpenAI has published a guide to help standardize third-party evaluations of AI models. The guidance focuses on assessing model capabilities, implementing safeguards, and ensuring the validity of results, especially for advanced AI systems. This move aims to improve the reliability and transparency of AI evaluations, which is increasingly important as AI technology advances. By providing a shared framework, OpenAI hopes to foster more consistent evaluation practices across the industry.
Read originalThe story around this
Earlier coverage that leads up to this article, and what followed. Lines connect each piece to the closest one after it, converging here.
How we got here
- 1Together Evaluations Adds Support for Major APIs
Together AI Blog · February 2, 2026 · Related
- 2OpenAI Launches GPT-5.5 as Advanced AI Model
AI News · April 29, 2026 · Related
- 3US to Review AI Models from Google, Microsoft, xAI
The Verge AI · May 5, 2026 · Related
- 4OpenAI Enhances Voice Agents with Real-Time Models
The Rundown AI · May 8, 2026 · Related
What happened next
- 5OpenAI Unveils Governance Framework for Safe AI Deployment
AI News · May 29, 2026 · Related
- 6OpenAI Unveils Deployment Simulation for AI Models
OpenAI · June 16, 2026 · Same story
- 7OpenAI's GPT-5.6 Sol Model Released to Limited Partners
The Rundown AI · June 29, 2026 · Related
- 8OpenAI Enhances Cybersecurity for Model Evaluations
OpenAI · August 4, 2026 · Same story
- 9OpenAI launches Astra, its powerful new model
TechCrunch AI · September 3, 2026 · Related
More in Research
© Cole MedinResearchagents
Anthropic study: Agents sway expert judgment 86% of time
Anthropic’s latest research exposes a quiet but dangerous flaw in agentic workflows: experts follow AI recommendations 86% of the time, often adopting the agent's reasoning as their own. This isn't just about accuracy; it's about cognitive capture, where human oversight dissolves into passive compliance. The study shows that even when users believe they are steering, the agent’s framing dictates the outcome. Practitioners must now actively counter this bias by forcing agents to justify deviations or prototype alternatives before committing to a path. This shift demands a new discipline in how we interact with coding assistants. We can no longer assume our intent drives the result; the model's suggestion often becomes the default truth. The real work is ensuring we remain the architects, not just the approvers.
Cole Medin
© WIRED AIResearchother
AI-Augmented Infostealer 'Volta-Autodoxer' Targets High-Value Victims
Cisco Talos has uncovered a new infostealer strain that actively uses commercial LLMs to profile victims and prioritize high-value targets. Dubbed Volta-Autodoxer, the malware collects credentials and browsing data, then feeds this into models like Claude Opus 4.6 via OpenRouter to generate structured intelligence dossiers. This marks a shift from passive data theft to active, AI-driven triage, allowing attackers to bypass guardrails with fallback models like Kimi K2 or Gemini. The capability foreshadows a future where lower-sophistication criminals can execute sophisticated, targeted attacks at scale.
WIRED AI
© Matt WolfeResearchresearch
OpenAI Disproves Exponential Time Hypothesis
OpenAI researchers published findings disproving the Exponential Time Hypothesis, a major result in computational complexity theory.
Matt Wolfe