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Models

GPT-5.6 sol pro disproves long-standing statistics conjecture in 90 minutes

A new iteration of GPT-5.6 solved a 20-year-old mathematical problem that previously stumped its predecessor. The model identified a fundamental flaw in a statistical method cited over 130,000 times within 90 minutes of processing.

Why it matters

This leap in reasoning capability suggests that enterprise teams can soon leverage AI for high-stakes validation of complex scientific frameworks. It demonstrates a shift from simple pattern matching to genuine structural analysis of established methodologies.

Key points

  • GPT-5.6 Sol Pro solved the conjecture in 90 minutes while GPT-5.5 failed after 20 hours.
  • The discovery challenges a statistical method widely used across thousands of academic publications.
  • The breakthrough highlights significant improvements in logical reasoning and automated error detection.

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OpenAI disbands preparedness team responsible for assessing catastrophic ai risks

OpenAI has reportedly dissolved its preparedness team, the group tasked with evaluating and mitigating potential catastrophic risks from advanced AI models. This internal restructuring follows several high profile departures from the company safety and alignment divisions.

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Deepseek raises V4 API pricing by up to eleven times ahead of reported IPO

DeepSeek has implemented a substantial price increase for its V4 API, with some costs rising by up to eleven times as of 16 August 2026. This shift signals the end of the low cost strategy the company utilised to disrupt the market in 2025. The adjustment coincides with industry reports suggesting the firm is preparing for an initial public offering.

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Writer releases enterprise-optimised GLM-5.2 variant with token-saving technology

Writer has launched a new variant of the open-source GLM-5.2 model, specifically post-trained to meet enterprise reliability standards. This release introduces a specialised harness designed to significantly reduce token consumption, directly addressing the high operational costs associated with large-scale model deployment.

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