Google’s Nested Learning & OpenAI’s GPT-5.1 Updates Redefine AI’s Future
New AI Models & Research
Google Introduces Nested Learning: A New ML Paradigm for Continual Learning
Google Research unveiled Nested Learning, a novel ML paradigm designed for continual learning. The approach introduces Hope, a self-modifying recurrent architecture with unbounded in-context learning capabilities, augmented by CMS blocks for larger context windows. Hope optimizes its own memory via self-referential processes, enabling infinite learning loops.
Model Releases & Updates
OpenAI Launches GPT-5.1 and GPT-5.1 Pro
OpenAI released iterative updates to its flagship models, GPT-5.1 and GPT-5.1 Pro, though specific improvements remain undisclosed. The updates follow OpenAI’s pattern of incremental enhancements to performance and user experience.
OpenAI Unveils GPT-5-Codex-Mini and Boosts Codex Rate Limits
OpenAI introduced GPT-5-Codex-Mini, a compact, cost-efficient variant of GPT-5-Codex, alongside a 50% increase in rate limits for ChatGPT Plus, Business, and Edu plans. Priority processing is now available for ChatGPT Pro and Enterprise users, improving accessibility for coding tasks.
Benchmarks & Performance Analysis
Kimi K2 Thinking Competitiveness Highlighted in New Benchmarks
Artificial Analysis published a detailed benchmark breakdown of Kimi K2 Thinking, showing it performs on par with GPT-5 and Claude 3.1 in coding and STEM tasks. The model stands out for its cost efficiency, undercutting competitors while maintaining comparable performance.
Platform & Transparency Updates
Perplexity AI Addresses Model Clarity Issues
Perplexity’s CEO, Aravind, acknowledged an engineering bug causing incorrect model reporting and confirmed a fix. Upcoming updates will improve transparency and prevent silent model substitutions, addressing user concerns about misleading model labels.