u/ResearchCrafty1804

Microsoft confirms on publicly accessible web page that OpenAI has been using Looped Transformers in the GPT-6 series, proving The Information's reporting was correct all along.

GPT-6.1 Sol uses 2 inference passes, with a passing mention of "instead of three".

For those confused by "same base model weights as GPT-6 Sol", I think Microsoft meant 6 & 6.1 are both post-trained models on top of the same pre-trained "base model", not that the final weights are identical

So different post-training (+ one less loop).

Update: Microsoft updated the web page to remove it

Prime Agent is an open-source coding and research agent for general and long-running work.

A self-improving RLM harness for coding and long-running autonomous tasks.

Designed to be both token-efficient and expressive through programmatic tool calling, context as a variable, multi-agent messaging, and a self-modifiable harness state.

On ARC-AGI-3, it scores 95.5%, surpassing the human-expert baseline, but the gain is not benchmark-specific.

We see major improvements across models when compared to their proprietary harnesses.

Prime Agent is built on pi and fully open-source with an open license.

GitHub: https://github.com/PrimeIntellect-ai/prime-agent

Blog: https://www.primeintellect.ai/blog/prime-agent

X post: https://x.com/primeintellect/status/2085086999267144083?s=46

Andrej Karpathy, a prominent advocate for open-source AI and a co-founder of OpenAI, appears to have removed Anthropic from his X bio, suggesting he may have left the company.

Karpathy joined Anthropic only a few months ago, making the apparent departure somewhat surprising.

This is possibly related to Anthropic’s increasingly strong opposition to open-weight and open-source AI models. Of course, that’s just speculation, but the timing is interesting.

Introducing FlashQLA: high-performance linear attention kernels built on TileLang.

2–3× forward speedup. 2× backward speedup.

💻 Purpose-built for agentic AI on your personal devices.

Key insights:

1. Gate-driven automatic intra-card CP.

2. Hardware-friendly algebraic reformulation.

3. TileLang fused warp-specialized kernels.

FlashQLA boosts SM utilization via automatic intra-device CP. The gains are especially pronounced for TP setups, small models, and long-context workloads.

Instead of fusing the entire GDN flow into a single kernel, we split it into two kernels optimized for CP and backward efficiency. At large batch sizes this incurs extra memory I/O overhead vs. a fully fused approach, but it delivers better real-world performance on edge devices and long-context workloads.

The backward pass was the hardest part: we built a 16-stage warp-specialized pipeline under extremely tight on-chip memory constraints, ultimately achieving 2×+ kernel-level speedups.

We hope this is useful to the community!

Learn more:

📖 Blog: https://qwen.ai/blog?id=flashqla

💻 Code: https://github.com/QwenLM/FlashQLA