Fixed-Point Reasoners: Stable and Adaptive Deep Looped Transformers

Fine-tuning with gpt-oss and Hugging Face Transformers

New Method Enables Constant-Cost Self-Attention Computation for Transformers

Researchers present a novel mathematical approach to compute self-attention in Transformer AI models with constant cost per token, rather th

Study Reveals How RL and SFT Differently Teach Transformers Chain-of-Thought Reasoning on Sparse Boolean Functions

This research paper analyzes how transformers learn Chain-of-Thought (CoT) reasoning capabilities through Reinforcement Learning (RL) with p

Ouro: Looped Language Models That Build Reasoning into Pre-Training Through Latent Space Iteration

Researchers introduce Ouro, a family of pre-trained Looped Language Models (LoopLM) that build reasoning capabilities directly into the pre-

Universal Reasoning Model (URM): Enhancing Transformer Performance for Complex AI Reasoning Tasks

This research paper analyzes Universal Transformers (UTs) used for complex reasoning tasks like ARC-AGI and Sudoku, finding that performance

Examining the Limitations of Transformer Models and the Gap to Human-Level AI

The article presents a skeptical perspective on claims about imminent Artificial General Intelligence (AGI), arguing that current transforme