Google Titans architecture, helping AI have long-term memory
The Titans architecture and MIRAS framework enable AI models to efficiently manage long-term memory and adapt in real-time, enhancing their ability to process extensive contexts without offline retraining.
Nested Learning: A new ML paradigm for continual learning
Nested Learning introduces a paradigm that treats machine learning models as interconnected, multi-level optimization problems, effectively addressing catastrophic forgetting by allowing simultaneous optimization of various learning tasks.
Z-Image: Powerful and highly efficient image generation model with 6B parameters
Z-Image is a 6B parameter image generation model that includes Z-Image-Turbo, a distilled variant achieving sub-second inference with only 8 NFEs, making it highly efficient for photorealistic image generation and bilingual text rendering.
At least 50 hallucinated citations found in ICLR 2026 submissions
GPTZero identified over 50 hallucinations in ICLR 2026 submissions, revealing that these errors were overlooked by 3-5 peer reviewers, raising concerns about the integrity of the peer review process.
The unexpected effectiveness of one-shot decompilation with Claude
One-shot decompilation with Claude has accelerated progress on Snowboard Kids 2, achieving more in three weeks than in the previous three months, thanks to a workflow that minimizes human intervention and maximizes throughput.
Top ICLR 2026 Papers Found with fake Citations — Even Reviewers Missed Them
Fifty hallucinations were identified in ICLR 2026 submissions, revealing that even top-tier papers with high scores contained fake citations overlooked by multiple reviewers.
Touching the Elephant – TPUs
Google's Tensor Processing Unit (TPU) has evolved from a research project into a powerful hardware accelerator, achieving 42.5 Exaflops with the latest generation, Ironwood, which features 9,216 chips in a pod and a focus on specialized computations for neural networks.
Zebra-Llama: Towards Efficient Hybrid Models
Zebra-Llama introduces a scalable hybrid model architecture that combines State Space Models (SSMs) and Multi-head Latent Attention (MLA) layers, achieving Transformer-level accuracy with significantly reduced training requirements of only 7-11B tokens.
From DeepSeek V3 to V3.2
DeepSeek V3.2 introduces a sparse attention mechanism (DeepSeek Sparse Attention) that enhances efficiency in long-context scenarios, achieving competitive performance against proprietary models like GPT-5 and Gemini 3.0 Pro.
Research ARC Prize 2025 Results and Analysis
ARC Prize 2025 highlights the ongoing challenge of achieving AGI, with no Grand Prize winner yet, despite significant advancements in model refinement and open-source contributions from 1,455 teams and 90 papers submitted.
96.1M Rows of iNaturalist Research-Grade plant images (with species names)
96.1M rows of cleaned iNaturalist plant images, complete with species names and coordinates, are now available for testing vision models on real-world noisy data, addressing the challenges of using GBIF data.