Titans Test-Time Memory: Why Google’s Architecture Matters Beyond Bigger Context Windows
Titans test-time memory adds a learnable long-term memory to AI models. Here’s how it works, what it changes, and what builders should watch.
15 articles on large language models.
Titans test-time memory adds a learnable long-term memory to AI models. Here’s how it works, what it changes, and what builders should watch.
The Free Transformer adds learned latent variables to decoder LLMs. Here’s how it works, what benchmarks show, and why builders should care.
LLM context rot explains why larger prompts can reduce AI accuracy—and how retrieval, compression, and context engineering improve reliability.
Claude 3.5 Haiku interpretability research reveals how AI uses parallel circuits for math, diagnosis, hallucinations, and refusals.
Kimi K3 review: Moonshot’s 2.8T model brings frontier-style coding, vision, and 1M context—but creators should test the real trade-offs.
Qwen3.8-Max Preview improves frontend generation and tool reliability. Learn what changed, how to test it, and what builders should verify.
Attention residuals turn Transformer depth into a retrieval system. See how Kimi’s Block AttnRes improves efficiency, reasoning, and what remains unproven.
DeepSeek V4 infrastructure combines compressed attention, MoE kernels, and resilient agent training to make million-token AI more practical.
Xiaomi MiMo V2.5 Pro shows how efficient models, open weights, and agent tooling turned Xiaomi into a serious AI challenger.
DeepSeek V4 brings million-token context and radically lower API prices. See what CSA, HCA, and its architecture mean for AI builders.
Looped transformers reuse layers to reason in latent space. Here’s why they matter, where they outperform CoT, and what still limits them.
Natural language autoencoders turn AI activations into text, giving builders a practical new way to audit model behavior and safety.
NVIDIA Nemotron 3 Ultra pairs 1M-token context with open checkpoints and fast agent inference. Here’s where it fits—and where it doesn’t.
The GLM-5.2 open-weights model brings long-horizon coding, a 1M-token context and MIT-licensed weights closer to practical AI ownership.
LLM character counting reveals how Claude 3.5 Haiku tracks line length through curved internal representations—and why it matters for AI safety.