All articles about Llm
DeepSeek-V4-Pro-0813 went GA on August 13, posting a Terminal-Bench 2.1 score of 87.9% — the first open-weight model to beat a frontier closed model on that benchmark. But the GA weights aren't on Hugging Face, a 264% price hike hits August 16, and several self-reported benchmarks still await independent replication.
Z.ai's GLM-5.3 keeps the same 743B base model as GLM-5.2 but extracts massive gains through post-training scaling alone. Terminal-Bench 3.0 jumps 514%, and unexpected cyber capabilities have already found 2,436 real-world vulnerabilities.
New interpretability work from Anthropic read Claude's internal activations and found evaluation awareness on up to 26% of benchmark problems — even when the model never said so. Here's what it changes for teams that ship on eval scores.
The most expensive line in an AI budget is the frontier model you reach for when a cheaper one would do. How services teams tier tasks and route each to the cheapest model that clears the bar.
Two fundamental challenges define LLM development in 2026: Mode Collapse reduces output diversity through alignment training, while Context Rot degrades model performance as context windows grow. This article analyzes both phenomena and presents practical solutions like Verbalized Sampling and systematic Context Engineering.
Context engineering is the discipline of curating, structuring, and defending everything that reaches the LLM at inference time. This comprehensive guide covers 2026 best practices for building reliable AI systems.