Every attention head learns three weight matrices for query, key, and value. A 2026 study trained models up to 1.2B parameters to ask which of them are actually load-bearing, and found that keys and values can share one projectio…
Pure Mamba was supposed to replace attention. Instead the most efficient open models in 2025 are roughly seven-eighths Mamba and one-eighth attention. The reason is the KV cache, and what each layer can and cannot remember.
Every frontier LLM released since 2023 uses some form of Mixture of Experts. The idea is deceptively simple: build a model with hundreds of billions of parameters but activate only a fraction for each token. The result is dense-m…
A single agent burns about 4x the tokens of a chat. A multi-agent system burns about 15x. The whole question of orchestration is whether the extra agents buy you accuracy worth more than that bill, and four named patterns decide …
Most teams reach for fine-tuning when they should reach for retrieval. The cost is not just dollars - it is months of confused engineers trying to figure out why the model started lying again.
Most long-context models compress the past into a fixed-size vector and hope nothing important got squeezed out. Titans instead gives the model a small neural network as its memory, and lets that network keep training on the data…
The industry frames every LLM feature as an agent. Most production systems that work are pipelines with one or two LLM steps and clear handoffs. Default to a workflow, reach for an agent only when the problem demands it.