Liquid AI and Liquid Foundation Models Quiz

Answers and explanations appear as you take the quiz.

1. What does “LFM” stand for in the LFM2 technical report?
   • Liquid Foundation Model
   • Latent Feature Mapper
   • Long-Form Memory
   • Linear Forecasting Module
2. According to the LFM2 technical report, what architecture did its hybrid backbone combine?
   • Only a dense layer with no sequence processing
   • Gated short convolutions with a small number of grouped-query attention blocks
   • A vision-only encoder and a decision tree
   • A single unchanging recurrent state with no attention
3. What is the LFM2-8.3B mixture-of-experts model’s active parameter count?
   • All 8.3 billion parameters are active for every token
   • About 350 million total parameters
   • About 1.5 billion active parameters
   • The report gives no active parameter count
4. What context length does the LFM2 report list for the model family?
   • 2K tokens only
   • 128K tokens for every variant
   • The report lists only audio duration
   • 32K tokens
5. What deployment setting does the LFM2 report explicitly optimize its architecture for?
   • On-device inference under latency, memory, and energy constraints, including phone- and laptop-class hardware
   • Guaranteed private operation on every car and phone
   • Cloud-only training on large GPU clusters
   • A fixed constant-memory budget independent of context and model size
6. What CPU speed comparison did the November 2025 LFM2 report make against similarly sized models?
   • Exactly 2× higher accuracy on every benchmark
   • Up to 2× faster prefill and decode
   • A fixed 32× reduction in memory for every device
   • A guaranteed 2× improvement over every Transformer workload
7. What constraint guided the hardware-in-the-loop architecture search described in the LFM2 report?
   • The need to maximize image resolution only
   • A requirement to train exclusively on one dataset
   • Edge-device latency and memory limits
   • The number of labels available for medical diagnosis
8. What is LFM2-ColBERT designed to provide?
   • An audio-only speech synthesizer
   • A general-purpose image decoder
   • A controller for training liquid time-constant networks
   • A low-latency encoder for queries and documents used in retrieval