The Key Handoff: Retrieval in Hybrid Language Models

arXiv:2609.32942 · cs.CL, cs.LG · Submitted 2026-09-26 · Read on arXiv

cs.CL, cs.LG

Submitted: 2026-09-26

Updated: 2026-09-26

Terminology

• Accuracy — 26× in this paper · explained in 1 episode
• Transformer — 13× in this paper · explained in 2 episodes
• Alignment — 8× in this paper · explained in 1 episode
• Interpretability — 6× in this paper · explained in 15 episodes
• Activation Patching — 4× in this paper · explained in 2 episodes
• Calibration — 4× in this paper · explained in 7 episodes
• Decode — 3× in this paper · explained in 2 episodes
• Hybrid Architectures — 2× in this paper · explained in 3 episodes
• Sparsity — 2× in this paper · explained in 4 episodes
• AI Alignment — 1× in this paper · explained in 2 episodes
• Abstraction — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Associative Memory — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Hybrid Models — 1× in this paper · explained in 2 episodes
• In-Context Learning — 1× in this paper · explained in 4 episodes
• Linear probe — 1× in this paper · explained in 2 episodes
• Mixture of Experts — 1× in this paper · explained in 2 episodes
• Multi-hop Reasoning — 1× in this paper · explained in 3 episodes
• Perplexity — 1× in this paper · explained in 3 episodes
• Quantization — 1× in this paper · explained in 12 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Transformer Models — 1× in this paper · explained in 2 episodes

Sources

• What Attention Recalls and Recurrence Controls in Hybrid Language Models
• Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality
• Localizing Model Behavior with Path Patching
• Does Circuit Analysis Interpretability Scale? Evidence from Multiple Choice Capabilities in Chinchilla
• NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning Model
• Nemotron-H: A Family of Accurate and Efficient Hybrid Mamba-Transformer Models
• An Empirical Study of Mamba-based Language Models

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