When to Think Fast and Slow? AMOR: Adaptive Entropy Gate for Hybrid Models

arXiv:2602.13215 · cs.AI · Submitted 2026-01-22 · Read on arXiv

cs.AI

Submitted: 2026-01-22

Updated: 2026-09-25

Code: https://github.com/HaoranZhengRaul/AMOR

Terminology

• Transformer — 32× in this paper · explained in 2 episodes
• Decode — 26× in this paper · explained in 2 episodes
• Benchmark — 15× in this paper · explained in 2 episodes
• Perplexity — 13× in this paper · explained in 3 episodes
• Hybrid Architectures — 8× in this paper · explained in 3 episodes
• Ablation — 7× in this paper · explained in 1 episode
• Hybrid Models — 7× in this paper · explained in 2 episodes
• Sparse Attention — 7× in this paper · explained in 5 episodes
• In International Conference on Learning Representations — 5× in this paper
• Distribution Shift — 4× in this paper · explained in 5 episodes
• Meeting of the Association for Computational Linguistics — 4× in this paper
• Robustness — 4× in this paper · explained in 5 episodes
• Accuracy — 3× in this paper · explained in 1 episode
• KV cache — 3× in this paper · explained in 3 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Adaptive Metacognitive Output Router — 2× in this paper
• Adaptive Metacognitive Output Router — 2× in this paper
• Attention Mechanism — 2× in this paper · explained in 2 episodes
• Benchmarking — 2× in this paper · explained in 3 episodes
• Calibration — 2× in this paper · explained in 7 episodes
• Conference on Language Modeling — 2× in this paper
• Gradient Flow — 2× in this paper · explained in 2 episodes
• In International Conference on Machine Learning — 2× in this paper
• Agentic Reasoning — 1× in this paper · explained in 1 episode
• Empirical Methods in Natural Language Processing — 1× in this paper
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• In-Context Learning — 1× in this paper · explained in 4 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes
• Simple linear attention language models balance the recall-throughput tradeoff
• Just read twice: closing the recall gap for recurrent language models
• Hybrid Architectures for Language Models: Systematic Analysis and Design Insights
• LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding
• Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation
• Lessons from the Trenches on Reproducible Evaluation of Language Models
• PIQA: Reasoning about Physical Commonsense in Natural Language
• Language Models are Few-Shot Learners
• Nemotron 3 Super: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning
• Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge
• Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality
• A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers
• Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models
• DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models
• Hymba: A Hybrid-head Architecture for Small Language Models
• DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs
• Multi-News: a Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model
• SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization
• The Zamba2 Suite: Technical Report

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