Another Blueprint In The Wall: How to Ask Frontier AI Like a Kid?

arXiv:2609.14803 · cs.AI · Submitted 2026-09-13 · Read on arXiv

cs.AI

Submitted: 2026-09-13

Updated: 2026-09-13

Comments: https://github.com/akhadangi/blueprint-mirage

Code: https://github.com/akhadangi/blueprint-mirage

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Core — 38× in this paper · explained in 2 episodes
• Verification — 33× in this paper · explained in 2 episodes
• Provenance — 30× in this paper · explained in 4 episodes
• World Model — 19× in this paper · explained in 7 episodes
• Transformer — 17× in this paper · explained in 2 episodes
• Alignment — 11× in this paper · explained in 1 episode
• Consistency — 7× in this paper · explained in 3 episodes
• Interpretability — 7× in this paper · explained in 15 episodes
• Calibration — 6× in this paper · explained in 7 episodes
• Verifier — 6× in this paper · explained in 2 episodes
• Embeddings — 5× in this paper · explained in 2 episodes
• Decode — 4× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Introspection — 3× in this paper · explained in 2 episodes
• Sycophancy — 3× in this paper · explained in 4 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Grounding — 2× in this paper · explained in 3 episodes
• Hallucination — 2× in this paper · explained in 4 episodes
• Latent Reasoning — 2× in this paper · explained in 2 episodes
• Sparse Attention — 2× in this paper · explained in 5 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Chain-of-Thought Prompting — 1× in this paper · explained in 2 episodes
• Epistemic Uncertainty — 1× in this paper · explained in 10 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Information Gain — 1× in this paper · explained in 2 episodes
• KV cache — 1× in this paper · explained in 3 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Mechanistic interpretability — 1× in this paper · explained in 2 episodes
• Mixture of Experts — 1× in this paper · explained in 2 episodes
• Multimodal Reasoning — 1× in this paper · explained in 5 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• RAG — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Scaffolding — 1× in this paper · explained in 3 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• Mixture-of-Depths: Dynamically allocating compute in transformer-based language models
• Language Models Fail to Introspect About Their Knowledge of Language
• Jamba: A Hybrid Transformer-Mamba Language Model
• Adaptive Computation Time for Recurrent Neural Networks
• Language Models (Mostly) Know What They Know
• Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet

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