Attention as Conditioning: What Classical Learning Theory Predicts About Linear Transformers

arXiv:2508.08289 · cs.LG, cs.AI, q-bio.NC · Submitted 2025-08-05 · Read on arXiv

cs.LG, cs.AI, q-bio.NC

Submitted: 2025-08-05

Updated: 2026-08-28

Terminology

• Transformer — 49× in this paper · explained in 2 episodes
• Associative Memory — 10× in this paper · explained in 2 episodes
• Core — 9× in this paper · explained in 2 episodes
• In-Context Learning — 8× in this paper · explained in 4 episodes
• Alignment — 7× in this paper · explained in 1 episode
• Redundancy — 7× in this paper · explained in 2 episodes
• Attention Mechanism — 6× in this paper · explained in 3 episodes
• Adaptation — 5× in this paper · explained in 1 episode
• Cosine Similarity — 5× in this paper · explained in 3 episodes
• Generalization — 5× in this paper · explained in 13 episodes
• Transformer Architecture — 5× in this paper · explained in 2 episodes
• Fidelity — 4× in this paper · explained in 2 episodes
• Activation Function — 3× in this paper · explained in 1 episode
• Adam — 3× in this paper · explained in 2 episodes
• Artificial Intelligence (AI) — 3× in this paper · explained in 2 episodes
• Interpretability — 3× in this paper · explained in 15 episodes
• AI Alignment — 2× in this paper · explained in 2 episodes
• Concept Drift — 2× in this paper · explained in 4 episodes
• Mechanistic interpretability — 2× in this paper · explained in 2 episodes
• Reinforcement Learning — 2× in this paper · explained in 4 episodes
• Analyzer — 1× in this paper · explained in 1 episode
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Online Adaptation — 1× in this paper · explained in 3 episodes
• Signal-to-noise ratio (SNR) — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks