Robust Filter Attention: Self-Attention as Precision-Weighted State Estimation

arXiv:2509.04154 · cs.LG, cs.AI · Submitted 2025-09-04 · Read on arXiv

cs.LG, cs.AI

Submitted: 2025-09-04

Updated: 2026-09-28

Code: https://github.com/PCR-git/Robust-Filter-Attention

Terminology

• Robust Filter Attention — 50× in this paper
• Transformer — 24× in this paper · explained in 2 episodes
• Aggregation — 19× in this paper · explained in 1 episode
• Perplexity — 18× in this paper · explained in 3 episodes
• Robustness — 16× in this paper · explained in 5 episodes
• Ablation — 14× in this paper · explained in 1 episode
• Embeddings — 10× in this paper · explained in 2 episodes
• Attention Mechanism — 9× in this paper · explained in 3 episodes
• Consistency — 9× in this paper · explained in 3 episodes
• Benchmark — 4× in this paper · explained in 2 episodes
• Differential Lyapunov Equation — 4× in this paper
• Stochastic Dynamics — 4× in this paper · explained in 2 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Covariance Propagation — 2× in this paper · explained in 2 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Iteratively Reweighted Least Squares — 2× in this paper
• Kalman Linear Attention — 2× in this paper
• Latent Space — 2× in this paper · explained in 7 episodes
• Positional Bias — 2× in this paper · explained in 2 episodes
• Transformer Architecture — 2× in this paper · explained in 2 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Bayesian Inference — 1× in this paper · explained in 5 episodes
• Core — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Gradient Flow — 1× in this paper · explained in 2 episodes
• Latent Dynamics — 1× in this paper · explained in 2 episodes
• State Space Models (SSMs) — 1× in this paper · explained in 4 episodes
• Structural Constraints — 1× in this paper · explained in 2 episodes

Sources

• Neural SDEs as a Unified Approach to Continuous-Domain Sequence Modeling
• Retentive Network: A Successor to Transformer for Large Language Models

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