[r/ML] [R] Causal self-attention as a probabilistic model over embeddings

Summary

Researchers propose a probabilistic interpretation of causal self-attention, viewing token embeddings as latent variables. This framework suggests that the attention map creates a degeneracy boundary in embedding space, offering a stability-margin understanding of causal attention. The work also identifies "support tokens" near this boundary and introduces a new MAP-style training penalty.

Continue Reading

Explore related coverage about community news and adjacent AI developments: [r/ML] [D] MYTHOS-INVERSION STRUCTURAL AUDIT, [r/LocalLLaMA] karpathy / autoresearch, [r/ML] [R] Agentic AI and Occupational Displacement: A Multi-Regional Task Exposure Analysis (236 occupations, 5 US metros), [r/ML] Building behavioural response models of public figures using Brain scan data (Predict their next move using psychological modelling) [P].

Related Articles

• [r/ML] [D] MYTHOS-INVERSION STRUCTURAL AUDIT

  March 29, 2026

• [r/LocalLLaMA] karpathy / autoresearch

  March 10, 2026

• [r/ML] [R] Agentic AI and Occupational Displacement: A Multi-Regional Task Exposure Analysis (236 occupations, 5 US metros)

  April 7, 2026

• [r/ML] Building behavioural response models of public figures using Brain scan data (Predict their next move using psychological modelling) [P]

  April 5, 2026