OrthoFormer: Instrumental Variable Estimation in Transformer Hidden States via Neural Control Functions
Transformer architectures excel at sequential modeling yet remain fundamentally limited by correlational learning - they capture spurious associations induced by latent confounders rather than invariant causal mechanisms. We identify this as an epistemological challenge: standard Transformers conflate static background factors (intrinsic identity, style, context) with dynamic causal flows (state evolution, mechanism), leading to catastrophic out-of-distribution failure. We propose OrthoFormer, a causally grounded architecture that embeds instrumental variable estimation directly into Transformer blocks via neural control functions. Our framework rests on four theoretical pillars: Structur...
| Authors | Charles Luo |
|---|---|
| Primary category | cs.LG |
| Published | 8 March 2026 |
| Last updated at source | 15 March 2026 |
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