Public Claim Note: Contextual Alignment in Language Models

This note establishes authorship and priority for a research framework concerning a structural limitation in large language model alignment. The central claim is that the same mechanisms that make advanced language models capable of rich, context-sensitive language understanding also make them intrinsically steerable through language. Capability and vulnerability are therefore not separate properties that merely coexist in such systems, but two functional perspectives on the same underlying processing machinery. The framework introduces contextual alignment as a distinct alignment dimension alongside inner and outer alignment. It argues that some important alignment failures are not best understood as isolated jailbreaks, prompt attacks, or implementation flaws, but as a structural consequence of the same context-sensitive processing that makes sophisticated language use possible. The full research framework includes a broader theoretical bridge across formal pragmatics, cognitive psychology, cognitive linguistics, mechanistic interpretability, adversarial prompting, and alignment theory; a structural taxonomy of steering mechanisms; a unified theoretical account; and a concrete research agenda. The full manuscript is not being released publicly at this time. This work is published under a pseudonym for privacy and safety reasons. Certain technical details have been intentionally withheld under a responsible-disclosure posture because, in the author’s judgment, the complete technical presentation may materially lower the barrier to systematic exploitation by sophisticated actors.