Persistent Operator-Curated Context as Subspace Prior Modulator in LLM Agent Output
Year: 2026
Type: article
Abstract: Persistent operator-curated context functions as a domain-conditional subspace prior modulator in frontier LLM agent output — a pattern confirmed across six empirical datums spanning two experimental series. A 36× task-completion ratio on a closed orthographic task (Datum-001) established the effect magnitude under strong prior anchoring. Open generative tasks (Datum-002, four-agent design study) revealed subspace splitting: operator-curated invariants present at 100% in the modulated condition and 0% in native, while native agents spontaneously generated first-principles invariants absent from the modulated output. Three domain-foreign confirmatory tasks (Datums 003–005, 24 trials total) consistently produced the inverse pattern: within-native similarity exceeded within-modulated at all three task domains (S < X < N), reflecting prior compression of non-anchored subspaces. The critical test — a domain-resident task requiring the operator's most deeply embedded structural primitive (signal-separation design, Datum-006, 8 trials + operationality control) — produced the predicted reversal (S = 0.7595 > X = 0.7542 > N = 0.7364), with the advantage non-replicable by concept-level instruction injection (mean gamma cosine = 0.7010, 0.053 below the cross-condition threshold). The domain-conditionality was predicted under the subspace-conditional formulation before Datums 003–006 were run; the prediction held across all four task types. A pre-registered multi-task falsifier was not triggered. The subspace prior modulator is not a uniform amplifier — it is a structural alignment between operator-curated operational experience and task-required domain primitives.
Author: Wender, Arnold
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7159778393