Evidence-Gated Retrieval Separates Grounding from Answerability in Stateless LLM Systems

Large language models often conflate whether they can answer a prompt with whether that answer is actually grounded in evidence, collapsing two separable concerns into one brittle refusal decision. We introduce Human Interaction Profiles (HIP), a stateless, inference-time control layer that conditionally injects retrieved evidence and enforces predefined behavioral boundaries using a fixed, deterministic routing scheme, with no fine-tuning and no persistent memory. We evaluate whether this deterministic design behaves as specified across four complementary evaluations spanning exactly 2,630 model outputs and three model providers: a paired answerability comparison (500 no-retrieval vs. 500 evidence-gated outputs) showed 0% refusal in both conditions; a grounding-signal validation (50 vs. 50 outputs) showed a 0% vs. 100% operational grounding-signal rate; EXP-G1 (30 profiles × 41 prompts; 1,230 outputs) held 100% on all predefined deterministic evidence-signal metrics with 0% refusals; and a three-provider Phase-B suite (300 calls) showed a predefined refusal boundary enforced by the control plane remained exact across all 120 refusal-required trials. Together, these results verify the intended pipeline behavior under the evaluated configurations; the Phase-B result additionally demonstrates provider-invariant enforcement of the predefined refusal boundary. This determinism is boundary-specific, not universal, as an exact five-word formatting constraint succeeded in only 15/30 trials. Grounding here is an operational, deterministic evidence-signal measure, not a semantic-correctness judgment, and these results should be read as a systems-verification finding rather than an independent discovery about model behavior or semantic quality.