Evaluate persistent cross-question interpretation retention

Evaluate whether the reflective natural-language-to-SPARQL process can preserve validated interpretation mappings across successive questions, sessions, and process revisions while maintaining monotone answer accuracy.

Background

The paper introduces a conditional conservative-retention theorem for a protected history of validated contextual mappings. The theorem requires identification assumptions, interface relations, totality, local simulation contracts, and terminal semantic compatibility.

The reported prompt-rewriting experiments do not implement or test these requirements. Consequently, persistent cross-question and cross-session retention, as well as monotone answer accuracy, remain unresolved empirical issues rather than demonstrated properties of the implemented system.

References

Theorem~\ref{thm:conservative-learning}'s retention guarantee holds for fixed, validated contexts under identifiability assumptions requiring independent certificates; persistent cross-question learning, cross-session retention, and monotone answer accuracy remain empirically unestablished.

— Reflective Compositional Grounding for Natural-Language-to-SPARQL  (2610.21796 - Mohr et al., 15 Oct 2026) in Section “Limitations”

The present experiments establish the effect of stage-attributed prompt rewriting, while certification of accumulated requirements remains a separate evaluation obligation.

— Reflective Compositional Grounding for Natural-Language-to-SPARQL  (2610.21796 - Mohr et al., 15 Oct 2026) in Section “Related Work,” paragraph “Reflective refinement and self-correction”; also stated in the Conclusion