Continuous optimization of schema feedback and evolution

Investigate how to efficiently utilize human feedback and external query-feedback signals for continuous optimization of the AnnoIndex schema-induction and document-analysis process, while balancing schema stability against the long-term cost of schema evolution.

Background

AnnoIndex decomposes schema induction and document analysis into multiple feedback loops, including an internal automatic verification loop, an error-case analysis loop, and an external feedback loop. The paper implements the internal verification loop and provides interfaces for human feedback and query-feedback signals such as error-case logs and query-log analysis.

The unresolved issue is how to turn these external signals into an effective continuous optimization mechanism without causing excessive schema changes. The system must therefore address both the operational use of feedback and the trade-off between maintaining a stable schema and paying the cost of repeatedly evolving it over the long term.

References

How to efficiently utilize these external signals for continuous optimization, and how to balance schema stability with evolution cost over the long term, are open directions for future exploration.

— Structure then Query: Enabling Precise Analytical Queries over Unstructured Documents  (2608.13384 - Lin et al., 13 Aug 2026) in Section “SchemaLoop: Progressive schema induction,” subsection “Multi-Loop Engineering and External Feedback Integration”

(i)~Schema dependence. The DSL assumes a predefined schema; extending it to schema-free or evolving corpora (e.g., via automatic schema discovery) is left to future work.

— ProRetrieval: Learning to Orchestrate Hybrid Search via Executable Program Synthesis  (2608.27017 - You et al., 27 Aug 2026) in Limitations, item (i) Schema dependence