SurfaceMind and SurfaceDelta
Verified operational surfaces and bounded graph patches for small and large language models
Language models are often asked to produce complete answers, pages, reports, dashboards, or interfaces in one generation. This gives the model too much responsibility: it must select sources, preserve state, choose layout, generate all text, maintain internal consistency, and express the result in a renderable form. Small models suffer most, but large models also produce malformed structures, unsupported claims, and destructive rewrites.
SurfaceMind separates intelligence from rendering and authority. It compiles exact sources and governed state into a versioned Semantic Surface Graph. A model operates the graph through small, schema-bound SurfaceDelta patches. A deterministic validator checks base version, target existence, citation spans, text budgets, ordering, authority, and surface invariants before the renderer updates the view. The model never generates authoritative pixels and never owns truth.
The architecture externalizes working memory into a visible surface. A compact local model can classify, extract, rank, repair, or patch one region instead of regenerating an entire answer. The system preserves exact source spans, evidence, versions, omissions, and receipts. SurfaceMind's central empirical claim—matched 4B tasks perform better through SurfaceDelta than raw full-answer chat—remains a named go/no-go experiment, not a completed result.
Current evidence state
IMPLEMENTED
This label describes the strongest public-safe state of the specific paper claims. Open the paper's claim ledger for claim-level distinctions and limitations.
Publication
- Full paper:
/papers/p07-surfacemind-surfacedelta - Claim ledger:
content/papers/p07-surfacemind-surfacedelta/claim-ledger.json - Source manifest:
content/papers/p07-surfacemind-surfacedelta/source-manifest.json - Diagrams: 4
- Approximate body length: 1,769 words
Demonstrations
d06-surfacedelta-versus-prosed14-workpage-generator
Benchmarks
b05-surfacedelta-uplift
Public boundary
The research entry publishes a public-safe architecture and evidence record. It does not grant implementation rights or expose restricted claim-ready material.