docs: align legacy positioning with project source (#158)

Author: ProfRandom92

AGENTS.md

@@ -1,7 +1,7 @@
-# CompTextv7 Agent Instructions
+# CompText V7 Agent Instructions
 
 ## Project focus
-- CompTextv7 is a deterministic operational replay-validation research prototype.
+- CompText V7 is the deterministic replay-integrity layer for compressed operational agent traces.
 - Current focus: core foundation, deterministic replay artifacts, CI artifacts, docs, and conservative positioning.
 - Showcase work is paused unless explicitly requested.
 - Chilli/Hatch/Pet assets must not be touched unless explicitly requested.

docs/API_SURFACE.md

@@ -7,9 +7,11 @@ surfaces that future benchmark/regression summaries can reference. It also
 records integration expectations for sanitized outputs from
 `ProfRandom92/Comptext-Daimler-Experiment-` without introducing runtime coupling.
 
-Comptextv7 should remain the main runtime/dashboard/API repository. Experiment
-outputs should influence review decisions through lightweight Markdown, JSON, or
-CSV summaries only.
+CompText V7 should treat API, dashboard, export, and report surfaces as
+deterministic review and evidence handoff surfaces, not as runtime
+orchestration, live monitoring, or platform expansion. Experiment outputs
+should influence review decisions through lightweight Markdown, JSON, or CSV
+summaries only.
 
 
 ## Agent workflow guardrails

docs/research_positioning.md

@@ -1,6 +1,6 @@
-# CompTextv7 Research Positioning
+# CompText V7 Research Positioning
 
-CompTextv7 is a deterministic operational replay-validation and state-survivability prototype. It asks whether compact, replay-safe operational state can preserve the fixture-defined evidence, constraints, blockers, dependencies, recovery paths, and tool-order signals needed to review continuation after compression and reconstruction. The project is complementary to learned context-compression research, RAG evaluation, vector-memory systems, serving-layer cache optimization, and durable workflow infrastructure, but it does not replace those systems or claim solved AI memory.
+CompText V7 is the deterministic replay-integrity layer for compressed operational agent traces. It asks whether compact or reconstructed operational state can preserve the evidence, constraints, blockers, dependencies, recovery paths, capability boundaries, and tool-order signals needed to replay a safe operational trajectory after compression. The project is complementary to learned context-compression research, RAG evaluation, vector-memory systems, serving-layer cache optimization, and durable workflow infrastructure, but it does not replace those systems or claim solved AI memory.
 
 ## What CompTextv7 measures