docs(model-grounded-detection): the arbiter is a program model, not a sandbox

Maintainer correction: execution isn't possible for all SAST targets, and the
bugs this project owns -- IDOR, missing authz, injection -- don't crash
anything. Execution is the easy arbiter only for memory-safety-in-C, which is
not our world.

What execution provided was an independent, deterministic arbiter of whether a
claim is real -- not the same LLM that made it, not a noisy vote. That arbiter
can be built statically, from the code, as a program model the claim is checked
against. LLM proposes; the model disposes. Same architecture, same repair of
the root cause, no runnable target required.

The evidence ladder becomes static and deterministic:
  suspicion -> model_corroborated (claim consistent with resolved dataflow /
  a guard its siblings carry is absent) -> model_entailed (the model alone
  establishes it) -> execution_confirmed (deferred tier, C memory-safety only).

model_corroborated and above are deterministic -- run the checker twice, same
verdict -- which removes the run-to-run noise the same way a crash would, with
no K-runs and no target to build. Our IR candidate enumerator (96.6% ceiling),
binding chains and obligation checker stop being prompt context and become the
arbiter itself.

The course: promote those assets from inputs to arbiter, delete the noise
layer, make the LLM a proposer the model audits, keep the corpus as the
model's calibration set (the fix diff is what the model should see), and defer
execution to Clearwing for the memory-safety classes only. Build the arbiter we
can build from code; adopt the one we cannot, when a class demands it.

Co-Authored-By: Claude Opus 4.8 <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG

.kiro/specs/model-grounded-detection/spec.json

@@ -0,0 +1,28 @@
+{
+  "feature_name": "model-grounded-detection",
+  "created_at": "2026-09-08T00:00:00Z",
+  "updated_at": "2026-09-08T01:00:00Z",
+  "language": "en",
+  "phase": "brief",
+  "approvals": {
+    "requirements": {
+      "generated": false,
+      "approved": false
+    },
+    "design": {
+      "generated": false,
+      "approved": false
+    },
+    "tasks": {
+      "generated": false,
+      "approved": false
+    }
+  },
+  "ready_for_implementation": false,
+  "supersedes": [
+    "learning-harness",
+    "constrained-detector"
+  ],
+  "depends_on": [],
+  "approval_note": "2026-09-08 corrective brief drafted at the maintainer's direction ('can you produce a corrective spec that supersedes the project and sets a course'). Written after the learning-harness evolve rounds produced zero decisions distinguishable from noise, and after researching Clearwing (/home/mc/Source/clearwing, fork norandom/deepwing), Google Big Sleep, and the DARPA AIxCC 2025 finals. Not approved; requirements not generated. The central recommendation is to adopt Clearwing as the execution substrate rather than rebuild, and to contribute the web/logic detection path (taxonomy + IR candidate enumeration + obligation checker + a differential web oracle) that Clearwing's crash-oracle model does not reach. ; 2026-09-08 renamed execution-grounded-detection -> model-grounded-detection after the maintainer's correction: execution is not possible for all SAST targets and the bugs we own do not crash, so the arbiter is a program model built deterministically from code (IR candidate enumeration, binding-chain dataflow, obligation/guard-dominance) that checks LLM claims, producing a static evidence ladder (suspicion -> model_corroborated -> model_entailed); execution is a deferred tier for memory-safety-in-C only, adopted from Clearwing when a class needs it."
+}