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/constrained-detector/spec.json

@@ -23,5 +23,5 @@
     "learning-harness"
   ],
   "approval_note": "2026-09-06 brief drafted at the maintainer's direction ('I'd rather spend time to design the exact DSPy style interface and evolutionary / statistical prompt optimization than fixing piece by piece'). Not approved; requirements not generated.; 2026-09-06 renamed from detector-optimizer to constrained-detector after the trajectory measurement: the brief's own conclusion is that the optimiser is not the next thing to build, and the spec should be named for what it delivers; 2026-09-06 requirements generated at the maintainer's direction, with the maintainer's emphasis recorded as Requirement 3 ('don't forget the emphasis on the real world bugs from the larger projects to avoid overfitting. we otherwise benchmark for cases that are not relevant'); 2026-09-06 requirements approved by the maintainer in session ('approve the requirements and run the design phase'); 2026-09-06 design approved by the maintainer in session ('approve the design and generate the tasks'); 2026-09-08 SUPERSEDED by execution-grounded-detection: this session's measurements showed the noise architecture (K-runs, per-family floor, regression budget, sign test, evolve loop) is a statistical treatment for a structural problem, and that the field (Big Sleep, AIxCC finals, Clearwing) grounds detection in execution rather than in scoring against a labeled corpus. The taxonomy, the IR candidate enumerator (96.6% ceiling) and the obligation checker survive as the web/logic detection path to contribute to a Clearwing-based substrate.",
-  "superseded_by": "execution-grounded-detection"
+  "superseded_by": "model-grounded-detection"
 }