docs: point sampler campaign records to Insight

AGENTS.md

@@ -53,6 +53,14 @@ human confirms once, and the chosen papers are filed in one PR.
   failed approaches) lives in **PyAutoMind** (the `complete/<YYYY>/<MM>/`
   records, GitHub issues), not here. Memory = what the science says; Mind = what
   the organism did.
+- **Inference and profiling campaign records** — campaign intent, coverage and
+  evidence presentation belong to **PyAutoInsight** and **PyAutoPulse**
+  respectively. Their `*_inference` / `*_profiling` projects own protocols,
+  detailed results and provenance; Cortex retains scientific run records and
+  human conclusions. Keep short pointers and reusable methodological lessons
+  here, not duplicate counts, per-search statuses, timing tables or pending work.
+  Route by information type, not permanence. Before removing an existing summary,
+  verify a merged, reachable destination preserves its evidence and provenance.
 - **Workflow state, health, execution** — Mind / Heart / Build respectively.
 
 ## Scope: personal research material, out of scope for user-facing repos

wiki/methods/concepts/sampler-benchmarks.md

@@ -73,42 +73,24 @@ From the `autolens_profiling` HPC runs ("searches first-class" series):
 Raw outputs, job scripts and result JSON/PNGs live in
 `autolens_profiling` (per-run directories; see that repo's AGENTS.md).
 
-## The search catalogue — every PyAutoFit search, per task (2026-10)
-
-The organism's first benchmark that covers **every search in the
-manifest** rather than a hand-picked few lives in `autofit_inference`:
-`catalogue/search_catalogue.json` (schema
-`autofit-inference-search-catalogue`, version 1), read here at commit
-`3463aa19014b6bd83563018568f0fb4a2376f8eb`.
-
-- **Protocol id `gaussian_x3@1`** — a toy likelihood (three blended or
-  separated 1D Gaussians, datasets `gaussian_x3_blend` /
-  `gaussian_x3_separated`), pre-registered in that repo's
-  `wiki/project/protocol_gaussian_x3.md`. Campaign task
-  `gaussian_x3_search_wave1`.
-- **Grouped by requested task, never compared across tasks**:
-  `point_map` (judged by protocol §4(a): max log posterior within 1 nat
-  of the reference MAP), `posterior` (§4(b): relabelled medians,
-  sigma-ratio band, PPC χ², mode coverage), `evidence` (§4(b)+§4(c):
-  posterior accuracy and ln Z within 1 nat).
-- **Wave 1 ranks nothing** (protocol §9): its rows calibrate the
-  protocol's thresholds. 223 of 520 expected runs landed before the
-  B3 wrap-up ruling (2026-10-08) stopped the pilot; the rest are
-  deferred to wave 2 on RAL. The pilot ran on PyAutoFit `main` before
-  phase A2 (gradients under JAX), so the BFGS / L-BFGS non-finite
-  results it records are not a judgement on those searches.
-- Per-search status at that commit: 13 `measured`, NSS `deferred`
-  (phase A3b), Drawer `unsupported` (a sanity floor that answers no
-  task).
-- The catalogue embeds the search-manifest snapshot it was built
-  against (`catalogue/search_manifest.json`, `search-manifest@1`), so a
-  row is keyed by the same registry name the rosters, the capability
-  matrix and the Brain's samplers faculty use. The faculty lists the
-  catalogue's cells as its `mature (autofit_inference search
-  catalogue)` tier.
-
-Read it as coverage and failure modes per task until a ranking wave
-lands; quote no "fastest search" from wave 1.
+## The search catalogue — campaign and evidence pointers
+
+[Insight's wave-1 campaign record](https://github.com/PyAutoLabs/PyAutoInsight/blob/main/tasks/gaussian_x3_search_wave1.md)
+is the entry point for the `gaussian_x3` pilot's intent, coverage and interpretation.
+Follow [wave 2](https://github.com/PyAutoLabs/PyAutoInsight/blob/main/tasks/gaussian_x3_search_wave2.md)
+for its successor campaign. Detailed per-search evidence remains in
+[autofit_inference's catalogue](https://github.com/PyAutoLabs/autofit_inference/blob/main/catalogue/search_catalogue.json),
+with the [project protocol](https://github.com/PyAutoLabs/autofit_inference/blob/main/wiki/project/protocol_gaussian_x3.md).
+
+The A5 summary previously recorded here used
+[catalogue revision 3463aa1](https://github.com/PyAutoLabs/autofit_inference/blob/3463aa19014b6bd83563018568f0fb4a2376f8eb/catalogue/search_catalogue.json).
+That source preserves the historical snapshot; consult the campaign and source
+catalogue for counts, statuses and protocol thresholds rather than maintaining
+another copy here.
+
+The reusable lesson is to distinguish point estimation, posterior inference and
+evidence estimation before comparing searches. Exploratory calibration alone
+does not establish a speed ranking.
 
 ## Benchmark hygiene
 

wiki/methods/index.md

@@ -24,9 +24,8 @@ should use this wiki.
   vmap batching, chunked vmap.
 - [[initialization-chaining]] — which samplers provide vs require
   starting points; PyAutoFit chained searches.
-- [[sampler-benchmarks]] — the benchmarking-campaign record (MLTracker
-  contract, minimal + A100 results, raw-output locations) and the
-  per-task search catalogue (`autofit_inference`, protocol `gaussian_x3@1`).
+- [[sampler-benchmarks]] — benchmark methodology, historical minimal + A100
+  results, and pointers to Insight's search campaigns and the project-owned catalogue.
 - [[gradient-optimizer-benchmarks]] — JAX MAP/optimization study on the MGE
   lens likelihood (CPU + A100): diversity × gradients; multi-start Adam scales
   (GIGA-Lens), SVGD reaches truth, CMA-ES collapses.

wiki/methods/log.md

@@ -142,3 +142,13 @@ hand-written section pointing at `autofit_inference`'s
 `gaussian_x3@1`): per-task grouping, wave 1 ranks nothing, the
 pilot's coverage and statuses. The index entry names it. No benchmark
 verdict was added.
+
+## 2026-10-15 — Route search campaign records through Insight
+
+Replaced the duplicate A5 campaign summary in [[sampler-benchmarks]] with links
+to Insight's existing wave-1 and wave-2 records and the project-owned catalogue
+and protocol. Retained the pinned catalogue revision for historical provenance
+and the reusable task-comparability lesson. Earlier benchmark sections remain
+historical evidence pending a separate destination/provenance audit. The write
+contract now routes inference campaigns through Insight and profiling through
+Pulse; permanence alone does not make a campaign record Memory content.