feat: curate inference sampler candidates for Insight

bibliography/pyautomemory.bib

@@ -16191,3 +16191,48 @@ @misc{Galan2026
   url = {https://arxiv.org/abs/2609.00179},
   year = {2026}
 }
+
+@misc{Lange2023Nautilus,
+  author = {Lange, Johannes U.},
+  title = {NAUTILUS: boosting Bayesian importance nested sampling with deep learning},
+  year = {2023},
+  archivePrefix = {arXiv},
+  eprint = {2306.16923},
+  url = {https://arxiv.org/abs/2306.16923}
+}
+
+@misc{Cabezas2024BlackJAX,
+  author = {Cabezas, Alberto and Corenflos, Adrien and Lao, Junpeng and Louf, Remi and Carnec, Antoine and Chaudhari, Kaustubh and Cohn-Gordon, Reuben and Coullon, Jeremie and Deng, Wei and Duffield, Sam and Duran-Martin, Gerardo and Elantkowski, Marcin and Foreman-Mackey, Dan and Gregori, Michele and Iguaran, Carlos and Kumar, Ravin and Lysy, Martin and Murphy, Kevin and Orduz, Juan Camilo and Patel, Karm and Wang, Xi and Zinkov, Rob},
+  title = {BlackJAX: Composable Bayesian inference in JAX},
+  year = {2024},
+  archivePrefix = {arXiv},
+  eprint = {2402.10797},
+  url = {https://arxiv.org/abs/2402.10797}
+}
+
+@misc{Buchner2021UltraNest,
+  author = {Buchner, Johannes},
+  title = {UltraNest -- a robust, general purpose Bayesian inference engine},
+  year = {2021},
+  archivePrefix = {arXiv},
+  eprint = {2101.09604},
+  url = {https://arxiv.org/abs/2101.09604}
+}
+
+@misc{Karamanis2022PocoMC,
+  author = {Karamanis, Minas and Nabergoj, David and Beutler, Florian and Peacock, John A. and Seljak, Uros},
+  title = {pocoMC: A Python package for accelerated Bayesian inference in astronomy and cosmology},
+  year = {2022},
+  archivePrefix = {arXiv},
+  eprint = {2207.05660},
+  url = {https://arxiv.org/abs/2207.05660}
+}
+
+@misc{Hoffman2011NUTS,
+  author = {Hoffman, Matthew D. and Gelman, Andrew},
+  title = {The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo},
+  year = {2011},
+  archivePrefix = {arXiv},
+  eprint = {1111.4246},
+  url = {https://arxiv.org/abs/1111.4246}
+}

wiki/methods/concepts/sampler-candidate-curation.md

@@ -0,0 +1,101 @@
+---
+title: Sampler candidate curation
+type: concept
+topics: [samplers, literature, inference]
+sources:
+  - Speagle 2020 — dynesty
+  - Lange 2023 — Nautilus
+  - Cabezas et al. 2024 — BlackJAX
+  - Buchner 2021 — UltraNest
+  - Karamanis et al. 2022 — pocoMC
+status: drafted
+---
+
+# Sampler candidate curation
+
+## TL;DR
+
+A sampler paper establishes a method and reported experiments, not its suitability
+for an untested likelihood. Keep a bounded shortlist with public paper/code
+provenance, explicit requirements, a dated integration check and unanswered
+benchmark questions. Curate on demand; literature ingestion never submits compute.
+
+## What it is
+
+A candidate is a hypothesis for investigation. This initial selection spans
+existing evidence baselines, gradient posterior sampling and two alternative
+evidence approaches. It is deliberately non-exhaustive and carries no ranking.
+
+## Why it matters
+
+Posterior sampling, evidence estimation and optimization answer different
+questions. Compare only methods that deliver the required output. A library
+wrapper, an installed optional dependency and demonstrated scientific performance
+are three distinct facts.
+
+## Key results from the literature
+
+- dynesty offers static/dynamic evidence and posterior estimation, making it an
+  allocation baseline ([[sources-samplers#dynesty]]).
+- Nautilus learns proposals for importance nested sampling; published advantages
+  motivate a test, with unseen-mode coverage still a benchmark question
+  ([[sources-samplers#nautilus-2023]]).
+- BlackJAX supplies composable JAX inference; NUTS adapts gradient trajectories.
+  Gradient compatibility, initialization and chain diagnostics must be checked
+  ([[sources-samplers#blackjax-nuts-2024]],
+  [[sources-samplers#hoffman-and-gelman-2011-nuts]]).
+- UltraNest prioritizes reliable inference for challenging shapes. Repeated-seed
+  evidence checks would test that promise locally
+  ([[sources-samplers#ultranest-2021]]).
+- pocoMC uses flow preconditioning; correlated and multimodal targets motivate
+  investigating population coverage and training cost
+  ([[sources-samplers#pocomc-2022]]).
+
+The coverage checks and benchmark design above are investigation proposals,
+not claims that these papers prove PyAutoFit correctness or speed.
+
+## Dated integration check
+
+On 2026-10-08, PyAutoFit revision
+`8bb5f6e8fab809d47f785e9f2ac8289b3e3c9e3e` exported `DynestyStatic`,
+`DynestyDynamic`, `Nautilus` and `BlackJAXNUTS` from `autofit/__init__.py`.
+The search tree contained no UltraNest or pocoMC wrapper. This was a read-only
+source inspection, not an import, install, run or completeness guarantee.
+Benchmark status for this shortlist is **not assessed**; consult actual project
+evidence separately rather than interpreting this status as “never run.”
+
+## On-demand Memory to Insight handoff
+
+1. For missed literature, reuse Memory's existing harvest:
+   `python scripts/catch_up.py --scope interests --topic Stats --since YYYY-MM-DD --json`.
+   Retain its source counts, cutoff, dropped-in-memory count and warnings,
+   including truncated or unavailable arXiv coverage. Harvest is optional;
+   a requested named paper can go directly to verification. This bounded
+   initial survey verified named records directly and did not run a gap-fill.
+2. Follow the existing catch-up tier curation and human filing confirmation
+   when invoking that workflow. Verify the primary record and official code;
+   deduplicate by DOI/arXiv identity, file compact claim support and canonical
+   bibliography metadata, then run `make validate`.
+3. Prepare an independent public-source-derived candidate JSON for Insight.
+   Each record has `id`, `name`, `family`, `paper_url`, `code_url`,
+   `expected_uses`, `limitations`, `integration_status`, `benchmark_status`,
+   `reviewed_at` and `investigation_prompt`. Use stable kebab-case identifiers,
+   HTTPS provenance, string-array uses/limitations and an ISO review date.
+   Investigation prompts request a bounded comparison with output accuracy,
+   repeated seeds, configuration/version capture and total cost including
+   compilation or training. They are proposals, not executable orders.
+4. Insight owns review/publication of its independent candidate list. Export
+   no Memory wiki excerpts, private references, archival filenames or local
+   paths. The public paper and official code must support the published text
+   without Memory access. Literature records do not set integration or science
+   acceptance; recheck the actual PyAutoFit roster when refreshing them.
+
+No autonomous scan or schedule is introduced. Any benchmark launch requires the
+separate campaign workflow and its authorization.
+
+## See also
+
+- [[nested-sampling]]
+- [[hamiltonian-monte-carlo]]
+- [[sampler-benchmarks]]
+- [Existing catch-up procedure](../../../skills/catch_up/catch_up.md)

wiki/methods/index.md

@@ -13,6 +13,9 @@ should use this wiki.
 
 ## Samplers
 
+- [[sampler-candidate-curation]] — bounded source-backed shortlist and on-demand
+  public-provenance handoff to Insight; no scheduled scan.
+
 - [[mcmc-samplers]] — Metropolis-Hastings, ensemble samplers, Eryn.
 - [[hamiltonian-monte-carlo]] — NUTS, BlackJAX, NumPyro, Stan; gradients
   via JAX.

wiki/methods/log.md

@@ -123,3 +123,12 @@ references became `[[seed-…]]`, and each index gained one *Seed (unverified
 imports)* line saying what they are. Promoting an entry means verifying the
 paper, adding its canonical key to `../../bibliography/pyautomemory.bib`, and
 moving the section into the matching `sources/` page.
+
+
+## 2026-10-08 — Bounded sampler literature curation
+
+Verified primary public records for dynesty, Nautilus, BlackJAX, NUTS, UltraNest
+and pocoMC. Resolved the dynesty source section to existing `Speagle2020`; added
+five canonical entries and compact claim support. Added candidate curation and
+an on-demand public-provenance Insight handoff using existing catch-up machinery.
+Integration is a dated source-roster check; no benchmark verdict, install or run.

wiki/methods/sources/samplers.md

@@ -2,7 +2,7 @@
 title: Sources — samplers
 type: sources
 topics: [samplers, mcmc, nested-sampling]
-status: stub
+status: drafted
 ---
 
 # Sources: samplers
@@ -78,18 +78,19 @@ hopping, and the libraries that implement them.
 
 ## Dynesty
 
-**Canonical BibTeX key:** TODO — ambiguous candidates: `Dynesty`, `Speagle2020`, `dynesty`.
-**Reference:** TODO — resolve the paper identity against `bibliography/pyautomemory.bib` and an authoritative public record.
-**Concepts:** [[nested-sampling]]
+**Canonical BibTeX key:** `Speagle2020`
+**Reference:** [arXiv:1904.02180](https://arxiv.org/abs/1904.02180); doi:10.1093/mnras/staa278.
+**Concepts:** [[nested-sampling]], [[sampler-candidate-curation]]
 
-**Supports TODO:**
-- TODO — not migrated: canonical key is ambiguous, so claim support must be verified after key resolution.
+**Supports:**
+- Static and dynamic nested sampling estimate posteriors and evidence.
+- Dynamic allocation adjusts effort between posterior and evidence estimation.
 
-**Use when TODO:**
-- TODO — use only after the canonical key and paper identity are resolved.
+**Use when:**
+- Explaining dynesty's allocation strategy or choosing an evidence baseline.
 
-**Do not use for TODO:**
-- Evidence until the ambiguous canonical key is resolved.
+**Do not use for:**
+- Assuming a winner on an untested likelihood or current integration status.
 
 
 ## emcee
@@ -114,3 +115,79 @@ hopping, and the libraries that implement them.
 - [[nested-sampling]]
 - [[hamiltonian-monte-carlo]]
 - [[sources-probabilistic-programming]]
+
+## Nautilus — 2023
+
+**Canonical BibTeX key:** `Lange2023Nautilus`
+**Reference:** [arXiv:2306.16923](https://arxiv.org/abs/2306.16923); [official code](https://github.com/johannesulf/nautilus).
+**Concepts:** [[sampler-candidate-curation]]
+
+**Supports:**
+- Combines importance nested sampling with neural-network regression for posterior and evidence estimation.
+
+**Use when:**
+- Evidence and posterior estimation for expensive gradient-free likelihoods.
+
+**Do not use for:**
+- A PyAutoFit performance ranking, evidence calibration claim or production acceptance.
+
+## BlackJAX NUTS — 2024
+
+**Canonical BibTeX key:** `Cabezas2024BlackJAX`
+**Reference:** [arXiv:2402.10797](https://arxiv.org/abs/2402.10797); [official code](https://github.com/blackjax-devs/blackjax).
+**Concepts:** [[sampler-candidate-curation]]
+
+**Supports:**
+- Provides composable JAX inference kernels operating on target log densities; the library includes NUTS.
+
+**Use when:**
+- Posterior refinement for differentiable JAX targets.
+
+**Do not use for:**
+- A PyAutoFit performance ranking, evidence calibration claim or production acceptance.
+
+## UltraNest — 2021
+
+**Canonical BibTeX key:** `Buchner2021UltraNest`
+**Reference:** [arXiv:2101.09604](https://arxiv.org/abs/2101.09604); [official code](https://github.com/JohannesBuchner/UltraNest).
+**Concepts:** [[sampler-candidate-curation]]
+
+**Supports:**
+- Provides parameter estimation and model comparison for non-Gaussian and multimodal spaces, with resumable parallel runs.
+
+**Use when:**
+- Independent evidence cross-check for non-Gaussian and multimodal targets.
+
+**Do not use for:**
+- A PyAutoFit performance ranking, evidence calibration claim or production acceptance.
+
+## pocoMC — 2022
+
+**Canonical BibTeX key:** `Karamanis2022PocoMC`
+**Reference:** [arXiv:2207.05660](https://arxiv.org/abs/2207.05660); [official code](https://github.com/minaskar/pocomc).
+**Concepts:** [[sampler-candidate-curation]]
+
+**Supports:**
+- Uses normalizing-flow preconditioning for nonlinear and multimodal posterior inference and model comparison.
+
+**Use when:**
+- Posterior and evidence investigation for strongly correlated nonlinear targets.
+
+**Do not use for:**
+- A PyAutoFit performance ranking, evidence calibration claim or production acceptance.
+
+## Hoffman and Gelman 2011 — NUTS
+
+**Canonical BibTeX key:** `Hoffman2011NUTS`
+**Reference:** [arXiv:1111.4246](https://arxiv.org/abs/1111.4246).
+**Concepts:** [[hamiltonian-monte-carlo]], [[sampler-candidate-curation]]
+
+**Supports:**
+- NUTS chooses HMC trajectory lengths using a U-turn stopping criterion.
+- Dual averaging adapts the step size.
+
+**Use when:**
+- Explaining the gradient sampler's algorithm, distinct from its BlackJAX implementation.
+
+**Do not use for:**
+- An evidence estimator or proof of multimodal coverage.