Paper Citation Record · LEDGER

Harnessing the Power of Reinforcement Learning for Adaptive MCMC

As of 9 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 1 inbound Pith citation observation for arXiv:2507.00671.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2507.00671 v1

Coverage vector

measured 16 of 16 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:20:14.510632Z

measured 17 of 17 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T11:20:24.613981Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

16 of 16 outbound references displayed

verified exact2
verified fuzzy4
unresolved10
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d9e878d3-457f-4d82-8075-4a3948910e36 · outbound

This paper cites an unresolved cited work.

Harnessing the Power of Reinforcement Learning for Adaptive MCMC Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:20:14.855832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c8c28bc4-bffb-4e2c-b00a-04dcb38f34ac · outbound

This paper cites an unresolved cited work.

Harnessing the Power of Reinforcement Learning for Adaptive MCMC Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:20:14.867804Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 395629aa-6094-477c-bb12-711bc430b193 · outbound

This paper cites Drawing on the reward-centring framework of Naik et al.

Harnessing the Power of Reinforcement Learning for Adaptive MCMC Drawing on the reward-centring framework of Naik et al

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:20:14.833574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b5bafeb6-6c0f-4149-bcbc-3ded8b53ee69 · outbound

This paper cites an unresolved cited work.

Harnessing the Power of Reinforcement Learning for Adaptive MCMC Unresolved cited work

Reference 12

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:20:14.820557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7c59ec7d-a3e8-4b07-8711-75fd3ec1b49d · outbound

This paper cites an unresolved cited work.

Harnessing the Power of Reinforcement Learning for Adaptive MCMC Unresolved cited work

Reference 13

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:20:14.808441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8be465f3-1a9b-4471-8510-993f359124aa · outbound

This paper cites E.4 Exploring the Sensitivity to G0 The results that we report for posteriordb in the main text set G0 based on 10 4 gold- standard samples from the target.

Harnessing the Power of Reinforcement Learning for Adaptive MCMC E.4 Exploring the Sensitivity to G0 The results that we report for posteriordb in the main text set G0 based on 10 4 gold- standard samples from the target

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:20:14.782357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8fed4785-731e-4da7-8fb9-9b87d3f82aad · outbound

This paper cites It is based on the accept-reject rule of Barker [1965], stated for d-dimensional distributions in Algorithm.

Harnessing the Power of Reinforcement Learning for Adaptive MCMC It is based on the accept-reject rule of Barker [1965], stated for d-dimensional distributions in Algorithm

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:20:14.770082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b7bd0bdb-78f1-47c4-96ce-4876a1e5d5b5 · outbound

This paper cites an unresolved cited work.

Harnessing the Power of Reinforcement Learning for Adaptive MCMC Unresolved cited work

Reference 1951

Resolution: unresolved; no resolver link, observed 2026-08-06T21:20:14.474741Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 905a90bd-270d-4989-8d7b-5682f68886bc · outbound

This paper cites an unresolved cited work.

Harnessing the Power of Reinforcement Learning for Adaptive MCMC Unresolved cited work

Reference 1996

Resolution: unresolved; no resolver link, observed 2026-08-06T21:20:14.462273Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c53f0ef4-f05f-4248-984e-c57e7adc613d · outbound

This paper cites AutoStep: Locally adaptive involutive MCMC.

Harnessing the Power of Reinforcement Learning for Adaptive MCMC AutoStep: Locally adaptive involutive MCMC

Reference 2000

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:20:14.639466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4b6a48a5-4d28-4e71-93bf-2bd0de7dbf1a · outbound

This paper cites Sampling via Gradient Flows in the Space of Probability Measures.

Harnessing the Power of Reinforcement Learning for Adaptive MCMC Sampling via Gradient Flows in the Space of Probability Measures

Reference 2012

Resolution: unresolved; no resolver link, observed 2026-08-06T21:20:14.449596Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53b2523d-771b-4cd1-b9b2-25f4fe694a5c · outbound

This paper cites an unresolved cited work.

Harnessing the Power of Reinforcement Learning for Adaptive MCMC Unresolved cited work

Reference 2016

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:20:14.845143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b5099016-f208-4b37-87d9-e8fa3eb3ba95 · outbound

This paper cites Large sample analysis of the median heuristic.

Harnessing the Power of Reinforcement Learning for Adaptive MCMC Large sample analysis of the median heuristic

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-06T21:20:14.458482Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cc71d6b1-904b-4c36-ba88-98183dabfa80 · outbound

This paper cites E.1 Implementation Details The initial state x0 ∈ Rd of all Markov chains was taken to be the arithmetic mean of 104 gold-standard samples from the target.

Harnessing the Power of Reinforcement Learning for Adaptive MCMC E.1 Implementation Details The initial state x0 ∈ Rd of all Markov chains was taken to be the arithmetic mean of 104 gold-standard samples from the target

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:20:14.795561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b0db2aff-f3f8-4dae-b8c4-676bae29a23c · outbound

This paper cites A Unified Framework for Multiple-Try Metropolis: Construction and Empirical Benchmarks.

Harnessing the Power of Reinforcement Learning for Adaptive MCMC A Unified Framework for Multiple-Try Metropolis: Construction and Empirical Benchmarks

Reference 2023

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:20:14.744606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6a234d97-1b19-4d65-bd3a-bd2b23d3b1a8 · outbound

This paper cites Reward Centering.

Harnessing the Power of Reinforcement Learning for Adaptive MCMC Reward Centering

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-06T21:20:14.470216Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation f3c185a4-7663-4ea0-82a3-8a697854852a · inbound

Stop the Sampler! Classifier-Based Adaptive Stopping for Sampling Kernels cites this paper.

Stop the Sampler! Classifier-Based Adaptive Stopping for Sampling Kernels Harnessing the Power of Reinforcement Learning for Adaptive MCMC

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-02T11:20:24.613981Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.