Paper Citation Record · LEDGER

MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration

As of 23 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 1 inbound Pith citation observation for arXiv:2507.12935.

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

pith.paper-citation-record.v1
2507.12935 v1

Coverage vector

measured 52 of 52 reference resolution

Typed states for the displayed outbound observations.

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measured 53 of 53 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T04:15:59.630119Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-11T04:16:02.869014Z

Reference resolution

52 of 52 outbound references displayed

verified exact1
verified fuzzy38
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5315cc6c-d6b8-4bc9-859b-be7d4ddd4aff · outbound

This paper cites LIGO Algorithm Library - LALSuite,.

MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration LIGO Algorithm Library - LALSuite,

Reference 1

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Observation 5877f23a-e5ef-4c80-8930-9441033e55a3 · outbound

This paper cites Near-optimal mimo detection using gradient-based mcmc in discrete spaces,.

MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration Near-optimal mimo detection using gradient-based mcmc in discrete spaces,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:42:12.754930Z

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Observation 6b7b6475-13b7-4d89-b74e-2edf4b837c11 · outbound

This paper cites Revisiting sampling for combinatorial optimization,.

MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration Revisiting sampling for combinatorial optimization,

Reference 3

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Observation 377f0df7-5c67-4890-ad73-2e418f3a1476 · outbound

This paper cites Gurobi Optimizer Reference Manual,.

MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration Gurobi Optimizer Reference Manual,

Reference 4

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Observation d349adb9-6b5c-4a8a-8d80-949a9984b3ea · outbound

This paper cites Dimes: A differentiable meta solver for combinatorial optimization problems,.

MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration Dimes: A differentiable meta solver for combinatorial optimization problems,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:42:12.729637Z

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Observation 80925904-1674-4d7d-9d0d-6957b3db8980 · outbound

This paper cites Combinatorial optimization with graph convolutional networks and guided tree search,.

MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration Combinatorial optimization with graph convolutional networks and guided tree search,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:42:12.721019Z

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Observation d29bf329-d897-4cd4-8f61-22dd941bd27c · outbound

This paper cites Difusco: Graph-based diffusion solvers for combinatorial optimization,.

MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration Difusco: Graph-based diffusion solvers for combinatorial optimization,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:42:12.713588Z

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Observation 715bf63f-bc52-4c64-877c-b15d54be97d0 · outbound

This paper cites Guest Editors Introduction to the top 10 algorithms ,.

MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration Guest Editors Introduction to the top 10 algorithms ,

Reference 8

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Observation a5193d3e-6036-4297-b97a-2cc6f12658dd · outbound

This paper cites an unresolved cited work.

MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration Unresolved cited work

Reference 9

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Observation fd7ff94c-1c2a-4666-ba4d-24caee1f4fa1 · outbound

This paper cites Probabilistic machine learning and artificial intelli- gence,.

MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration Probabilistic machine learning and artificial intelli- gence,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:42:12.698564Z

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Observation a4befb5b-f272-4950-abee-30a54db06a14 · outbound

This paper cites An introduction to mcmc for machine learning,.

MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration An introduction to mcmc for machine learning,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:42:12.690637Z

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Observation 4de33712-3ccf-43e9-a907-354ea2203d9c · outbound

This paper cites Koller and N.

MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration Koller and N

Reference 12

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Observation 10cecdc0-232e-4dd5-a9a4-5bc700e290b7 · outbound

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MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration Unresolved cited work

Reference 13

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Observation 5e6e7e53-d1b0-4346-a27c-520d3f56a47f · outbound

This paper cites DISCS: A Benchmark for Discrete Sampling,.

MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration DISCS: A Benchmark for Discrete Sampling,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:42:12.669080Z

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Observation 71417e3f-43cf-4c62-99eb-91e824e9a6ed · outbound

This paper cites A tutorial on energy-based learning,.

MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration A tutorial on energy-based learning,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:42:12.661093Z

Source-reported events for the cited work

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Observation 6fcb20b4-0881-427e-a710-39a20f2363b9 · outbound

This paper cites Fast, accurate training and sampling of restricted boltzmann machines,.

MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration Fast, accurate training and sampling of restricted boltzmann machines,

Reference 16

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Observation c176bd12-891c-4321-b247-231fdfd67807 · outbound

This paper cites Parameter estimation with gravitational waves,.

MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration Parameter estimation with gravitational waves,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:42:12.653595Z

Source-reported events for the cited work

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Observation d492483e-868d-4929-9adb-5cb9277f81e8 · outbound

This paper cites A novel monte-carlo-sampling-based receiver for large-scale uplink multiuser mimo systems,.

MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration A novel monte-carlo-sampling-based receiver for large-scale uplink multiuser mimo systems,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:42:12.646029Z

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Observation 54ca3514-5f5f-4514-a843-cf2e34473241 · outbound

This paper cites Large-scale mimo detection using mcmc approach with blockwise sampling,.

MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration Large-scale mimo detection using mcmc approach with blockwise sampling,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:42:12.638352Z

Source-reported events for the cited work

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Observation 75a4c3a6-2660-47c2-9ec3-ad2cb926cdee · outbound

This paper cites Imagenet classification with deep convolutional neural networks,.

MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration Imagenet classification with deep convolutional neural networks,

Reference 20

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Observation 2e5c8d52-9170-472b-9f0d-c6c96c4c2baf · outbound

This paper cites Natural language processing,.

MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration Natural language processing,

Reference 21

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Observation 69229bee-2d13-4730-9382-e7bf4ce66b3e · outbound

This paper cites Monte carlo sampling methods using markov chains and their applications,.

MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration Monte carlo sampling methods using markov chains and their applications,

Reference 22

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Observation 0603e7f2-4f0a-442a-bce6-f97a6b2df144 · outbound

This paper cites Illustration of bayesian inference in normal data models using gibbs sampling,.

MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration Illustration of bayesian inference in normal data models using gibbs sampling,

Reference 23

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Observation e07d74e8-61b9-46b3-867c-db4873b678e2 · outbound

This paper cites Blocking gibbs sampling in very large probabilistic expert systems,.

MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration Blocking gibbs sampling in very large probabilistic expert systems,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:42:12.608742Z

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Observation 8e4e7f4c-99ba-4f66-95ba-51d024ea20d5 · outbound

This paper cites Asynchronous gibbs sampling,.

MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration Asynchronous gibbs sampling,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:42:12.600709Z

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Observation 6b1b9e4c-8ada-4c2c-a122-9d77229c3625 · outbound

This paper cites Path auxiliary proposal for mcmc in discrete space,.

MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration Path auxiliary proposal for mcmc in discrete space,

Reference 26

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Observation ce03d28b-5e15-4779-abb5-0e0617b9f056 · outbound

This paper cites A langevin-like sampler for discrete dis- tributions,.

MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration A langevin-like sampler for discrete dis- tributions,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:42:12.584295Z

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Observation 301fad30-3b64-4bc2-a945-0ee036fe0f51 · outbound

This paper cites A 3mm 2 programmable bayesian inference accelerator for unsupervised machine perception using parallel gibbs sampling in 16nm,.

MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration A 3mm 2 programmable bayesian inference accelerator for unsupervised machine perception using parallel gibbs sampling in 16nm,

Reference 28

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Observation ceff54b9-44ee-4892-a959-4118e7926179 · outbound

This paper cites Coopmc: Algorithm-architecture co-optimization for markov chain monte carlo accelerators,.

MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration Coopmc: Algorithm-architecture co-optimization for markov chain monte carlo accelerators,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:42:12.567479Z

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Observation 4f050e86-6610-4117-90d3-b6c4c49973d9 · outbound

This paper cites Proca: Programmable probabilistic processing unit archi- tecture with accept/reject prediction & multicore pipelining for causal inference,.

MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration Proca: Programmable probabilistic processing unit archi- tecture with accept/reject prediction & multicore pipelining for causal inference,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:42:12.558595Z

Source-reported events for the cited work

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Observation d8dcf8b8-5367-4b37-8e1b-72da57024d7e · outbound

This paper cites Statistical robustness of markov chain monte carlo accelerators,.

MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration Statistical robustness of markov chain monte carlo accelerators,

Reference 31

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Observation 1dbf912d-34d4-4a08-83ce-f4939a6f04fb · outbound

This paper cites Massively parallel probabilistic computing with sparse ising machines,.

MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration Massively parallel probabilistic computing with sparse ising machines,

Reference 32

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Observation 300d2c1c-c64f-4668-a332-40af6cd85676 · outbound

This paper cites Towards 3d cmos+x ising machines: Addressing the connectivity prob- lem with back-end-of-line fefets,.

MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration Towards 3d cmos+x ising machines: Addressing the connectivity prob- lem with back-end-of-line fefets,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:42:12.539981Z

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Observation 3a105427-2f7f-4753-82c4-b7f571cc1f6e · outbound

This paper cites Ising machines as hard- ware solvers of combinatorial optimization problems,.

MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration Ising machines as hard- ware solvers of combinatorial optimization problems,

Reference 34

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Observation e23aae1f-326f-4ec9-8fd2-12f0998bad80 · outbound

This paper cites A full-stack view of probabilistic computing with p-bits: Devices, architectures, and algorithms,.

MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration A full-stack view of probabilistic computing with p-bits: Devices, architectures, and algorithms,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:42:12.521554Z

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Observation 1d151277-9618-44cc-91db-1727420e233a · outbound

This paper cites Acmc 2: Accelerating markov chain monte carlo algorithms for probabilistic models,.

MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration Acmc 2: Accelerating markov chain monte carlo algorithms for probabilistic models,

Reference 36

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Observation 11545ee4-1bed-42f2-92d7-60fc3eb64637 · outbound

This paper cites 12.2 p-circuits: Neither digital nor analog,.

MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration 12.2 p-circuits: Neither digital nor analog,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:42:12.512831Z

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Observation 54384f85-dc61-4fed-b694-3c5dbaaadc7a · outbound

This paper cites an unresolved cited work.

MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration Unresolved cited work

Reference 38

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Observation 7a60a25b-dc5b-495f-abc7-3761d585b2b7 · outbound

This paper cites PASS: An Asynchronous Probabilistic Processor for Next Generation Intelligence,.

MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration PASS: An Asynchronous Probabilistic Processor for Next Generation Intelligence,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:42:12.498176Z

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Observation 4611aed2-6dc9-48ac-b5aa-ea6ec32946ab · outbound

This paper cites Discrete langevin samplers via wasserstein gradient flow,.

MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration Discrete langevin samplers via wasserstein gradient flow,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:42:12.489715Z

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Observation 1a3d5a88-c80a-40aa-9f78-e21d31e97aff · outbound

This paper cites No free lunch theorems for optimization,.

MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration No free lunch theorems for optimization,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:42:12.480686Z

Source-reported events for the cited work

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Observation 38b9dfc5-cb56-41b0-84ba-6096551fbbaa · outbound

This paper cites CausaLearn: Automated Framework for Scalable Streaming-based Causal Bayesian Learning using FPGAs,.

MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration CausaLearn: Automated Framework for Scalable Streaming-based Causal Bayesian Learning using FPGAs,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:42:12.473060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 39419294-d442-4fb6-b000-06310e940cbc · outbound

This paper cites PMBA: A Parallel MCMC Bayesian Computing Accelerator,.

MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration PMBA: A Parallel MCMC Bayesian Computing Accelerator,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:42:12.463775Z

Source-reported events for the cited work

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Observation 99a2a821-40b2-403e-8be4-a89b697c3d8b · outbound

This paper cites Running Markov Chain Monte Carlo on Modern Hardware and Software.

MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration Running Markov Chain Monte Carlo on Modern Hardware and Software

Reference 44

Resolution: verified exact; local_arxiv, observed 2026-08-06T16:42:12.120689Z

Source-reported events for the cited work

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Observation 476456b4-db13-4461-a0fd-78b55d423102 · outbound

This paper cites Simple, distributed, and accelerated probabilistic program- ming,.

MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration Simple, distributed, and accelerated probabilistic program- ming,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:42:12.454657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 55450845-f8ba-44b2-aaff-85613eca232e · outbound

This paper cites Pyro: Deep universal probabilistic programming,.

MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration Pyro: Deep universal probabilistic programming,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:42:12.445369Z

Source-reported events for the cited work

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Observation 4814fb98-501e-46f8-87c2-b17ef698aada · outbound

This paper cites AIA: A 16nm Multicore SoC for Approximate Inference Acceleration Exploiting Non-normalized Knuth-Yao Sampling and Inter-Core Register Sharing,.

MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration AIA: A 16nm Multicore SoC for Approximate Inference Acceleration Exploiting Non-normalized Knuth-Yao Sampling and Inter-Core Register Sharing,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:42:12.436960Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 0419bd0d-7dbf-4b15-91ee-cbd51df71782 · outbound

This paper cites High performance monte carlo simulation of ising model on tpu clusters,.

MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration High performance monte carlo simulation of ising model on tpu clusters,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:42:12.428032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3629539d-aa6d-4b1e-9007-7d5aa0e1ffef · outbound

This paper cites Bayes net repository,.

MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration Bayes net repository,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:42:12.419746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3ab749db-a085-4fb1-8072-f2875e8a6abf · outbound

This paper cites agrum/pyagrum: a tool- box to build models and algorithms for probabilistic graphical models in python,.

MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration agrum/pyagrum: a tool- box to build models and algorithms for probabilistic graphical models in python,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:42:12.410387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1e5f5816-fd60-45e8-89ff-ad0479b3db41 · outbound

This paper cites Bayeslib,.

MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration Bayeslib,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:42:12.401314Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5fa72222-cf81-448e-8db9-8b43f4724083 · outbound

This paper cites Available: https://www.gurobi.com.

MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration Available: https://www.gurobi.com

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-06T16:42:11.972046Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 290cda98-3282-44ce-8236-f3e77c5516df · inbound

MCHA: A Memory-Centric Hierarchical Architecture for Parallel-Sequential Computing cites this paper.

MCHA: A Memory-Centric Hierarchical Architecture for Parallel-Sequential Computing MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration

Reference 69

Resolution: verified exact; local_arxiv, observed 2026-08-11T04:16:02.879983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.