Paper Citation Record · LEDGER

Reinforced sequential Monte Carlo for amortised sampling

As of 8 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2510.11711.

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

pith.paper-citation-record.v1
2510.11711 v3

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T10:09:48.298843Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

30 of 30 outbound references displayed

verified exact0
verified fuzzy0
unresolved30
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cac2bb8f-47e5-4294-b4fb-dac952d1f0d6 · outbound

This paper cites an unresolved cited work.

Reinforced sequential Monte Carlo for amortised sampling Unresolved cited work

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T10:09:44.991726Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f69419c0-a320-4316-8307-0ab868695047 · outbound

This paper cites AdaLead: A simple and robust adaptive greedy search algorithm for sequence design.

Reinforced sequential Monte Carlo for amortised sampling AdaLead: A simple and robust adaptive greedy search algorithm for sequence design

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-04T10:09:45.652844Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e761ffc6-3029-4e47-8f9a-5992ceb434f2 · outbound

This paper cites an unresolved cited work.

Reinforced sequential Monte Carlo for amortised sampling Unresolved cited work

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T10:09:47.801346Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5a8d99db-e306-4989-bb8b-0157b37a985b · outbound

This paper cites Loula, J., LeBrun, B., Du, L., Lipkin, B., Pasti, C., Grand, G., Liu, T., Emara, Y., Freedman, M., Eis- ner, J., Cotterell, R., Mansinghka, V., Lew, A.

Reinforced sequential Monte Carlo for amortised sampling Loula, J., LeBrun, B., Du, L., Lipkin, B., Pasti, C., Grand, G., Liu, T., Emara, Y., Freedman, M., Eis- ner, J., Cotterell, R., Mansinghka, V., Lew, A

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T10:09:45.447921Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4402c683-ef1f-4083-9f98-422b4c4236a0 · outbound

This paper cites (2023) (further developed in Zhang et al.

Reinforced sequential Monte Carlo for amortised sampling (2023) (further developed in Zhang et al

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-04T10:09:46.556254Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 19cf8d06-491d-4908-8f89-544471dfa0d7 · outbound

This paper cites an unresolved cited work.

Reinforced sequential Monte Carlo for amortised sampling Unresolved cited work

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-04T10:09:46.727078Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9334235e-343d-4d14-b0b1-486e39310a55 · outbound

This paper cites By setting the latent variables asx n =y tn , we can see that the diffusion models belong to the family of hierarchical latent variable models.

Reinforced sequential Monte Carlo for amortised sampling By setting the latent variables asx n =y tn , we can see that the diffusion models belong to the family of hierarchical latent variable models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T10:09:46.868730Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3dd19927-977b-4e48-9f45-25c676ec2ea5 · outbound

This paper cites an unresolved cited work.

Reinforced sequential Monte Carlo for amortised sampling Unresolved cited work

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-04T10:09:46.950762Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 414f5831-05c1-4689-9f69-06960477a67e · outbound

This paper cites The Double Well energy is: EDW(x1, x2) =x 4 1 −6x 2 1 − 1 2 x1 + 1 2 x2.

Reinforced sequential Monte Carlo for amortised sampling The Double Well energy is: EDW(x1, x2) =x 4 1 −6x 2 1 − 1 2 x1 + 1 2 x2

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-04T10:09:47.106490Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6bfc475d-2783-4a13-b254-820e9fd31a11 · outbound

This paper cites an unresolved cited work.

Reinforced sequential Monte Carlo for amortised sampling Unresolved cited work

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T10:09:47.190028Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b75be19d-67d4-4162-943e-c9376259c8f3 · outbound

This paper cites We generate DNA sequences of length 8, where each token is a DNA nucleotide (A, G, C, or T).

Reinforced sequential Monte Carlo for amortised sampling We generate DNA sequences of length 8, where each token is a DNA nucleotide (A, G, C, or T)

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-04T10:09:48.018880Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eb80afee-7458-48dd-8faf-ee28ce1aeb1c · outbound

This paper cites Lett 2 be the density of Student’s t-distribution with degree 2 andν i be the shift of componenti.

Reinforced sequential Monte Carlo for amortised sampling Lett 2 be the density of Student’s t-distribution with degree 2 andν i be the shift of componenti

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-04T10:09:47.503933Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5c5658b7-6643-4d27-ad61-39a3306dd1ed · outbound

This paper cites We adopt the visualisation method in Chen et al.

Reinforced sequential Monte Carlo for amortised sampling We adopt the visualisation method in Chen et al

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-04T10:09:47.733839Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e7b52716-e518-4f84-abf9-b7b0b41773b0 · outbound

This paper cites Similar to QM9, we create string representations of small molecular graphs with 6 blocks, each from a predefined set of 18 building blocks with 2 stems.

Reinforced sequential Monte Carlo for amortised sampling Similar to QM9, we create string representations of small molecular graphs with 6 blocks, each from a predefined set of 18 building blocks with 2 stems

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-04T10:09:47.887728Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ff321923-18ab-4a2b-a0f5-c570dfc28413 · outbound

This paper cites We generate RNA sequences of length 14, where each token is an RNA nucleotide (A, G, C, or U).

Reinforced sequential Monte Carlo for amortised sampling We generate RNA sequences of length 14, where each token is an RNA nucleotide (A, G, C, or U)

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-04T10:09:48.137633Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a6bcafbe-95e2-4e25-94d4-f6114e2ca508 · outbound

This paper cites an unresolved cited work.

Reinforced sequential Monte Carlo for amortised sampling Unresolved cited work

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-04T10:09:48.219574Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation edb9508c-69c7-462b-b391-0d90c82e5153 · outbound

This paper cites an unresolved cited work.

Reinforced sequential Monte Carlo for amortised sampling Unresolved cited work

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-04T10:09:45.081740Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eb01820d-1f92-4021-9582-b356fce9f443 · outbound

This paper cites Amortized In-Context Bayesian Posterior Estimation.

Reinforced sequential Monte Carlo for amortised sampling Amortized In-Context Bayesian Posterior Estimation

Reference 401

Resolution: unresolved; no resolver link, observed 2026-08-04T10:09:45.545917Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bad64ee8-a4d0-4347-af1e-23b28fea839a · outbound

This paper cites Sequential Monte Carlo Steering of Large Language Models using Probabilistic Programs.

Reinforced sequential Monte Carlo for amortised sampling Sequential Monte Carlo Steering of Large Language Models using Probabilistic Programs

Reference 470

Resolution: unresolved; no resolver link, observed 2026-08-04T10:09:45.350776Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c93abe8f-a1bc-4066-8ef4-56b945bf12c6 · outbound

This paper cites Optimal Transport Tools (OTT): A JAX Toolbox for all things Wasserstein.

Reinforced sequential Monte Carlo for amortised sampling Optimal Transport Tools (OTT): A JAX Toolbox for all things Wasserstein

Reference 812

Resolution: unresolved; no resolver link, observed 2026-08-04T10:09:45.264711Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4a33f38-62f2-4805-a1bb-228c8fe5ce60 · outbound

This paper cites •ManyW ell(d∈[32,64]) (N¨ usken and Richter, 2021; Midgley et al.,.

Reinforced sequential Monte Carlo for amortised sampling •ManyW ell(d∈[32,64]) (N¨ usken and Richter, 2021; Midgley et al.,

Reference 2003

Resolution: unresolved; no resolver link, observed 2026-08-04T10:09:47.027671Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 968ad3ec-3587-4f03-9aea-e42ed7486379 · outbound

This paper cites Note that EUBO, MMD, and Sinkhorn distance calculations require unbiased samples from the target distribution.

Reinforced sequential Monte Carlo for amortised sampling Note that EUBO, MMD, and Sinkhorn distance calculations require unbiased samples from the target distribution

Reference 2012

Resolution: unresolved; no resolver link, observed 2026-08-04T10:09:47.588836Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2eebe29-527d-409c-8594-3351a319ef28 · outbound

This paper cites an unresolved cited work.

Reinforced sequential Monte Carlo for amortised sampling Unresolved cited work

Reference 2013

Resolution: unresolved; no resolver link, observed 2026-08-04T10:09:45.816812Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0041fb9c-af1b-40f1-a557-175653971b44 · outbound

This paper cites We do not apply learning rate scheduling.

Reinforced sequential Monte Carlo for amortised sampling We do not apply learning rate scheduling

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-04T10:09:48.298843Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bcf05785-6d33-4315-ba02-66abbcf48e75 · outbound

This paper cites an unresolved cited work.

Reinforced sequential Monte Carlo for amortised sampling Unresolved cited work

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-04T10:09:46.268400Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c9f5f6c5-3917-4ae6-8586-8159e5468e43 · outbound

This paper cites Generative flow networks (GFlowNets; Bengio et al., 2021,.

Reinforced sequential Monte Carlo for amortised sampling Generative flow networks (GFlowNets; Bengio et al., 2021,

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-04T10:09:45.961462Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab44cb14-4ea9-429f-9794-5c95bf16fea6 · outbound

This paper cites an unresolved cited work.

Reinforced sequential Monte Carlo for amortised sampling Unresolved cited work

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-04T10:09:47.649584Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c1d75544-befb-451c-8181-59900bef40f9 · outbound

This paper cites GFlowNets bridge the gap between maximum entropy reinforcement learning (MaxEnt RL) algorithms (Haarnoja et al., 2017, 2018; Nachum et al.,.

Reinforced sequential Monte Carlo for amortised sampling GFlowNets bridge the gap between maximum entropy reinforcement learning (MaxEnt RL) algorithms (Haarnoja et al., 2017, 2018; Nachum et al.,

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-04T10:09:46.078361Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1f1fa4fe-1ccb-494c-909f-236dac2adcd5 · outbound

This paper cites These approaches provide valuable off-policy training examples that improve training efficiency and/or mode coverage.

Reinforced sequential Monte Carlo for amortised sampling These approaches provide valuable off-policy training examples that improve training efficiency and/or mode coverage

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-04T10:09:46.435321Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation becba1ed-d615-48f9-9162-df26a30c19e9 · outbound

This paper cites an unresolved cited work.

Reinforced sequential Monte Carlo for amortised sampling Unresolved cited work

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-04T10:09:47.370465Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.