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Paper Citation Record · LEDGER

Improving Trajectory Stitching with Flow Models

As of 16 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2505.07802.

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

pith.paper-citation-record.v1
2505.07802 v2

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:12:32.988682Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

42 of 42 outbound references displayed

  • verified exact0
  • verified fuzzy7
  • unresolved35
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 10dd5dd2-8ed4-4989-8459-1397b35eab83 · outbound

This paper cites Rombach, A.

Improving Trajectory Stitching with Flow Models Rombach, A

Reference 1

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation be2c7519-1dc6-4601-98d7-442a9beff6bc · outbound

This paper cites SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis.

Improving Trajectory Stitching with Flow Models SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 2

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source=pdf_text observed=2026-08-15T22:12:32.798361Z digest=sha256:85f33ac0fa8c69526c8525b703b70035a0cddf63fcfecadd9dec4d04ca2d63ef

Observation 38de9407-7569-424f-b944-e269df685157 · outbound

This paper cites Esser, J.

Improving Trajectory Stitching with Flow Models Esser, J

Reference 3

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 24b0ed26-a0f0-417e-98c5-de937e5fdcd1 · outbound

This paper cites Janner, Y.

Improving Trajectory Stitching with Flow Models Janner, Y

Reference 4

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T22:12:32.808133Z digest=sha256:a9e3e9a3297210954f91c7dc2b28ce1d5fac5f9c86d19d0b4b8fbc4dd0faf00e

Observation d5b4de98-8eb0-40ca-a07a-22176115aa24 · outbound

This paper cites an unresolved cited work.

Improving Trajectory Stitching with Flow Models Unresolved cited work

Reference 5

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T22:12:32.812694Z digest=sha256:5acce68b4cab84dff4b4923f4d9502b2145dab1b5a0d8f3fad0882f7bc930554

Observation f35e57b7-9cc3-4933-9495-b3a523d46117 · outbound

This paper cites an unresolved cited work.

Improving Trajectory Stitching with Flow Models Unresolved cited work

Reference 6

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source=pdf_text observed=2026-08-15T22:12:32.816892Z digest=sha256:c6a9b3ef19546f640f069edb8dabb07337d410a023126de983ef40b0c9826b54

Observation 0d4a49d6-6fe0-427f-af80-c86f63eebc90 · outbound

This paper cites an unresolved cited work.

Improving Trajectory Stitching with Flow Models Unresolved cited work

Reference 7

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source=pdf_text observed=2026-08-15T22:12:32.822008Z digest=sha256:65ac15a14ff1078fdd9ce6e283553e11541f9a56b1bcaa1cd12068a38a90d177

Observation 5ed82554-cb1d-4e7c-8072-6a1ed70c0cd7 · outbound

This paper cites Song and S.

Improving Trajectory Stitching with Flow Models Song and S

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T22:12:32.828205Z digest=sha256:dba50f272eb1bd9bd172d499207e5ee5dcd9cb583c477cb5336b079d0bde38eb

Observation aade08e2-3979-4045-a91d-bdf774c24043 · outbound

This paper cites Scaling Rectified Flow Transformers for High-Resolution Image Synthesis.

Improving Trajectory Stitching with Flow Models Scaling Rectified Flow Transformers for High-Resolution Image Synthesis

Reference 9

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source=pdf_text observed=2026-08-15T22:12:32.833031Z digest=sha256:194227ed9271c7d1153f1b784274141d340cd9db187c81efdb709d1380051cf0

Observation 4c2dd4be-5224-4e8f-a76f-e683ade4f452 · outbound

This paper cites Movie Gen: A Cast of Media Foundation Models.

Improving Trajectory Stitching with Flow Models Movie Gen: A Cast of Media Foundation Models

Reference 10

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source=pdf_text observed=2026-08-15T22:12:32.838090Z digest=sha256:bf6a4d5ea00392c14edf345180a25996961fcc9f1b90cc280f76bd2d026aaf70

Observation 8c436648-c066-4e7d-bf43-27b29dadef12 · outbound

This paper cites Voicebox: Text-Guided Multilingual Universal Speech Generation at Scale.

Improving Trajectory Stitching with Flow Models Voicebox: Text-Guided Multilingual Universal Speech Generation at Scale

Reference 11

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source=pdf_text observed=2026-08-15T22:12:32.845011Z digest=sha256:5043f4886dddc3e6fc03cf1ebb7a5c06fd8b253fd05ce4eb7b5e9f251c05e73d

Observation d22d94e8-7ac1-45dd-994b-6a1826e3ffc9 · outbound

This paper cites $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control.

Improving Trajectory Stitching with Flow Models $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control

Reference 12

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source=pdf_text observed=2026-08-15T22:12:32.852080Z digest=sha256:b0b9f32af16abddc9ee73779f5218f090b511f521a0af53f8b579967f7374e07

Observation 549040ef-99d5-457c-ad08-050c530edc7a · outbound

This paper cites Generator Matching: Generative modeling with arbitrary Markov processes.

Improving Trajectory Stitching with Flow Models Generator Matching: Generative modeling with arbitrary Markov processes

Reference 13

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source=pdf_text observed=2026-08-15T22:12:32.855848Z digest=sha256:e26f6daed57df7330b286edf7356080fb96174ca94308c27d54dbf7e53e3707f

Observation 9e73eb43-b29e-49fc-8976-49791c297c77 · outbound

This paper cites Goal-Conditioned Imitation Learning using Score-based Diffusion Policies.

Improving Trajectory Stitching with Flow Models Goal-Conditioned Imitation Learning using Score-based Diffusion Policies

Reference 14

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source=pdf_text observed=2026-08-15T22:12:32.859682Z digest=sha256:1328570cdcb90faa4762f46889c6d183ded77c53d210c95e862f5997fc90c6fa

Observation 8586f603-e87f-48ec-903b-138709b93553 · outbound

This paper cites an unresolved cited work.

Improving Trajectory Stitching with Flow Models Unresolved cited work

Reference 15

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation f1192e9b-ba2e-474c-b8c4-81b8c3fecb2b · outbound

This paper cites Motion Planning Diffusion: Learning and Planning of Robot Motions with Diffusion Models.

Improving Trajectory Stitching with Flow Models Motion Planning Diffusion: Learning and Planning of Robot Motions with Diffusion Models

Reference 16

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source=pdf_text observed=2026-08-15T22:12:32.875722Z digest=sha256:8f40d50fba9f955f622569f981098965dd655f084211132b01710d4921355584

Observation 53a0aacf-41fd-418c-a5d0-df3bbcbeba46 · outbound

This paper cites Potential Based Diffusion Motion Planning.

Improving Trajectory Stitching with Flow Models Potential Based Diffusion Motion Planning

Reference 17

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source=pdf_text observed=2026-08-15T22:12:32.880825Z digest=sha256:eb50e9539950b38067f3da6bfa7dfb567f19a14c539b7733023a094ba492e446

Observation ba8c01ef-eef9-4050-862f-1cc958febaea · outbound

This paper cites Understanding the Effective Receptive Field in Deep Convolutional Neural Networks.

Improving Trajectory Stitching with Flow Models Understanding the Effective Receptive Field in Deep Convolutional Neural Networks

Reference 18

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source=pdf_text observed=2026-08-15T22:12:32.885586Z digest=sha256:1a99698c24883eceeb881e301f537597af3f3423bf4059eaa649572f556a0e26

Observation 38dc7ad4-1580-42f5-be72-a48f25821c2e · outbound

This paper cites Ubukata, J.

Improving Trajectory Stitching with Flow Models Ubukata, J

Reference 19

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T22:12:32.890177Z digest=sha256:281b2381ddaba61f091adb1dec5b4d62a8a39eb103fa7c759ab7bd1050f3425d

Observation c271dbb9-e047-4bd9-99a2-d5b1a60a6c52 · outbound

This paper cites Imitating Human Behaviour with Diffusion Models.

Improving Trajectory Stitching with Flow Models Imitating Human Behaviour with Diffusion Models

Reference 20

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source=pdf_text observed=2026-08-15T22:12:32.901064Z digest=sha256:fedf4b7a643d741f6c711fcf0aa963b1d2e831ff47eef305ebbb1148911a553b

Observation 42866470-12e9-481d-b5ea-e71c10cbd091 · outbound

This paper cites Diffusion Model is an Effective Planner and Data Synthesizer for Multi-Task Reinforcement Learning.

Improving Trajectory Stitching with Flow Models Diffusion Model is an Effective Planner and Data Synthesizer for Multi-Task Reinforcement Learning

Reference 21

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source=pdf_text observed=2026-08-15T22:12:32.905419Z digest=sha256:c37b9c66b4d3b1d2bde1178331549cc731537d379274b080768a42aec476ddb2

Observation 4fa9a35c-3d73-4fef-b45a-18f80c73c6c1 · outbound

This paper cites PoCo: Policy Composition from and for Heterogeneous Robot Learning.

Improving Trajectory Stitching with Flow Models PoCo: Policy Composition from and for Heterogeneous Robot Learning

Reference 22

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source=pdf_text observed=2026-08-15T22:12:32.910300Z digest=sha256:84363156be2e213090f97275527af4876e4bfad0afef155a7df3338843c6ca7a

Observation 313bb469-205f-4d51-8097-f8755c947432 · outbound

This paper cites DiffuseLoco: Real-Time Legged Locomotion Control with Diffusion from Offline Datasets.

Improving Trajectory Stitching with Flow Models DiffuseLoco: Real-Time Legged Locomotion Control with Diffusion from Offline Datasets

Reference 23

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source=pdf_text observed=2026-08-15T22:12:32.914595Z digest=sha256:dbae51a22ee0468c97043b59f396983e00d1caebb7dc1fba7a470f77a9292bc2

Observation b6ff2400-8dbf-409b-9a6b-6b467a6c0a67 · outbound

This paper cites O’Mahoney, A.

Improving Trajectory Stitching with Flow Models O’Mahoney, A

Reference 24

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T22:12:32.918182Z digest=sha256:a69bdbb5828dc02d02caf42cbaa4e2fae46850c460e811ce0e7cd8b5c6168591

Observation 7c49b880-2337-4b83-9345-c94b069917cd · outbound

This paper cites Refining Diffusion Planner for Reliable Behavior Synthesis by Automatic Detection of Infeasible Plans.

Improving Trajectory Stitching with Flow Models Refining Diffusion Planner for Reliable Behavior Synthesis by Automatic Detection of Infeasible Plans

Reference 25

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source=pdf_text observed=2026-08-15T22:12:32.921975Z digest=sha256:fef5c2208df4f8762b204e395e532c4bfdeba179a34d055a4d409f6fac4902fb

Observation be772153-61d4-4e6a-b952-0f36c580fa84 · outbound

This paper cites DiffStitch: Boosting Offline Reinforcement Learning with Diffusion-based Trajectory Stitching.

Improving Trajectory Stitching with Flow Models DiffStitch: Boosting Offline Reinforcement Learning with Diffusion-based Trajectory Stitching

Reference 26

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source=pdf_text observed=2026-08-15T22:12:32.926123Z digest=sha256:b9909c72be42061734b61446a29672afd2bcd3aa106b761b766a2652268e174a

Observation cb1e2bfa-894b-4372-a49e-55c0f256fb37 · outbound

This paper cites an unresolved cited work.

Improving Trajectory Stitching with Flow Models Unresolved cited work

Reference 27

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T22:12:32.930812Z digest=sha256:b0dd9a40864324bd240aadae793ca96bc7054adcd0d95dd961b8231f4629f3ca

Observation b18e1dc7-1bbc-4348-811d-f624f2f353d0 · outbound

This paper cites GTA: Generative Trajectory Augmentation with Guidance for Offline Reinforcement Learning.

Improving Trajectory Stitching with Flow Models GTA: Generative Trajectory Augmentation with Guidance for Offline Reinforcement Learning

Reference 28

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source=pdf_text observed=2026-08-15T22:12:32.934772Z digest=sha256:c9f483f9e20d350885be53a5ca4d7c8c6e1235f8a61335b09ce48e580a3c32d5

Observation 61952b64-2272-4190-920d-52f9041da51d · outbound

This paper cites Generative Trajectory Stitching through Diffusion Composition.

Improving Trajectory Stitching with Flow Models Generative Trajectory Stitching through Diffusion Composition

Reference 29

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source=pdf_text observed=2026-08-15T22:12:32.939362Z digest=sha256:25bf96bc1835cac8c55925006faed9df5ddd1d55dbd21e872a8a35b822968561

Observation 2cb9de5a-7676-4626-b4f4-8808ee995b33 · outbound

This paper cites BATS: Best Action Trajectory Stitching.

Improving Trajectory Stitching with Flow Models BATS: Best Action Trajectory Stitching

Reference 30

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source=pdf_text observed=2026-08-15T22:12:32.943423Z digest=sha256:15ff21dab35fa6098e3b6270cb0d5ec00000f64e47940cda642104f37d333dd6

Observation 4fa9f5c0-f7f3-408f-9365-107d0593b907 · outbound

This paper cites Model-based Trajectory Stitching for Improved Offline Reinforcement Learning.

Improving Trajectory Stitching with Flow Models Model-based Trajectory Stitching for Improved Offline Reinforcement Learning

Reference 31

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source=pdf_text observed=2026-08-15T22:12:32.948742Z digest=sha256:d59a1184bc4f36a513470bd9d11c6339f6a0882d64c6965fb415031e7390b206

Observation 01ffe2e1-838f-4c27-b1d0-af71d17cc938 · outbound

This paper cites Elastic Decision Transformer.

Improving Trajectory Stitching with Flow Models Elastic Decision Transformer

Reference 32

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source=pdf_text observed=2026-08-15T22:12:32.952791Z digest=sha256:5491d7a0a5935ecfbc64b146cacde302048ea58657f1040c3411f48fbdd284ec

Observation 9abbccf5-5bba-4155-897d-635878dc40c3 · outbound

This paper cites Flow Matching Guide and Code.

Improving Trajectory Stitching with Flow Models Flow Matching Guide and Code

Reference 33

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source=pdf_text observed=2026-08-15T22:12:32.957487Z digest=sha256:b88143aee705564b1a1f76dc565670ee9adf3d21637a9229df505295d5ff2caa

Observation 61a4a9d3-3176-428d-98df-63b60f3c002c · outbound

This paper cites Flow Matching for Generative Modeling.

Improving Trajectory Stitching with Flow Models Flow Matching for Generative Modeling

Reference 34

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source=pdf_text observed=2026-08-15T22:12:32.962835Z digest=sha256:b9a6050fd50c5200207f90f4a42f8f279cfdb795ff254e529130c03ebe73777e

Observation 408b9fb5-3701-4249-bec9-87c13fde506d · outbound

This paper cites GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models.

Improving Trajectory Stitching with Flow Models GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models

Reference 35

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source=pdf_text observed=2026-08-15T22:12:32.967698Z digest=sha256:c1b262e167972486d66430b1b1f453c97690f93e6498055fca0f73855906eaf5

Observation 3cbf58b7-7640-4633-91cd-c8e197b3fd71 · outbound

This paper cites Score-Based Generative Modeling through Stochastic Differential Equations.

Improving Trajectory Stitching with Flow Models Score-Based Generative Modeling through Stochastic Differential Equations

Reference 36

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source=pdf_text observed=2026-08-15T22:12:32.972035Z digest=sha256:3fa8b08f8607fc3f33f717e32f180b05898ae8b2f04623930875536bccf9a032

Observation be3b9794-8374-4680-9267-c3fd21ea994a · outbound

This paper cites Diffusion Models Beat GANs on Image Synthesis.

Improving Trajectory Stitching with Flow Models Diffusion Models Beat GANs on Image Synthesis

Reference 37

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source=pdf_text observed=2026-08-15T22:12:32.976165Z digest=sha256:ad4937e833ed273c7059e5b8b82bcc12b893876b52ac2e5d55588eb9ffccf9ef

Observation 990da754-c085-4e60-8d8d-1da8b8df54f9 · outbound

This paper cites Zheng, M.

Improving Trajectory Stitching with Flow Models Zheng, M

Reference 38

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raw_fallback, observed 2026-08-15T22:12:33.503754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T22:12:32.980017Z digest=sha256:79aad64a3532f102a155d23dba11c87e7cd83a82b06da5f6272d74877bb37597

Observation 721a19f7-1558-4b41-8484-9021cbed3c7d · outbound

This paper cites Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World.

Improving Trajectory Stitching with Flow Models Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World

Reference 39

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation d64ed428-fb91-44e9-b2ec-b3558ad3722f · outbound

This paper cites reliably avoid.

Improving Trajectory Stitching with Flow Models reliably avoid

Reference 40

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unresolved
no resolver link, observed 2026-08-15T22:12:32.988682Z

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source=pdf_text observed=2026-08-15T22:12:32.988682Z digest=sha256:28c50c104bf0aba9206948bfce03dac12636451a5b10d6302604da29627fd366

Observation 74b38b5d-cf68-4622-a3ea-3d87ad14ddf1 · outbound

This paper cites an unresolved cited work.

Improving Trajectory Stitching with Flow Models Unresolved cited work

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-15T22:12:32.870429Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T22:12:32.870429Z digest=sha256:22b0ea54b9d8db6615bede47d1fbd4bf74fadee43d6cfb7aea85ea925b39f1fb

Observation 92711c66-1876-4e24-b338-c699bfc72a9a · outbound

This paper cites Diffusion Model for Planning: A Systematic Literature Review.

Improving Trajectory Stitching with Flow Models Diffusion Model for Planning: A Systematic Literature Review

Reference 2024

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unresolved
no resolver link, observed 2026-08-15T22:12:32.895140Z

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Unavailable: canonical work link unavailable.

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Pith citing papers

No inbound Pith citation observations are available.