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Paper Citation Record · LEDGER

FlowQ: Energy-Guided Flow Policies for Offline Reinforcement Learning

As of 7 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 9 inbound Pith citation observations for arXiv:2505.14139.

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

pith.paper-citation-record.v1
2505.14139 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:42:51.385352Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T00:32:46.531563Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T00:07:27.693869Z

Reference resolution

24 of 24 outbound references displayed

  • verified exact3
  • verified fuzzy1
  • unresolved20
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f42ccfc9-013a-4bc6-bd91-727df8aa24da · outbound

This paper cites Figure 5: Normalized return of FlowQ for the D4RL adroit environments using 5 seeds.

FlowQ: Energy-Guided Flow Policies for Offline Reinforcement Learning Figure 5: Normalized return of FlowQ for the D4RL adroit environments using 5 seeds

Reference 1

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raw_fallback, observed 2026-08-07T15:42:53.191140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T15:42:51.385352Z digest=sha256:792f2a8126ff5f7ae77fc3071c5bc252e58ba58dffe2d985f5310003c6de1fd9

Observation 3eb51ae0-f6dd-43b4-8f03-d419b8f68cdf · outbound

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

FlowQ: Energy-Guided Flow Policies for Offline Reinforcement Learning Scaling Rectified Flow Transformers for High-Resolution Image Synthesis

Reference 3

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source=pdf_text observed=2026-08-07T15:42:49.306851Z digest=sha256:42a2f6ca8876ba953065325349fa3d7defb31c12879bfab12c36e982593cc302

Observation ca30f5ef-759b-47ad-97b6-76f6c65d8374 · outbound

This paper cites Diffusion Actor-Critic: Formulating Constrained Policy Iteration as Diffusion Noise Regression for Offline Reinforcement Learning.

FlowQ: Energy-Guided Flow Policies for Offline Reinforcement Learning Diffusion Actor-Critic: Formulating Constrained Policy Iteration as Diffusion Noise Regression for Offline Reinforcement Learning

Reference 4

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source=pdf_text observed=2026-08-07T15:42:49.396954Z digest=sha256:fe221609fea9fcf179fe9d3aba3834947ad5ecf61f2e9268a1a945ab5458fe96

Observation d918cf25-0e55-4308-86f0-0e6d2bc130c4 · outbound

This paper cites an unresolved cited work.

FlowQ: Energy-Guided Flow Policies for Offline Reinforcement Learning Unresolved cited work

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T15:42:51.250242Z digest=sha256:fcab478bd6056ccdbb0b6e6bd925074039347abde0c46148c0c04f3a9182280a

Observation 2bbb50df-d400-456e-8af2-26c74709b86c · outbound

This paper cites Denoising Diffusion Probabilistic Models.

FlowQ: Energy-Guided Flow Policies for Offline Reinforcement Learning Denoising Diffusion Probabilistic Models

Reference 9

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source=pdf_text observed=2026-08-07T15:42:49.782023Z digest=sha256:a2fc00e08ee33316acc822ce12a7065a98f434977dd93fdd7dc652db10ac2d48

Observation 3f9416a0-23a7-464b-9886-546706d16168 · outbound

This paper cites Offline Reinforcement Learning with Implicit Q-Learning.

FlowQ: Energy-Guided Flow Policies for Offline Reinforcement Learning Offline Reinforcement Learning with Implicit Q-Learning

Reference 12

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source=pdf_text observed=2026-08-07T15:42:50.185601Z digest=sha256:d198f50cb3404b45a1551c8110b7cca01e24a082eaa88f748e368596edf25d87

Observation f4f97b07-d29c-440e-9072-8bd97f6c68f0 · outbound

This paper cites an unresolved cited work.

FlowQ: Energy-Guided Flow Policies for Offline Reinforcement Learning Unresolved cited work

Reference 13

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source=pdf_text observed=2026-08-07T15:42:50.230127Z digest=sha256:7f3418c4c939d3bbe45e2ccd6941fa65e85c56fa41d933f06922f64f0c4882c5

Observation 5ab699ca-464f-46fe-a9f0-25cc41be7228 · outbound

This paper cites Flow Matching for Generative Modeling.

FlowQ: Energy-Guided Flow Policies for Offline Reinforcement Learning Flow Matching for Generative Modeling

Reference 14

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source=pdf_text observed=2026-08-07T15:42:50.314458Z digest=sha256:a6718d30d63cebb731ec433e6c44341b08bdd73d227feca7832d04b9569a2896

Observation ccf2fe4e-d7b2-4ccc-9501-c01f458b10c6 · outbound

This paper cites Mish: A Self Regularized Non-Monotonic Activation Function.

FlowQ: Energy-Guided Flow Policies for Offline Reinforcement Learning Mish: A Self Regularized Non-Monotonic Activation Function

Reference 16

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source=pdf_text observed=2026-08-07T15:42:50.478554Z digest=sha256:7679d06dac8da7012fe0eb6809e0e71809af74a73b5c5f6acbd89951b3440ab2

Observation f6a33093-fd25-4670-ac65-277f2c2119fc · outbound

This paper cites Offline Reinforcement Learning from Images with Latent Space Models.

FlowQ: Energy-Guided Flow Policies for Offline Reinforcement Learning Offline Reinforcement Learning from Images with Latent Space Models

Reference 18

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local_arxiv, observed 2026-08-07T15:42:52.081987Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T15:42:50.697409Z digest=sha256:bc3a6eff3ccb768b655ef43d1369e252c6159438418ba453233ea4873ef57189

Observation a72f762f-edd0-4573-b06c-85b28c7a4840 · outbound

This paper cites Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning.

FlowQ: Energy-Guided Flow Policies for Offline Reinforcement Learning Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning

Reference 19

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source=pdf_text observed=2026-08-07T15:42:50.747484Z digest=sha256:0595b7a157569dd2e898933a3aa78d678fce011b54534f32c5be8e92aacfd12c

Observation 17afe07b-bccf-4af8-a79f-d6f9e52682bd · outbound

This paper cites Critic Regularized Regression.

FlowQ: Energy-Guided Flow Policies for Offline Reinforcement Learning Critic Regularized Regression

Reference 20

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source=pdf_text observed=2026-08-07T15:42:50.880303Z digest=sha256:efce42194581111e5f8b663be7f784c2be2300f898c43c16cf250052671c8cf8

Observation 502d693a-a891-43e7-bbff-f76c055b7b81 · outbound

This paper cites MOPO: Model-based Offline Policy Optimization.

FlowQ: Energy-Guided Flow Policies for Offline Reinforcement Learning MOPO: Model-based Offline Policy Optimization

Reference 21

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source=pdf_text observed=2026-08-07T15:42:50.991836Z digest=sha256:b8da34efbab70e1be367956f91f4da171f8bea8937c959c68f9e4577c382c5df

Observation 1d271efa-623e-408e-b0e4-e9b8cec62007 · outbound

This paper cites Guided Flows for Generative Modeling and Decision Making.

FlowQ: Energy-Guided Flow Policies for Offline Reinforcement Learning Guided Flows for Generative Modeling and Decision Making

Reference 22

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source=pdf_text observed=2026-08-07T15:42:51.079534Z digest=sha256:fc4c5075461211394dc914d8e404ad3ff2cecdf6fe8d15e3615b9531efd69c36

Observation af01259c-5016-4578-a3d6-c356b6d41965 · outbound

This paper cites PLAS: Latent Action Space for Offline Reinforcement Learning.

FlowQ: Energy-Guided Flow Policies for Offline Reinforcement Learning PLAS: Latent Action Space for Offline Reinforcement Learning

Reference 23

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local_arxiv, observed 2026-08-07T15:42:51.700274Z

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source=pdf_text observed=2026-08-07T15:42:51.181002Z digest=sha256:78958c8586299155e490493b7661757cf5d5143f8a62c3f87d6c47bf5d7030de

Observation 59abff0a-f10e-4b17-94a5-2c47107fb8ce · outbound

This paper cites Gaussian Error Linear Units (GELUs).

FlowQ: Energy-Guided Flow Policies for Offline Reinforcement Learning Gaussian Error Linear Units (GELUs)

Reference 2016

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source=pdf_text observed=2026-08-07T15:42:49.723240Z digest=sha256:702d5f8cd180fdb8a880e3478099fcfc72f2ff7bedb5b65ed0aa5a4bda8daaa4

Observation 5b69c49b-924d-46e2-ab2e-3f2b5d10b17d · outbound

This paper cites Adam: A Method for Stochastic Optimization.

FlowQ: Energy-Guided Flow Policies for Offline Reinforcement Learning Adam: A Method for Stochastic Optimization

Reference 2017

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source=pdf_text observed=2026-08-07T15:42:50.038962Z digest=sha256:46e21eaac29e2faa7f8bcc72b35ab1a8236bf7752cb42bd3e3c15f691c5b8f6c

Observation 832ce9c9-889f-4ad2-9cb1-0298b845ddf0 · outbound

This paper cites Off-Policy Deep Reinforcement Learning without Exploration.

FlowQ: Energy-Guided Flow Policies for Offline Reinforcement Learning Off-Policy Deep Reinforcement Learning without Exploration

Reference 2018

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source=pdf_text observed=2026-08-07T15:42:49.667242Z digest=sha256:a659219ca8230678298c5b7e374f1b20867c307b19d8b8fb74a02aa15b84de85

Observation 6ad074b3-a6f5-4c12-80f5-3bfab2aa636f · outbound

This paper cites D4RL: Datasets for Deep Data-Driven Reinforcement Learning.

FlowQ: Energy-Guided Flow Policies for Offline Reinforcement Learning D4RL: Datasets for Deep Data-Driven Reinforcement Learning

Reference 2020

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source=pdf_text observed=2026-08-07T15:42:49.466319Z digest=sha256:4f1433f3613057ac7e4b0b79bf570f94ed3b37cca750cb7e0c8b66b3b203438d

Observation 5d26abf5-f678-4528-b515-ff9311f8250f · outbound

This paper cites A Minimalist Approach to Offline Reinforcement Learning.

FlowQ: Energy-Guided Flow Policies for Offline Reinforcement Learning A Minimalist Approach to Offline Reinforcement Learning

Reference 2021

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source=pdf_text observed=2026-08-07T15:42:49.563459Z digest=sha256:12b70629f082db9b3533135312006c12cf51a34aa2fe40508c285782fe72611d

Observation 54ffd1f8-04c3-4452-b03d-54da3fb97846 · outbound

This paper cites DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps.

FlowQ: Energy-Guided Flow Policies for Offline Reinforcement Learning DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps

Reference 2022

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source=pdf_text observed=2026-08-07T15:42:50.432520Z digest=sha256:bfc062077ca531816a744770734c0d477779b1141035a30b5d8a39602d722e46

Observation f03702ea-474c-4204-8a39-dadc3920baeb · outbound

This paper cites Efficient Diffusion Policies for Offline Reinforcement Learning.

FlowQ: Energy-Guided Flow Policies for Offline Reinforcement Learning Efficient Diffusion Policies for Offline Reinforcement Learning

Reference 2023

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source=pdf_text observed=2026-08-07T15:42:49.907562Z digest=sha256:479cd3ad11fc2f9451721e45341d6a42335e1104e59361d45bb43123ee58437f

Observation 95aabe8e-02c7-4362-98ad-c9865fc5b5cd · outbound

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

FlowQ: Energy-Guided Flow Policies for Offline Reinforcement Learning $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control

Reference 2024

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Observation 438fe611-de93-454e-8dc2-8f5980d3e5ae · outbound

This paper cites an unresolved cited work.

FlowQ: Energy-Guided Flow Policies for Offline Reinforcement Learning Unresolved cited work

Reference 2025

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source=pdf_text observed=2026-08-07T15:42:49.151820Z digest=sha256:b958c3d22afe826e55c412030cfb26ac1764ef0cfba8cde9aed3b86f12073e31

Pith citing papers

Observation e874a85f-4a32-4f77-a3f8-c478d703a54b · inbound

Action Emergence from Streaming Intent cites this paper.

Action Emergence from Streaming Intent FlowQ: Energy-Guided Flow Policies for Offline Reinforcement Learning

Reference 57

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arxiv_id, observed 2026-05-14T21:02:59.187795Z

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source=arxiv_source observed=2026-05-14T20:59:42.456958Z digest=sha256:3bac1f97afda5ad4ed060ce2fe595b0c01910a1fa0a2834b246eb15bfcb42e74

Observation fe907729-1795-43fd-91f1-a78dfba98a68 · inbound

Action Emergence from Streaming Intent cites this paper.

Action Emergence from Streaming Intent FlowQ: Energy-Guided Flow Policies for Offline Reinforcement Learning

Reference 57

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arxiv_id, observed 2026-05-15T05:15:02.985480Z

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source=arxiv_source observed=2026-05-15T05:13:44.795483Z digest=sha256:42479a78b8a319056871ab1e253122f86f12137e1b4e913adc9b16f90776dac6

Observation 62d7a1be-0619-455e-9ff5-92a6f45ac329 · inbound

Driving Intents Amplify Planning-Oriented Reinforcement Learning cites this paper.

Driving Intents Amplify Planning-Oriented Reinforcement Learning FlowQ: Energy-Guided Flow Policies for Offline Reinforcement Learning

Reference 1

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arxiv_id, observed 2026-05-14T20:52:58.729137Z

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source=pdf_text observed=2026-05-14T20:50:14.602822Z digest=sha256:a2c8db1d2dab584f0b4dc548895de5bb74adcb20c2d29299c19d03160682fe7f

Observation 98741778-00e8-48ec-8acf-970f908a4d18 · inbound

Driving Intents Amplify Planning-Oriented Reinforcement Learning cites this paper.

Driving Intents Amplify Planning-Oriented Reinforcement Learning FlowQ: Energy-Guided Flow Policies for Offline Reinforcement Learning

Reference 1

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arxiv_id, observed 2026-05-15T04:59:45.526358Z

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source=pdf_text observed=2026-05-15T04:58:07.943615Z digest=sha256:2d1e8970c080c74083956c8e04f835775f865c9a655488c4dcaace6c3004c3d3

Observation fd7edf9d-bae1-44d5-bba4-deb47f1c9f52 · inbound

Reinforcement Learning for Flow-Matching Policies with Density Transport cites this paper.

Reinforcement Learning for Flow-Matching Policies with Density Transport FlowQ: Energy-Guided Flow Policies for Offline Reinforcement Learning

Reference 1

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arxiv_id, observed 2026-07-02T22:27:25.812719Z

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source=pdf_text observed=2026-06-27T18:55:02.040180Z digest=sha256:b254c140a24b58b05b6cc41c9e7a462a1dbff21a473a98f0402db99f238757f3

Observation 96d8a000-fbb2-4097-9f99-a61a5978c4eb · inbound

Counterfactual Transport Flows for Offline Conservative Trajectory Refinement cites this paper.

Counterfactual Transport Flows for Offline Conservative Trajectory Refinement FlowQ: Energy-Guided Flow Policies for Offline Reinforcement Learning

Reference 88

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source=arxiv_source observed=2026-06-27T17:33:35.857240Z digest=sha256:c649f90148868b02d9672a7099b1873ddfdacf4ce6d228114c62f6ec31740ca7

Observation 01131813-7290-4a4f-9c57-d9c6c107e908 · inbound

ConFlow: Constraints-Guided Learning with Flow Matching for Motion Generation cites this paper.

ConFlow: Constraints-Guided Learning with Flow Matching for Motion Generation FlowQ: Energy-Guided Flow Policies for Offline Reinforcement Learning

Reference 1

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source=pdf_text observed=2026-08-02T02:10:24.308093Z digest=sha256:fa12252ad1d302a08b29c5255e695b3ad27bf49e8ca99865e1e09b0dfd649962

Observation 7a705d2d-e3e4-42e3-aacb-279a33792de1 · inbound

Collaborative Weighting with Pessimistic Critic for Mitigating Overestimation in Off-Policy Reinforcement Learning cites this paper.

Collaborative Weighting with Pessimistic Critic for Mitigating Overestimation in Off-Policy Reinforcement Learning FlowQ: Energy-Guided Flow Policies for Offline Reinforcement Learning

Reference 35

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source=pdf_text observed=2026-08-01T14:19:59.991253Z digest=sha256:309859f46a44c1b3ffa7c3654260307911cb723462cbdbbf090a74f36ac4f745

Observation 60624e1d-49e3-480b-8fb9-cd732ec8b0e6 · inbound

ReBRAC-v2: The Return of the King cites this paper.

ReBRAC-v2: The Return of the King FlowQ: Energy-Guided Flow Policies for Offline Reinforcement Learning

Reference 79

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source=arxiv_source observed=2026-08-06T00:32:46.531563Z digest=sha256:b3e11faa056fc8f8f4b347c6dcbabd5a7ecb71cb6a6f1085e42dc6801a8abbdb