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Paper Citation Record · LEDGER

Let Offline RL Flow: Training Conservative Agents in the Latent Space of Normalizing Flows

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2211.11096.

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

pith.paper-citation-record.v1
2211.11096 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 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 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:20:35.925222Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T23:47:28.409712Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation de253d64-9fad-452f-b44c-a5243b4d6ea9 · inbound

Decision Flow Policy Optimization cites this paper.

Decision Flow Policy Optimization Let Offline RL Flow: Training Conservative Agents in the Latent Space of Normalizing Flows

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-07T14:20:35.925222Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:20:35.925222Z digest=sha256:9837282cb9cbbae2652edd71afd48c747df9141033bac3acf07e2f7d7201ba51

Observation ff8531ab-2cb3-4fca-8ab8-1d5e4f266ea9 · inbound

Training Diffusion Policies via Prior-Mapping Co-Evolution cites this paper.

Training Diffusion Policies via Prior-Mapping Co-Evolution Let Offline RL Flow: Training Conservative Agents in the Latent Space of Normalizing Flows

Reference 2017

Resolution
unresolved
no resolver link, observed 2026-08-03T19:03:02.628280Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T19:03:02.628280Z digest=sha256:48635787f3de8ef71c646e3e16b4db540955aae55eb31317a630e71ec0731def

Observation 78c7792b-5f42-4e4e-9e2b-f1fa0c3933c7 · inbound

SERNF: Sample-Efficient Real-World Dexterous Policy Fine-Tuning via Action-Chunked Critics and Normalizing Flows cites this paper.

SERNF: Sample-Efficient Real-World Dexterous Policy Fine-Tuning via Action-Chunked Critics and Normalizing Flows Let Offline RL Flow: Training Conservative Agents in the Latent Space of Normalizing Flows

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-05-16T03:37:13.814008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-16T03:36:09.272019Z digest=sha256:77c12ee6d165b2eaadefb60b8d0262608ac29cd8f6585f0e5bc928a4d9d52599

Observation 71f5a7c8-9934-4277-96bb-3ad95448799d · inbound

SERNF: Sample-Efficient Real-World Dexterous Policy Fine-Tuning via Action-Chunked Critics and Normalizing Flows cites this paper.

SERNF: Sample-Efficient Real-World Dexterous Policy Fine-Tuning via Action-Chunked Critics and Normalizing Flows Let Offline RL Flow: Training Conservative Agents in the Latent Space of Normalizing Flows

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-03T02:53:09.350819Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T02:53:09.350819Z digest=sha256:fdb5d946834dc3aa507973f97f6d7f52014f9f7fc1d4c6386e300a7c66b22afe

Observation 5da9fbb1-23a1-4d1c-9df8-67cd4e55fdf5 · inbound

Generative OOD-regularized Model-based Policy Optimization cites this paper.

Generative OOD-regularized Model-based Policy Optimization Let Offline RL Flow: Training Conservative Agents in the Latent Space of Normalizing Flows

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-06-30T14:44:45.097653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-30T14:42:38.148642Z digest=sha256:f7da2fbc67c7f4398120fe45124d7e146212ab33c245e8dc45a6ded804a769a3

Observation 7104e2f7-a938-47a8-9c59-33fb91c43a42 · inbound

Some Essential Constructive Foundations for Systems and Control cites this paper.

Some Essential Constructive Foundations for Systems and Control Let Offline RL Flow: Training Conservative Agents in the Latent Space of Normalizing Flows

Reference 9

Resolution
metadata mismatch
arxiv_id, observed 2026-07-02T23:47:28.411176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-06-27T17:48:35.402903Z digest=sha256:e33400da58d76bf1371b84bcc43f91ac5e29732820690bed79d58db1bf2afc7b

Observation 44c91a74-0eaa-4cbc-8e61-3e2b5723c5f1 · inbound

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

ReBRAC-v2: The Return of the King Let Offline RL Flow: Training Conservative Agents in the Latent Space of Normalizing Flows

Reference 70

Resolution
unresolved
no resolver link, observed 2026-08-06T00:32:46.057727Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T00:32:46.057727Z digest=sha256:1712db6c212163987966fb95302fadda8514ca6b3c3a4acb7c19f9cf9ef84ab9