Paper Citation Record · LEDGER

World Models via Policy-Guided Trajectory Diffusion

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2312.08533.

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

pith.paper-citation-record.v1
2312.08533 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T21:02:04.314098Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

2
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 663e197e-a569-400a-8227-212c98c82aeb · inbound

Diffusion Policy Policy Optimization cites this paper.

Diffusion Policy Policy Optimization World Models via Policy-Guided Trajectory Diffusion

Reference 79

Resolution: verified exact; arxiv_id, observed 2026-05-16T08:48:14.987049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c53049be-15db-4a3c-acdf-fbe05de42a8a · inbound

Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges cites this paper.

Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges World Models via Policy-Guided Trajectory Diffusion

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-05T21:02:04.314098Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ba367bc1-98a2-4858-834a-7ef6aadec38d · inbound

DAWM: Diffusion Action World Models for Offline Reinforcement Learning via Action-Inferred Transitions cites this paper.

DAWM: Diffusion Action World Models for Offline Reinforcement Learning via Action-Inferred Transitions World Models via Policy-Guided Trajectory Diffusion

Reference 22

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T13:56:26.226868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1d67e35f-98eb-4d8e-ab9c-9bbc8c8d3107 · inbound

From Static Constraints to Dynamic Adaptation: Sample-Level Constraint Relaxation for Offline-to-Online Reinforcement Learning cites this paper.

From Static Constraints to Dynamic Adaptation: Sample-Level Constraint Relaxation for Offline-to-Online Reinforcement Learning World Models via Policy-Guided Trajectory Diffusion

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-18T00:40:34.131233Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 96158358-d647-4462-8785-bcd2e5326d3f · inbound

From Static Constraints to Dynamic Adaptation: Sample-Level Constraint Relaxation for Offline-to-Online Reinforcement Learning cites this paper.

From Static Constraints to Dynamic Adaptation: Sample-Level Constraint Relaxation for Offline-to-Online Reinforcement Learning World Models via Policy-Guided Trajectory Diffusion

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-21T19:04:19.115747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3d769411-9fd2-4b11-b731-03eb4939f7a7 · inbound

Multimodal Diffusion Forcing for Forceful Manipulation cites this paper.

Multimodal Diffusion Forcing for Forceful Manipulation World Models via Policy-Guided Trajectory Diffusion

Reference 12

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T00:35:32.988184Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4ccf9485-0f2c-4e38-a837-f15ab6936aca · inbound

Advantage-Guided Diffusion for Model-Based Reinforcement Learning cites this paper.

Advantage-Guided Diffusion for Model-Based Reinforcement Learning World Models via Policy-Guided Trajectory Diffusion

Reference 12

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T07:01:00.651669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2fad442b-2f1d-465f-adcf-cf94fd02a86b · inbound

Global Convergence of Sampling-Based Nonconvex Optimization through Diffusion-Style Smoothing cites this paper.

Global Convergence of Sampling-Based Nonconvex Optimization through Diffusion-Style Smoothing World Models via Policy-Guided Trajectory Diffusion

Reference 182

Resolution: verified exact; arxiv_id, observed 2026-05-20T20:18:59.661711Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 91cc5beb-2451-420c-812f-86d8a14933b3 · inbound

Reinforcement Learning: From Algorithms To Foundation Models cites this paper.

Reinforcement Learning: From Algorithms To Foundation Models World Models via Policy-Guided Trajectory Diffusion

Reference 288

Resolution: unresolved; no resolver link, observed 2026-08-01T17:45:24.039793Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.