Paper Citation Record · LEDGER

WM-DAgger: Enabling Efficient Data Aggregation for Imitation Learning with World Models

As of 14 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 4 inbound Pith citation observations for arXiv:2604.11351.

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

pith.paper-citation-record.v1
2604.11351 v1

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-10T16:30:40.064714Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T09:43:33.065347Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-02T13:36:59.321852Z

Reference resolution

26 of 26 outbound references displayed

verified exact13
verified fuzzy13
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c2c94a3d-8272-4022-b7c7-308c5ef8b000 · outbound

This paper cites Is imitation learning the route to humanoid robots?.

WM-DAgger: Enabling Efficient Data Aggregation for Imitation Learning with World Models Is imitation learning the route to humanoid robots?

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:01:53.576250Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 475a0a24-9a95-4044-ae06-a764fa359670 · outbound

This paper cites A survey of imitation learning: Algorithms, recent developments, and challenges.

WM-DAgger: Enabling Efficient Data Aggregation for Imitation Learning with World Models A survey of imitation learning: Algorithms, recent developments, and challenges

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:01:53.564044Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 64c2a711-5f93-451a-a144-abec20ee06f2 · outbound

This paper cites Towards a unified understanding of robot manipulation: A comprehensive survey.

WM-DAgger: Enabling Efficient Data Aggregation for Imitation Learning with World Models Towards a unified understanding of robot manipulation: A comprehensive survey

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-11T08:45:59.061103Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5a9eacaf-d80e-44e8-b4d3-9de9eae6d4c1 · outbound

This paper cites A reduction of imitation learning and structured prediction to no-regret online learning.

WM-DAgger: Enabling Efficient Data Aggregation for Imitation Learning with World Models A reduction of imitation learning and structured prediction to no-regret online learning

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:01:53.578923Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0075ba1a-467d-4278-8afe-4072da09a9ae · outbound

This paper cites Hg-dagger: Interactive imitation learning with human experts.

WM-DAgger: Enabling Efficient Data Aggregation for Imitation Learning with World Models Hg-dagger: Interactive imitation learning with human experts

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:01:53.582047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8338e1ba-34d9-4530-a48c-1594dcae908e · outbound

This paper cites Diffusion Meets DAgger: Supercharging Eye-in-hand Imitation Learning.

WM-DAgger: Enabling Efficient Data Aggregation for Imitation Learning with World Models Diffusion Meets DAgger: Supercharging Eye-in-hand Imitation Learning

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-11T08:45:59.087107Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8aec545e-44fb-4651-9588-c9e1fb5eb53c · outbound

This paper cites Understanding world or predicting future? a comprehensive survey of world models.

WM-DAgger: Enabling Efficient Data Aggregation for Imitation Learning with World Models Understanding world or predicting future? a comprehensive survey of world models

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:01:53.570346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7731cc34-d158-4116-bba0-93eccad24b1f · outbound

This paper cites Simworld: A unified benchmark for simulator-conditioned scene generation via world model.

WM-DAgger: Enabling Efficient Data Aggregation for Imitation Learning with World Models Simworld: A unified benchmark for simulator-conditioned scene generation via world model

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:01:53.573246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 601abcb5-0866-48af-9a4e-7c3289303936 · outbound

This paper cites A Comprehensive Survey on World Models for Embodied AI.

WM-DAgger: Enabling Efficient Data Aggregation for Imitation Learning with World Models A Comprehensive Survey on World Models for Embodied AI

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-06-29T01:14:24.976532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7f305cfe-a1e2-43ed-9384-df251ae30f67 · outbound

This paper cites Cosmos World Foundation Model Platform for Physical AI.

WM-DAgger: Enabling Efficient Data Aggregation for Imitation Learning with World Models Cosmos World Foundation Model Platform for Physical AI

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-05-11T08:45:59.010000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ef5c8da0-e280-45c9-a493-1044469dc283 · outbound

This paper cites Extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations.

WM-DAgger: Enabling Efficient Data Aggregation for Imitation Learning with World Models Extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:01:53.561274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 84e9ae9b-7dea-4fce-a49d-f65cb067d3b3 · outbound

This paper cites Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots.

WM-DAgger: Enabling Efficient Data Aggregation for Imitation Learning with World Models Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-14T21:35:33.640506Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 87217755-3495-4694-b639-e13f9029e131 · outbound

This paper cites Compliant residual dagger: Improving real-world contact-rich manipulation with human corrections.

WM-DAgger: Enabling Efficient Data Aggregation for Imitation Learning with World Models Compliant residual dagger: Improving real-world contact-rich manipulation with human corrections

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-11T08:45:59.040345Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c0e4f732-fcfb-4673-bdc3-13231c48aa5a · outbound

This paper cites Manigaussian++: General robotic bimanual manipulation with hierarchical gaussian world model.

WM-DAgger: Enabling Efficient Data Aggregation for Imitation Learning with World Models Manigaussian++: General robotic bimanual manipulation with hierarchical gaussian world model

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:01:53.567350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 55e3fdbc-fc62-4d6c-b36c-cb9826fc9104 · outbound

This paper cites Mastering Diverse Domains through World Models.

WM-DAgger: Enabling Efficient Data Aggregation for Imitation Learning with World Models Mastering Diverse Domains through World Models

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-11T09:08:22.800194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ae3b5dc8-e546-460a-942f-ea411ce9e062 · outbound

This paper cites Day- dreamer: World models for physical robot learning.

WM-DAgger: Enabling Efficient Data Aggregation for Imitation Learning with World Models Day- dreamer: World models for physical robot learning

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:01:53.584666Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d0fd3f2b-f699-4630-a66b-1b19207bb0a7 · outbound

This paper cites World4rl: Diffusion world models for policy refinement with reinforcement learning for robotic manipulation.

WM-DAgger: Enabling Efficient Data Aggregation for Imitation Learning with World Models World4rl: Diffusion world models for policy refinement with reinforcement learning for robotic manipulation

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-11T08:45:59.119522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation aad994b3-2489-43fe-8d24-44993cfed8a0 · outbound

This paper cites Genie Envisioner: A Unified World Foundation Platform for Robotic Manipulation.

WM-DAgger: Enabling Efficient Data Aggregation for Imitation Learning with World Models Genie Envisioner: A Unified World Foundation Platform for Robotic Manipulation

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-15T21:28:42.077022Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8e8f023a-8d5b-4d4c-9546-ec8485a33b6c · outbound

This paper cites Input-level inductive biases for 3d reconstruction.

WM-DAgger: Enabling Efficient Data Aggregation for Imitation Learning with World Models Input-level inductive biases for 3d reconstruction

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:01:53.596669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 602128f6-68ea-43f7-8b2d-360764378ff1 · outbound

This paper cites Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow.

WM-DAgger: Enabling Efficient Data Aggregation for Imitation Learning with World Models Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-05-11T08:45:59.107015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 26368a7e-3a88-4ca5-8bd1-d1d38df556f2 · outbound

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

WM-DAgger: Enabling Efficient Data Aggregation for Imitation Learning with World Models Scaling Rectified Flow Transformers for High-Resolution Image Synthesis

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-12T08:27:54.173931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation bfb47b99-5a0a-4147-b7a4-d1034137911f · outbound

This paper cites Ultravico: Breaking extrapolation limits in video diffusion transformers.

WM-DAgger: Enabling Efficient Data Aggregation for Imitation Learning with World Models Ultravico: Breaking extrapolation limits in video diffusion transformers

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-11T08:45:59.134592Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f57b5b34-2d2c-45a2-9a75-658801be5e6d · outbound

This paper cites Dinov2: Learning robust visual features without supervision.

WM-DAgger: Enabling Efficient Data Aggregation for Imitation Learning with World Models Dinov2: Learning robust visual features without supervision

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:01:53.590477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e201e5e3-5786-4f11-9b97-7a1b7cb92d67 · outbound

This paper cites Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation.

WM-DAgger: Enabling Efficient Data Aggregation for Imitation Learning with World Models Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-14T22:02:55.841967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0c26891e-2411-4242-8009-e58fb244d52c · outbound

This paper cites Htc vive tracker: accuracy for indoor localization.

WM-DAgger: Enabling Efficient Data Aggregation for Imitation Learning with World Models Htc vive tracker: accuracy for indoor localization

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:01:53.587628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c3845caf-ad2a-4f2c-8408-d9ef595d7e2c · outbound

This paper cites GR00T N1: An open foundation model for generalist humanoid robots.

WM-DAgger: Enabling Efficient Data Aggregation for Imitation Learning with World Models GR00T N1: An open foundation model for generalist humanoid robots

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:01:53.593524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

Observation b5d92695-07b2-4d4a-9032-fb5b0be830db · inbound

Towards a Data Flywheel for Embodied Intelligence in Logistics cites this paper.

Towards a Data Flywheel for Embodied Intelligence in Logistics WM-DAgger: Enabling Efficient Data Aggregation for Imitation Learning with World Models

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-07-02T13:36:59.323067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c3faff35-cd16-4d82-9613-6bc879f9703c · inbound

TACO: TActile World Model as a Self-COrrector forScalable VLA Post-Training cites this paper.

TACO: TActile World Model as a Self-COrrector forScalable VLA Post-Training WM-DAgger: Enabling Efficient Data Aggregation for Imitation Learning with World Models

Reference 29

Resolution: unresolved; no resolver link, observed 2026-07-12T06:41:57.276146Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fdcdd492-ec04-40aa-89e4-4a4384b47493 · inbound

EgoRecovery: Acquiring Failure Recovery Ability Through Human Recovery Demonstration cites this paper.

EgoRecovery: Acquiring Failure Recovery Ability Through Human Recovery Demonstration WM-DAgger: Enabling Efficient Data Aggregation for Imitation Learning with World Models

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-01T11:54:20.301562Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3072b60a-95af-4294-b517-265dcb439004 · inbound

BWM: A Low-Cost High-Fidelity World Simulator for Robot Learning cites this paper.

BWM: A Low-Cost High-Fidelity World Simulator for Robot Learning WM-DAgger: Enabling Efficient Data Aggregation for Imitation Learning with World Models

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-03T09:43:33.065347Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.