Paper Citation Record · LEDGER

Revealing the Challenges of Sim-to-Real Transfer in Model-Based Reinforcement Learning via Latent Space Modeling

As of 8 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 1 inbound Pith citation observation for arXiv:2506.12735.

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

pith.paper-citation-record.v1
2506.12735 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:49:10.893779Z

measured 37 of 37 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-27T22:26:44.624473Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T16:47:09.223705Z

Reference resolution

36 of 36 outbound references displayed

verified exact0
verified fuzzy26
unresolved10
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c557015a-4679-452e-a2f9-ef1d1fb80e41 · outbound

This paper cites Waymo Public Road Safety Performance Data.

Revealing the Challenges of Sim-to-Real Transfer in Model-Based Reinforcement Learning via Latent Space Modeling Waymo Public Road Safety Performance Data

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T00:49:06.424326Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ef0ef047-cbbc-4377-a3c5-099c3356e8fe · outbound

This paper cites CARLA: An open urban driving simulator.

Revealing the Challenges of Sim-to-Real Transfer in Model-Based Reinforcement Learning via Latent Space Modeling CARLA: An open urban driving simulator

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:17.160446Z

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.

Observation 6a825a0d-32b5-4595-bb62-6549797926bd · outbound

This paper cites When to trust your simulator: Dynamics-aware hybrid offline-and-online reinforcement learning.

Revealing the Challenges of Sim-to-Real Transfer in Model-Based Reinforcement Learning via Latent Space Modeling When to trust your simulator: Dynamics-aware hybrid offline-and-online reinforcement learning

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T00:49:06.534800Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 86cb0955-8b44-4a55-bad0-9a2f3c57ef18 · outbound

This paper cites H2O+: An Improved Framework for Hybrid Offline-and-Online RL with Dynamics Gaps.

Revealing the Challenges of Sim-to-Real Transfer in Model-Based Reinforcement Learning via Latent Space Modeling H2O+: An Improved Framework for Hybrid Offline-and-Online RL with Dynamics Gaps

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T00:49:06.586776Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 51030ad0-3886-4f7a-a68e-b0bb73226e74 · outbound

This paper cites Improving offline reinforcement learning with inaccurate simulators.

Revealing the Challenges of Sim-to-Real Transfer in Model-Based Reinforcement Learning via Latent Space Modeling Improving offline reinforcement learning with inaccurate simulators

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:16.959615Z

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.

Observation 025c0a17-5299-4c68-a3ec-99f0c07c8683 · outbound

This paper cites MIT press Cambridge, 1998.

Revealing the Challenges of Sim-to-Real Transfer in Model-Based Reinforcement Learning via Latent Space Modeling MIT press Cambridge, 1998

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T00:49:06.771354Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e1d7c0a-b693-41e0-9da7-d8849ccfbc1b · outbound

This paper cites Transfer learning for reinforcement learning domains: A survey.

Revealing the Challenges of Sim-to-Real Transfer in Model-Based Reinforcement Learning via Latent Space Modeling Transfer learning for reinforcement learning domains: A survey

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:16.776325Z

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.

Observation a3801aa7-f0e4-43f3-832d-3dc9bc874636 · outbound

This paper cites Policy invariance under reward transformations: Theory and application to reward shaping.

Revealing the Challenges of Sim-to-Real Transfer in Model-Based Reinforcement Learning via Latent Space Modeling Policy invariance under reward transformations: Theory and application to reward shaping

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:16.555293Z

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.

Observation a90a187b-bee3-441c-8781-3c3e3dfa21b8 · outbound

This paper cites Curriculum learning.

Revealing the Challenges of Sim-to-Real Transfer in Model-Based Reinforcement Learning via Latent Space Modeling Curriculum learning

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:16.390722Z

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.

Observation 89f0ddc5-e778-4d01-bdca-28be1b069e8d · outbound

This paper cites Policy distillation.

Revealing the Challenges of Sim-to-Real Transfer in Model-Based Reinforcement Learning via Latent Space Modeling Policy distillation

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:16.199062Z

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.

Observation b6497504-93ac-4806-94ca-51c0fa9ff848 · outbound

This paper cites One-shot visual imitation learning via meta-learning.

Revealing the Challenges of Sim-to-Real Transfer in Model-Based Reinforcement Learning via Latent Space Modeling One-shot visual imitation learning via meta-learning

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:15.976387Z

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.

Observation d6b9dfe8-372f-4dee-a291-7ace852a5ea2 · outbound

This paper cites Adversarial discriminative domain adaptation.

Revealing the Challenges of Sim-to-Real Transfer in Model-Based Reinforcement Learning via Latent Space Modeling Adversarial discriminative domain adaptation

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:15.783834Z

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.

Observation edb00b82-66ba-4aeb-9f13-8c73af189510 · outbound

This paper cites Sim-to-real transfer of robotic control with dynamics randomization.

Revealing the Challenges of Sim-to-Real Transfer in Model-Based Reinforcement Learning via Latent Space Modeling Sim-to-real transfer of robotic control with dynamics randomization

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:15.598850Z

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.

Observation b5785569-44c6-40ce-b32a-9ad4cca0ca0c · outbound

This paper cites Deep reinforcement learning framework for autonomous driving.

Revealing the Challenges of Sim-to-Real Transfer in Model-Based Reinforcement Learning via Latent Space Modeling Deep reinforcement learning framework for autonomous driving

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:15.356449Z

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.

Observation 5fcfa85c-68ac-4ed3-a1c5-3aa3648bd4e3 · outbound

This paper cites Deep learning-enabled medical computer vision.

Revealing the Challenges of Sim-to-Real Transfer in Model-Based Reinforcement Learning via Latent Space Modeling Deep learning-enabled medical computer vision

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:15.201814Z

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.

Observation 51537159-e374-4f24-a715-e1dee639acbb · outbound

This paper cites Domain-adversarial training of neural networks.

Revealing the Challenges of Sim-to-Real Transfer in Model-Based Reinforcement Learning via Latent Space Modeling Domain-adversarial training of neural networks

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:14.922317Z

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.

Observation 35376df2-c0e4-4647-800e-c53327fce959 · outbound

This paper cites Human-level control through deep reinforcement learning.

Revealing the Challenges of Sim-to-Real Transfer in Model-Based Reinforcement Learning via Latent Space Modeling Human-level control through deep reinforcement learning

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T00:49:08.183718Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 440dde7a-4d76-4c5e-95fa-ebd55b04b16d · outbound

This paper cites Integrated architectures for learning, planning, and reacting based on approximating dynamic programming.

Revealing the Challenges of Sim-to-Real Transfer in Model-Based Reinforcement Learning via Latent Space Modeling Integrated architectures for learning, planning, and reacting based on approximating dynamic programming

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T00:49:08.321559Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8951ce37-5462-470b-bd5f-a94d691bc371 · outbound

This paper cites PILCO: A model-based and data-efficient approach to policy search.

Revealing the Challenges of Sim-to-Real Transfer in Model-Based Reinforcement Learning via Latent Space Modeling PILCO: A model-based and data-efficient approach to policy search

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:14.599150Z

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.

Observation 5b40a867-7566-4ac5-b5b9-848594e55f9a · outbound

This paper cites Deep reinforcement learning in a handful of trials using probabilistic dynamics models.

Revealing the Challenges of Sim-to-Real Transfer in Model-Based Reinforcement Learning via Latent Space Modeling Deep reinforcement learning in a handful of trials using probabilistic dynamics models

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:14.283638Z

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.

Observation a29cf676-4cdf-4c55-8c68-80b85e134adc · outbound

This paper cites When to trust your model: Model-based policy optimization.

Revealing the Challenges of Sim-to-Real Transfer in Model-Based Reinforcement Learning via Latent Space Modeling When to trust your model: Model-based policy optimization

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:13.995734Z

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.

Observation 34e8aa42-f30d-4a4b-b703-7c4b0f95bd37 · outbound

This paper cites MOPO: Model-based offline policy optimization.

Revealing the Challenges of Sim-to-Real Transfer in Model-Based Reinforcement Learning via Latent Space Modeling MOPO: Model-based offline policy optimization

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:13.779097Z

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.

Observation f50274b6-0ddd-4cb1-9989-f00d21fcf994 · outbound

This paper cites RAMBO-RL: Robust adversarial model-based offline reinforcement learning.

Revealing the Challenges of Sim-to-Real Transfer in Model-Based Reinforcement Learning via Latent Space Modeling RAMBO-RL: Robust adversarial model-based offline reinforcement learning

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:13.524089Z

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.

Observation 79f25aaf-1be1-4f62-8387-f82516e9e613 · outbound

This paper cites Dream to Control: Learning Behaviors by Latent Imagination.

Revealing the Challenges of Sim-to-Real Transfer in Model-Based Reinforcement Learning via Latent Space Modeling Dream to Control: Learning Behaviors by Latent Imagination

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T00:49:09.233772Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 60571efa-909e-4389-9447-4bda6d8c49d8 · outbound

This paper cites Mastering Atari with Discrete World Models.

Revealing the Challenges of Sim-to-Real Transfer in Model-Based Reinforcement Learning via Latent Space Modeling Mastering Atari with Discrete World Models

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T00:49:09.381561Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0187c015-156b-49f9-b6e8-c9a6e2e14763 · outbound

This paper cites Mastering Diverse Domains through World Models.

Revealing the Challenges of Sim-to-Real Transfer in Model-Based Reinforcement Learning via Latent Space Modeling Mastering Diverse Domains through World Models

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T00:49:09.544394Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4163198c-8b65-4b12-8990-4a0686f4e1e1 · outbound

This paper cites Active domain randomization.

Revealing the Challenges of Sim-to-Real Transfer in Model-Based Reinforcement Learning via Latent Space Modeling Active domain randomization

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:13.201879Z

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.

Observation 4df56883-7be6-472a-84fc-e75e7a6be6c2 · outbound

This paper cites Closing the sim-to-real loop: Adapting simulation randomization with real world experience.

Revealing the Challenges of Sim-to-Real Transfer in Model-Based Reinforcement Learning via Latent Space Modeling Closing the sim-to-real loop: Adapting simulation randomization with real world experience

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:12.890859Z

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.

Observation 32cdc2cb-069a-4015-a164-1e7b3966b63e · outbound

This paper cites A novel sim2real re- inforcement learning algorithm for process control.

Revealing the Challenges of Sim-to-Real Transfer in Model-Based Reinforcement Learning via Latent Space Modeling A novel sim2real re- inforcement learning algorithm for process control

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:12.644903Z

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.

Observation 2160565d-c6c1-4f4a-b7e4-d372aff3cea7 · outbound

This paper cites PAC reinforcement learning with an imperfect model.

Revealing the Challenges of Sim-to-Real Transfer in Model-Based Reinforcement Learning via Latent Space Modeling PAC reinforcement learning with an imperfect model

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:12.470939Z

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.

Observation fd39f8f5-6339-4b95-9fd8-0fc40bb92783 · outbound

This paper cites Soft Actor-Critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor.

Revealing the Challenges of Sim-to-Real Transfer in Model-Based Reinforcement Learning via Latent Space Modeling Soft Actor-Critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:12.318341Z

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.

Observation 4c991771-9a9a-4209-b88e-ede154995b27 · outbound

This paper cites MuJoCo: A physics engine for model-based control.

Revealing the Challenges of Sim-to-Real Transfer in Model-Based Reinforcement Learning via Latent Space Modeling MuJoCo: A physics engine for model-based control

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:12.065092Z

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.

Observation 50b19bcf-3df8-4b57-b2a4-a3f8ddfe3a7f · outbound

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

Revealing the Challenges of Sim-to-Real Transfer in Model-Based Reinforcement Learning via Latent Space Modeling D4RL: Datasets for Deep Data-Driven Reinforcement Learning

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T00:49:10.445339Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6c13f784-cf88-49e8-972a-e2a555c65971 · outbound

This paper cites A survey on deep transfer learning.

Revealing the Challenges of Sim-to-Real Transfer in Model-Based Reinforcement Learning via Latent Space Modeling A survey on deep transfer learning

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:11.785067Z

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.

Observation 4e3c7a15-b588-433d-a63c-20727e7f2655 · outbound

This paper cites De-pessimism offline reinforcement learning via value compensation.

Revealing the Challenges of Sim-to-Real Transfer in Model-Based Reinforcement Learning via Latent Space Modeling De-pessimism offline reinforcement learning via value compensation

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:11.470309Z

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.

Observation 5ca53279-548d-4723-baf6-b7d222d40641 · outbound

This paper cites Pattern Recognition and Machine Learning.

Revealing the Challenges of Sim-to-Real Transfer in Model-Based Reinforcement Learning via Latent Space Modeling Pattern Recognition and Machine Learning

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:11.196697Z

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.

Pith citing papers

Observation 904f56fb-1219-4d7e-ba3b-1bf9629d055a · inbound

The Sim-to-Real Gap of Foundation Model Agents: A Unified MDP Perspective cites this paper.

The Sim-to-Real Gap of Foundation Model Agents: A Unified MDP Perspective Revealing the Challenges of Sim-to-Real Transfer in Model-Based Reinforcement Learning via Latent Space Modeling

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-07-02T16:47:09.225038Z

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.