Paper Citation Record · LEDGER

Towards Empowerment Gain through Causal Structure Learning in Model-Based RL

As of 8 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 4 inbound Pith citation observations for arXiv:2502.10077.

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

pith.paper-citation-record.v1
2502.10077 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T19:33:22.168006Z

measured 32 of 32 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 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-11T19:24:48.899301Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T20:59:02.013378Z

Reference resolution

28 of 28 outbound references displayed

verified exact2
verified fuzzy13
unresolved13
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 087d59ff-68fc-4739-96f4-50792555e94a · outbound

This paper cites Computation cost.

Towards Empowerment Gain through Causal Structure Learning in Model-Based RL Computation cost

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:33:22.447168Z

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 4dcf38bc-b38c-4d02-bb34-db37c81181bf · outbound

This paper cites Open X-Embodiment: Robotic Learning Datasets and RT-X Models.

Towards Empowerment Gain through Causal Structure Learning in Model-Based RL Open X-Embodiment: Robotic Learning Datasets and RT-X Models

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T19:33:22.037078Z

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Observation 7eccb483-980f-4918-9b29-14c52a1cb8d0 · outbound

This paper cites Moreover, The policyπcollect is trained with a reward function r = tanh(PdS j=1 log p(sj t+1|st,at) p(sj t+1|PAsj ) ).

Towards Empowerment Gain through Causal Structure Learning in Model-Based RL Moreover, The policyπcollect is trained with a reward function r = tanh(PdS j=1 log p(sj t+1|st,at) p(sj t+1|PAsj ) )

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:33:22.549364Z

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Observation 09bd1756-6698-4a53-ad55-014911be5be3 · outbound

This paper cites For pixel-based task learning, we leverage the four distinct categories of latent state variables by IFactor to conduct empowerment maximization for policy learning.

Towards Empowerment Gain through Causal Structure Learning in Model-Based RL For pixel-based task learning, we leverage the four distinct categories of latent state variables by IFactor to conduct empowerment maximization for policy learning

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:33:22.533416Z

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 4c7fe606-e7fa-4bf8-94fe-847e537d54e0 · outbound

This paper cites Action-sufficient state representation learning for control with structural constraints.

Towards Empowerment Gain through Causal Structure Learning in Model-Based RL Action-sufficient state representation learning for control with structural constraints

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:33:22.661336Z

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 1571818c-875e-44ed-8992-ba48001c2740 · outbound

This paper cites Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning.

Towards Empowerment Gain through Causal Structure Learning in Model-Based RL Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T19:33:22.064109Z

Source-reported events for the cited work

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Observation d1213412-b902-4fd5-864f-d9d2ef9c992b · outbound

This paper cites Empowerment: A universal agent-centric measure of control.

Towards Empowerment Gain through Causal Structure Learning in Model-Based RL Empowerment: A universal agent-centric measure of control

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:33:22.644276Z

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 78255886-aa1b-4b71-a17a-201b796b78ce · outbound

This paper cites Dreaming: Model-based reinforcement learning by la- tent imagination without reconstruction.

Towards Empowerment Gain through Causal Structure Learning in Model-Based RL Dreaming: Model-based reinforcement learning by la- tent imagination without reconstruction

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:33:22.629092Z

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 73f038f7-40ad-4e13-a6d7-ef8aaf7caba1 · outbound

This paper cites Robust agents learn causal world models.

Towards Empowerment Gain through Causal Structure Learning in Model-Based RL Robust agents learn causal world models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T19:33:22.084760Z

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Observation 3edf811a-25a6-4d4c-9e4c-6bd29e848472 · outbound

This paper cites Composing Pre-Trained Object-Centric Representations for Robotics From "What" and "Where" Foundation Models.

Towards Empowerment Gain through Causal Structure Learning in Model-Based RL Composing Pre-Trained Object-Centric Representations for Robotics From "What" and "Where" Foundation Models

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-07T19:33:22.263392Z

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 01e50c56-b27b-44db-8e63-761cf0070b03 · outbound

This paper cites Causal Dynamics Learning for Task-Independent State Abstraction.

Towards Empowerment Gain through Causal Structure Learning in Model-Based RL Causal Dynamics Learning for Task-Independent State Abstraction

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T19:33:22.099938Z

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Observation 38e5fc79-5ea7-4ffe-83ea-8a877a0d394d · outbound

This paper cites Learning Invariant Representations for Reinforcement Learning without Reconstruction.

Towards Empowerment Gain through Causal Structure Learning in Model-Based RL Learning Invariant Representations for Reinforcement Learning without Reconstruction

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T19:33:22.105321Z

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Observation 1654e2ff-451b-4d9a-9e79-1269328b3c3e · outbound

This paper cites 3 2.2 Empowerment.

Towards Empowerment Gain through Causal Structure Learning in Model-Based RL 3 2.2 Empowerment

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:33:22.613331Z

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 d508e17a-7ac2-4170-a8f2-83891f92a66b · outbound

This paper cites dSX i=1 log Pϕc (si t+1|st, at; ϕc) # (13) Lc−dyn = E(st,at,st+1)∼D.

Towards Empowerment Gain through Causal Structure Learning in Model-Based RL dSX i=1 log Pϕc (si t+1|st, at; ϕc) # (13) Lc−dyn = E(st,at,st+1)∼D

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:33:22.597306Z

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 9c4646c3-debd-43cc-ad29-9f052e582550 · outbound

This paper cites Meanwhile, in the downstream tasks, we evaluate the proposed methods by episodic reward and success rate.

Towards Empowerment Gain through Causal Structure Learning in Model-Based RL Meanwhile, in the downstream tasks, we evaluate the proposed methods by episodic reward and success rate

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:33:22.580578Z

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 7ddae2be-cf20-45d3-ae6f-c14aecc67127 · outbound

This paper cites Subsequently, we apply the proposed ECL framework for policy learning.

Towards Empowerment Gain through Causal Structure Learning in Model-Based RL Subsequently, we apply the proposed ECL framework for policy learning

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:33:22.565172Z

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 dba74cc7-f66d-41ff-811b-8f2789550467 · outbound

This paper cites D.6 P IXEL -BASED TASKS LEARNING We evaluate ECL on 5 pixel-input tasks across 3 latent state environments.

Towards Empowerment Gain through Causal Structure Learning in Model-Based RL D.6 P IXEL -BASED TASKS LEARNING We evaluate ECL on 5 pixel-input tasks across 3 latent state environments

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:33:22.463792Z

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 feeca8ef-fa0d-445d-b241-499193e38817 · outbound

This paper cites an unresolved cited work.

Towards Empowerment Gain through Causal Structure Learning in Model-Based RL Unresolved cited work

Reference 23

Resolution: unresolved; raw_fallback, observed 2026-08-07T19:33:22.517418Z

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 115cb626-0705-48e6-bac3-11a3e65cca65 · outbound

This paper cites Moreover, we achieve extensive elimination of causality between irrelevant factors.

Towards Empowerment Gain through Causal Structure Learning in Model-Based RL Moreover, we achieve extensive elimination of causality between irrelevant factors

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:33:22.500114Z

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 891f9fb0-ebe4-4831-a5f4-75478d673393 · outbound

This paper cites Compared to CDL shown in Figure 16, ECL-Con learns more causal associations from relevant causal components related to the gripper, movable states, and actions.

Towards Empowerment Gain through Causal Structure Learning in Model-Based RL Compared to CDL shown in Figure 16, ECL-Con learns more causal associations from relevant causal components related to the gripper, movable states, and actions

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:33:22.482245Z

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 730db136-fcb6-4ed9-a9cc-390a0e0e1057 · outbound

This paper cites an unresolved cited work.

Towards Empowerment Gain through Causal Structure Learning in Model-Based RL Unresolved cited work

Reference 28

Resolution: unresolved; raw_fallback, observed 2026-08-07T19:33:22.431420Z

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

Observation 719faa82-f7a2-4c09-b2b2-88b8baf15275 · outbound

This paper cites Mastering Diverse Domains through World Models.

Towards Empowerment Gain through Causal Structure Learning in Model-Based RL Mastering Diverse Domains through World Models

Reference 2003

Resolution: unresolved; no resolver link, observed 2026-08-07T19:33:22.048125Z

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Observation 1e0e4f8b-686f-4ce6-a64f-ebfc52f1a5a9 · outbound

This paper cites Exploiting Causal Graph Priors with Posterior Sampling for Reinforcement Learning.

Towards Empowerment Gain through Causal Structure Learning in Model-Based RL Exploiting Causal Graph Priors with Posterior Sampling for Reinforcement Learning

Reference 2015

Resolution: verified exact; local_arxiv, observed 2026-08-07T19:33:22.302606Z

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 cead91a3-2bf4-48f2-893c-b6e3cead9530 · outbound

This paper cites Octo: An Open-Source Generalist Robot Policy.

Towards Empowerment Gain through Causal Structure Learning in Model-Based RL Octo: An Open-Source Generalist Robot Policy

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-07T19:33:22.094900Z

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Observation 0fd5e862-537a-4be4-ae24-d5a56184b43b · outbound

This paper cites AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning.

Towards Empowerment Gain through Causal Structure Learning in Model-Based RL AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-07T19:33:22.053467Z

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Observation c7348873-df94-4368-ad4b-d226824f9056 · outbound

This paper cites INFOrmation Prioritization through EmPOWERment in Visual Model-Based RL.

Towards Empowerment Gain through Causal Structure Learning in Model-Based RL INFOrmation Prioritization through EmPOWERment in Visual Model-Based RL

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-07T19:33:22.025702Z

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Observation 44f610a2-273b-49db-8e3c-9fb18238be8b · outbound

This paper cites Variational Empowerment as Representation Learning for Goal-Based Reinforcement Learning.

Towards Empowerment Gain through Causal Structure Learning in Model-Based RL Variational Empowerment as Representation Learning for Goal-Based Reinforcement Learning

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-07T19:33:22.031764Z

Source-reported events for the cited work

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Observation b7766713-a6d8-47cb-a214-cbec1caa5894 · outbound

This paper cites Diversity is All You Need: Learning Skills without a Reward Function.

Towards Empowerment Gain through Causal Structure Learning in Model-Based RL Diversity is All You Need: Learning Skills without a Reward Function

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-07T19:33:22.042629Z

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Pith citing papers

Observation 0e17feba-24f2-458b-aee6-b42486190fd2 · inbound

Delay-Empowered Causal Hierarchical Reinforcement Learning cites this paper.

Delay-Empowered Causal Hierarchical Reinforcement Learning Towards Empowerment Gain through Causal Structure Learning in Model-Based RL

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-13T05:47:21.212637Z

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 7111635b-9a1c-4bb6-a2fe-3a46316098c4 · inbound

Transferable Delay-Aware Reinforcement Learning via Implicit Causal Graph Modeling cites this paper.

Transferable Delay-Aware Reinforcement Learning via Implicit Causal Graph Modeling Towards Empowerment Gain through Causal Structure Learning in Model-Based RL

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-13T07:02:26.996146Z

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 6abe2f19-0d55-4b41-8351-3e2a48da617b · inbound

Ada-Diffuser: Latent-Aware Adaptive Diffusion for Decision-Making cites this paper.

Ada-Diffuser: Latent-Aware Adaptive Diffusion for Decision-Making Towards Empowerment Gain through Causal Structure Learning in Model-Based RL

Reference 272

Resolution: verified exact; arxiv_id, observed 2026-05-20T20:59:02.015455Z

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

Observation 83fbc19f-8134-4485-b109-36d58fed41d7 · inbound

Learning Task-Sufficient World Models by Synergizing Agentic Exploration and Structured Modeling cites this paper.

Learning Task-Sufficient World Models by Synergizing Agentic Exploration and Structured Modeling Towards Empowerment Gain through Causal Structure Learning in Model-Based RL

Reference 124

Resolution: unresolved; no resolver link, observed 2026-07-11T19:24:48.899301Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.