Paper Citation Record · LEDGER

Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning

As of 16 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 4 inbound Pith citation observations for arXiv:2505.08361.

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

pith.paper-citation-record.v1
2505.08361 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:07:50.028349Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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 2026-05-20T20:59:02.203456Z

Reference resolution

36 of 36 outbound references displayed

verified exact0
verified fuzzy12
unresolved23
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3c5f59f4-d4af-4c0d-a414-910e9c740e26 · outbound

This paper cites The latent variabless t have6dimensions, wheren c1 =n c2 =n c3 =.

Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning The latent variabless t have6dimensions, wheren c1 =n c2 =n c3 =

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:07:50.443470Z

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Observation 939235a6-26ab-4a6d-bb34-adcf7234e8f7 · outbound

This paper cites However, this also adds some constraints and requirements on the number of tasks that combinedl 1 andl 2, which we are going to discuss later.

Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning However, this also adds some constraints and requirements on the number of tasks that combinedl 1 andl 2, which we are going to discuss later

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:07:50.491203Z

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Observation 640993cc-32be-4764-a8a0-5a02074f88ee · outbound

This paper cites Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor.

Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

Reference 3

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Observation bc0da1ed-0fc4-45f3-abbe-282299af87d4 · outbound

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

Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning Dream to Control: Learning Behaviors by Latent Imagination

Reference 4

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Observation 6a84de14-3c1a-40b3-9010-f35756dce9f5 · outbound

This paper cites Mastering Diverse Domains through World Models.

Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning Mastering Diverse Domains through World Models

Reference 5

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Observation 8eac3ddf-7c89-4e16-a3d0-7410b8815878 · outbound

This paper cites Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding.

Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T22:07:49.897777Z

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Observation 140450fe-c4d8-4a4f-a2cc-76d5764badc7 · outbound

This paper cites Identification of nonlinear latent hierarchical models.Advances in Neural Information Processing Systems, 36: 2010–2032, 2023a.

Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning Identification of nonlinear latent hierarchical models.Advances in Neural Information Processing Systems, 36: 2010–2032, 2023a

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:07:50.617051Z

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Observation ddd2600a-f39f-4d31-b392-120871390d6d · outbound

This paper cites R3M: A Universal Visual Representation for Robot Manipulation.

Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning R3M: A Universal Visual Representation for Robot Manipulation

Reference 13

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Source-reported events for the cited work

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Observation 1aaf4bd3-1748-482f-8432-ef03ea9bff44 · outbound

This paper cites Skill-based Model-based Reinforcement Learning.

Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning Skill-based Model-based Reinforcement Learning

Reference 17

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Observation 28164990-8f7e-4c8f-8ae5-2dc16397c6d8 · outbound

This paper cites Temporally disentangled representation learning under unknown nonstationarity.Advances in Neural Information Processing Systems, 36,.

Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning Temporally disentangled representation learning under unknown nonstationarity.Advances in Neural Information Processing Systems, 36,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:07:50.586865Z

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Observation 44b2fa85-6c17-4a60-ae44-e894decbf37c · outbound

This paper cites Observational Overfitting in Reinforcement Learning.

Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning Observational Overfitting in Reinforcement Learning

Reference 19

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Observation 1e707e8f-e345-485b-bab2-1bd9481a801d · outbound

This paper cites Learning Temporally Causal Latent Processes from General Temporal Data.

Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning Learning Temporally Causal Latent Processes from General Temporal Data

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T22:07:49.952829Z

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Observation 4f2a9fe7-54fd-492f-9bb3-ebb46dbc79ab · outbound

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Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning Unresolved cited work

Reference 21

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Observation b27d0189-c8fa-418a-b952-963f50a2d587 · outbound

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Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning Unresolved cited work

Reference 25

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Observation 49249234-cf49-4317-b789-21520e026b1b · outbound

This paper cites However, these approaches often focus on dimension-wise identifiability, which can be difficult to scale in real-world applications with complex causal dynamics.

Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning However, these approaches often focus on dimension-wise identifiability, which can be difficult to scale in real-world applications with complex causal dynamics

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:07:50.507469Z

Source-reported events for the cited work

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Observation efca8cb3-1cd9-4a52-b167-c4d985576deb · outbound

This paper cites This would re- quireQ2 i=1nci tasks for2language components and Qm i=1nci + 1tasks formlanguage components.

Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning This would re- quireQ2 i=1nci tasks for2language components and Qm i=1nci + 1tasks formlanguage components

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:07:50.474328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9165fd8b-edd1-47e9-b394-d4b14342b15a · outbound

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Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning Unresolved cited work

Reference 30

Resolution: unresolved; raw_fallback, observed 2026-08-15T22:07:50.458976Z

Source-reported events for the cited work

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Observation 15510cca-2ba7-49f1-8f1b-97cdea3119b5 · outbound

This paper cites To best achieve the identifiability condition, we choose the common language component system that include most tasks,verbandobject.

Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning To best achieve the identifiability condition, we choose the common language component system that include most tasks,verbandobject

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:07:50.426021Z

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Observation 09a3a2f0-8e06-43e4-8b76-c62e07c82de3 · outbound

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Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning Unresolved cited work

Reference 33

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Observation 54839c12-b7f1-43fd-9cd5-bef5b7520ac6 · outbound

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Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning Unresolved cited work

Reference 34

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Observation fcb3f51b-39b8-485f-832c-fc595e9324de · outbound

This paper cites These methods are often dimension-wise and fail to scale effectively to complex systems with interdependent latent structures.

Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning These methods are often dimension-wise and fail to scale effectively to complex systems with interdependent latent structures

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:07:50.362664Z

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Observation 7a91dedd-6d7b-489c-b6b3-7f84b0fa99ef · outbound

This paper cites However, their methodologies and applications diverge significantly.

Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning However, their methodologies and applications diverge significantly

Reference 1997

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:07:50.378681Z

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Observation ae68136a-67ac-4702-b643-5a4dea31ace6 · outbound

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Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning Unresolved cited work

Reference 1999

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Observation e1d6f5fb-3f39-4875-b89b-20f44d5f61ab · outbound

This paper cites Rusu, Joel Veness, Marc G.

Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning Rusu, Joel Veness, Marc G

Reference 2003

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:07:50.601311Z

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Observation 6b9df42d-d8eb-4e33-a2fa-abf4a485c14c · outbound

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

Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning

Reference 2008

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Observation b78af3aa-3e52-4b7d-af73-9b622c4aa536 · outbound

This paper cites On the Identifiability of the Post-Nonlinear Causal Model.

Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning On the Identifiability of the Post-Nonlinear Causal Model

Reference 2009

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Observation 4841c140-364e-4db2-bd23-fe7dbe0f2094 · outbound

This paper cites Early approaches like Meta-RL and invariant representa- tion learning (Lee et al., 2019; Hansen & Wang, 2020; Yuan et al., 2022; Nair et al.,.

Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning Early approaches like Meta-RL and invariant representa- tion learning (Lee et al., 2019; Hansen & Wang, 2020; Yuan et al., 2022; Nair et al.,

Reference 2012

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:07:50.554748Z

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Observation 62419903-8327-4ede-8f69-431cfb2fb4d8 · outbound

This paper cites Causal Representation Learning Made Identifiable by Grouping of Observational Variables.

Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning Causal Representation Learning Made Identifiable by Grouping of Observational Variables

Reference 2016

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This paper cites Improving Dictionary Learning with Gated Sparse Autoencoders.

Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning Improving Dictionary Learning with Gated Sparse Autoencoders

Reference 2017

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This paper cites RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning.

Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning

Reference 2018

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Observation f7ba97c7-7d46-41eb-87a8-51bfb1a3c490 · outbound

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Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning World Models

Reference 2019

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Observation 1c96dca4-befd-4b64-8dfa-249cb62e5fc1 · outbound

This paper cites TD-MPC2: Scalable, Robust World Models for Continuous Control.

Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning TD-MPC2: Scalable, Robust World Models for Continuous Control

Reference 2020

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Observation b6c70547-e7fb-4e09-b6cb-10c46d1dc391 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning Proximal Policy Optimization Algorithms

Reference 2021

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Observation 92066836-5a71-4b14-94f4-daec2aed95f2 · outbound

This paper cites Invariant Causal Prediction for Block MDPs.

Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning Invariant Causal Prediction for Block MDPs

Reference 2022

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Observation 5940a011-68c7-40d2-96e6-3477cf5fb7fb · outbound

This paper cites Generalization in reinforcement learning by soft data augmen- tation.2021 IEEE International Conference on Robotics and Automation (ICRA), pp.

Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning Generalization in reinforcement learning by soft data augmen- tation.2021 IEEE International Conference on Robotics and Automation (ICRA), pp

Reference 2023

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Observation 362323ba-ef67-45ac-ba7f-afde5129b684 · outbound

This paper cites Towards Causal Representation Learning.

Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning Towards Causal Representation Learning

Reference 2024

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Pith citing papers

Observation 0b4faa37-f3f1-4430-920b-24fdccf822f3 · inbound

The Design and Composition of Structural Causal Decision Processes cites this paper.

The Design and Composition of Structural Causal Decision Processes Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning

Reference 54

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Observation e14ddd6b-4b6f-47b4-b4da-b27392a83a67 · inbound

Plan in Sandbox, Navigate in Open Worlds: Learning Physics-Grounded Abstracted Experience for Embodied Navigation cites this paper.

Plan in Sandbox, Navigate in Open Worlds: Learning Physics-Grounded Abstracted Experience for Embodied Navigation Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning

Reference 25

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Observation 349ef338-0215-49f3-8bf4-c349a211c2ac · inbound

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

Ada-Diffuser: Latent-Aware Adaptive Diffusion for Decision-Making Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-20T20:59:02.205289Z

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Observation 50290f3d-81b4-40c3-9d82-a708dcbb29b6 · 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 Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning

Reference 178

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