Paper Citation Record · LEDGER

Learning Invariant Representations for Reinforcement Learning without Reconstruction

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 31 inbound Pith citation observations for arXiv:2006.10742.

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

pith.paper-citation-record.v1
2006.10742 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 31 of 31 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:10:25.500000Z

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

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External citation measurements

77
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 781a693a-7d47-4f67-96e8-0efad716c3c2 · inbound

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

R3M: A Universal Visual Representation for Robot Manipulation Learning Invariant Representations for Reinforcement Learning without Reconstruction

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-15T13:26:53.911524Z

Source-reported events for the cited work

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Observation ac87f509-a843-4003-8c9f-eca1b4d66716 · inbound

Bayesian Inverse Transition Learning: Learning Dynamics From Near-Optimal Trajectories cites this paper.

Bayesian Inverse Transition Learning: Learning Dynamics From Near-Optimal Trajectories Learning Invariant Representations for Reinforcement Learning without Reconstruction

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-05-23T17:13:14.099190Z

Source-reported events for the cited work

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Observation b9c1d488-5725-480b-9848-7bbad204caab · inbound

Optimal Control with Natural Images: Efficient Reinforcement Learning using Overcomplete Sparse Codes cites this paper.

Optimal Control with Natural Images: Efficient Reinforcement Learning using Overcomplete Sparse Codes Learning Invariant Representations for Reinforcement Learning without Reconstruction

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-23T07:22:42.504333Z

Source-reported events for the cited work

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Observation 9c139145-ea5c-41d5-ad42-0094979afa8b · inbound

Contrastive Representation for Interactive Recommendation cites this paper.

Contrastive Representation for Interactive Recommendation Learning Invariant Representations for Reinforcement Learning without Reconstruction

Reference 54

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Observation 176cb31d-a95d-4667-a688-0828faf09e2f · inbound

Latent Action Learning Requires Supervision in the Presence of Distractors cites this paper.

Latent Action Learning Requires Supervision in the Presence of Distractors Learning Invariant Representations for Reinforcement Learning without Reconstruction

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-09T19:18:45.167910Z

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Observation 5c4ad380-aff4-4c88-8cf5-3c28898c07a3 · inbound

LLM Bandit: Cost-Efficient LLM Generation via Preference-Conditioned Dynamic Routing cites this paper.

LLM Bandit: Cost-Efficient LLM Generation via Preference-Conditioned Dynamic Routing Learning Invariant Representations for Reinforcement Learning without Reconstruction

Reference 35

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

Towards Empowerment Gain through Causal Structure Learning in Model-Based RL cites this paper.

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

Reference 16

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Observation 9306d304-a8bb-4ec2-8753-2efd89d9e46c · inbound

Physics-informed Temporal Difference Metric Learning for Robot Motion Planning cites this paper.

Physics-informed Temporal Difference Metric Learning for Robot Motion Planning Learning Invariant Representations for Reinforcement Learning without Reconstruction

Reference 60

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Observation 49aed078-ddc7-42b7-9de5-14a9594c55af · inbound

Approximated Behavioral Metric-based State Projection for Federated Reinforcement Learning cites this paper.

Approximated Behavioral Metric-based State Projection for Federated Reinforcement Learning Learning Invariant Representations for Reinforcement Learning without Reconstruction

Reference 34

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Observation c31911ca-51a1-4fe3-9d1f-3e4581e9afdb · inbound

Understanding Behavioral Metric Learning: A Large-Scale Study on Distracting Reinforcement Learning Environments cites this paper.

Understanding Behavioral Metric Learning: A Large-Scale Study on Distracting Reinforcement Learning Environments Learning Invariant Representations for Reinforcement Learning without Reconstruction

Reference 78

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Observation 745bc053-2879-47b3-a394-eea2e7e30507 · inbound

Decoupled Hierarchical Reinforcement Learning with State Abstraction for Discrete Grids cites this paper.

Decoupled Hierarchical Reinforcement Learning with State Abstraction for Discrete Grids Learning Invariant Representations for Reinforcement Learning without Reconstruction

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T12:02:48.124549Z

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Observation 4270a024-b1fc-4f57-bb03-5fc166d17ede · inbound

Self-Predictive Dynamics for Generalization of Vision-based Reinforcement Learning cites this paper.

Self-Predictive Dynamics for Generalization of Vision-based Reinforcement Learning Learning Invariant Representations for Reinforcement Learning without Reconstruction

Reference 23

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Observation 262d9d42-3a04-4d69-956b-7a8c212f3858 · inbound

Efficient and Generalizable Environmental Understanding for Visual Navigation cites this paper.

Efficient and Generalizable Environmental Understanding for Visual Navigation Learning Invariant Representations for Reinforcement Learning without Reconstruction

Reference 59

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Observation 1b84a277-27fa-4c80-9c63-5c2c2990b398 · inbound

Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control cites this paper.

Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control Learning Invariant Representations for Reinforcement Learning without Reconstruction

Reference 24

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Observation 932549f6-ac7a-4cf1-b8de-d70b042efbbd · inbound

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces cites this paper.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Learning Invariant Representations for Reinforcement Learning without Reconstruction

Reference 138

Resolution: unresolved; no resolver link, observed 2026-08-06T13:21:59.445779Z

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Observation 370db0ef-760c-4821-afd8-1d528ae46d08 · inbound

Next-Latent Prediction Transformers Learn Compact World Models cites this paper.

Next-Latent Prediction Transformers Learn Compact World Models Learning Invariant Representations for Reinforcement Learning without Reconstruction

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-25T07:25:29.458245Z

Source-reported events for the cited work

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Observation dd6831bc-67aa-4601-aa33-37dc112c75d5 · inbound

Next-Latent Prediction Transformers Learn Compact World Models cites this paper.

Next-Latent Prediction Transformers Learn Compact World Models Learning Invariant Representations for Reinforcement Learning without Reconstruction

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-03T23:27:57.468872Z

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Observation 8d209b51-f58e-41ea-93d7-7422cb413cbc · inbound

Hierarchical Successor Representation for Robust Transfer cites this paper.

Hierarchical Successor Representation for Robust Transfer Learning Invariant Representations for Reinforcement Learning without Reconstruction

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-02T23:46:56.591823Z

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Observation fa3cfe61-a583-41ae-8dc9-612d19b1fabc · inbound

Predictive but Not Plannable: RC-aux for Latent World Models cites this paper.

Predictive but Not Plannable: RC-aux for Latent World Models Learning Invariant Representations for Reinforcement Learning without Reconstruction

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-05-11T03:50:57.323227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 01271e96-3075-4b09-83f2-64c1397f6db3 · inbound

What to Ignore, What to React: Visually Robust RL Fine-Tuning of VLA Models cites this paper.

What to Ignore, What to React: Visually Robust RL Fine-Tuning of VLA Models Learning Invariant Representations for Reinforcement Learning without Reconstruction

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-14T18:47:36.094929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9733eb41-d64c-4c30-b566-676eb4f0c864 · inbound

Matrix-Space Reinforcement Learning for Reusing Local Transition Geometry cites this paper.

Matrix-Space Reinforcement Learning for Reusing Local Transition Geometry Learning Invariant Representations for Reinforcement Learning without Reconstruction

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-15T02:03:28.837266Z

Source-reported events for the cited work

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Observation a27e217f-c1e2-4ebf-839a-9fe2c1b87dc1 · inbound

Topology-Aware State Abstraction with Tangle Cores for Markov Decision Processes cites this paper.

Topology-Aware State Abstraction with Tangle Cores for Markov Decision Processes Learning Invariant Representations for Reinforcement Learning without Reconstruction

Reference 24

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d592bc2d-b457-41a2-ab71-ef168aad790a · inbound

Learning Object Manipulation from Scratch via Contrastive Interaction cites this paper.

Learning Object Manipulation from Scratch via Contrastive Interaction Learning Invariant Representations for Reinforcement Learning without Reconstruction

Reference 71

Resolution: verified exact; arxiv_id, observed 2026-07-03T10:17:57.422451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ba372a39-d7ac-4f7f-ad95-731e4701af78 · inbound

Direct Advantage Estimation for Scalable and Sample-efficient Deep Reinforcement Learning cites this paper.

Direct Advantage Estimation for Scalable and Sample-efficient Deep Reinforcement Learning Learning Invariant Representations for Reinforcement Learning without Reconstruction

Reference 82

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Observation 85f26f7b-d96a-4380-9974-ef2e081cd7cd · inbound

Beyond Next-Observation Prediction: Agent-Authored World Modeling for Sequential Decision Making cites this paper.

Beyond Next-Observation Prediction: Agent-Authored World Modeling for Sequential Decision Making Learning Invariant Representations for Reinforcement Learning without Reconstruction

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-07-04T19:40:06.155010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 352d998d-fff4-42ed-a36d-3956d1af82a9 · inbound

BiPACE: Bisimulation-Guided Policy Optimization with Action Counterfactual Estimation for LLM Agents cites this paper.

BiPACE: Bisimulation-Guided Policy Optimization with Action Counterfactual Estimation for LLM Agents Learning Invariant Representations for Reinforcement Learning without Reconstruction

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-07-04T19:40:07.693168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c1497871-8c96-4503-b634-4a8afdf2633d · inbound

Textual Belief States for World Models: Identifiable Representation Learning Under Strict Mediation cites this paper.

Textual Belief States for World Models: Identifiable Representation Learning Under Strict Mediation Learning Invariant Representations for Reinforcement Learning without Reconstruction

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-06-29T19:13:53.227287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1df26121-5b5d-4d3a-9fa9-f6e9efdcaee8 · inbound

Mask-based Predictive Representations for Reinforcement Learning cites this paper.

Mask-based Predictive Representations for Reinforcement Learning Learning Invariant Representations for Reinforcement Learning without Reconstruction

Reference 4

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Observation 666940ed-ca11-4316-b663-587cfa4c0341 · inbound

When Does Reward Teach State? A Hidden-Automaton Instrument and a Group-Language Warning Signal cites this paper.

When Does Reward Teach State? A Hidden-Automaton Instrument and a Group-Language Warning Signal Learning Invariant Representations for Reinforcement Learning without Reconstruction

Reference 15

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Observation 598dd703-9069-434e-a509-d80dca1144a7 · inbound

TaskSense: Focusing on What Matters in World Models cites this paper.

TaskSense: Focusing on What Matters in World Models Learning Invariant Representations for Reinforcement Learning without Reconstruction

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T04:21:20.349823Z

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Observation fd9920c3-ef01-4b67-a935-1db39ff9b084 · inbound

Same physical state, different collective dynamics: state encodings select synchronization outcomes in language-model agents cites this paper.

Same physical state, different collective dynamics: state encodings select synchronization outcomes in language-model agents Learning Invariant Representations for Reinforcement Learning without Reconstruction

Reference 16

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