Paper Citation Record · LEDGER

Robust agents learn causal world models

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2402.10877.

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

pith.paper-citation-record.v1
2402.10877 v7

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 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 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T19:33:43.791925Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T06:44:18.998459Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
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malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 73f038f7-40ad-4e13-a6d7-ef8aaf7caba1 · 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 Robust agents learn causal world models

Reference 12

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f82ef5f4-1176-4a30-8aeb-6bd587fb31e7 · inbound

Causal Information Prioritization for Efficient Reinforcement Learning cites this paper.

Causal Information Prioritization for Efficient Reinforcement Learning Robust agents learn causal world models

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-07T19:33:43.791925Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9d97dfa0-8ecb-42d8-ac3d-f806e2f31504 · inbound

The Limits of Predicting Agents from Behaviour cites this paper.

The Limits of Predicting Agents from Behaviour Robust agents learn causal world models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T11:25:27.272210Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f47078de-afc2-4e70-b067-a0470baf38a9 · inbound

Linear Spatial World Models Emerge in Large Language Models cites this paper.

Linear Spatial World Models Emerge in Large Language Models Robust agents learn causal world models

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T11:17:24.319460Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 05c50f3a-479a-4bf1-8854-926c8b6f34b1 · inbound

Enhancing LLM Agent Safety via Causal Influence Prompting cites this paper.

Enhancing LLM Agent Safety via Causal Influence Prompting Robust agents learn causal world models

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-06T21:06:54.862967Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 99e1efbb-a332-46d2-9e79-c841c09058f0 · inbound

The Generalist Brain Module: Module Repetition in Neural Networks in Light of the Minicolumn Hypothesis cites this paper.

The Generalist Brain Module: Module Repetition in Neural Networks in Light of the Minicolumn Hypothesis Robust agents learn causal world models

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-06T21:16:47.904845Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 584c8a66-b773-4203-a89d-1a8991ffc0be · inbound

Calculating Mutual Information between a Reward Maximizer and its Environment cites this paper.

Calculating Mutual Information between a Reward Maximizer and its Environment Robust agents learn causal world models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-02T23:49:12.592843Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f79764d5-cb6b-4ee2-a25d-5ab956e0ae19 · inbound

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

The Design and Composition of Structural Causal Decision Processes Robust agents learn causal world models

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-05-11T22:46:13.439736Z

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 ee11e4c8-1dd7-40dc-b7c7-a5ece5b66480 · inbound

You Are in Control of Your State: Why Human Outcomes Are Controllable Through Causal State Intervention cites this paper.

You Are in Control of Your State: Why Human Outcomes Are Controllable Through Causal State Intervention Robust agents learn causal world models

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-06-29T17:13:44.754267Z

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 4d3c5438-ebc4-426d-a23a-d0327e59ab01 · inbound

The CRISTAL Method: Neurosymbolic analysis from AI-synthesized world models cites this paper.

The CRISTAL Method: Neurosymbolic analysis from AI-synthesized world models Robust agents learn causal world models

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-06-30T06:44:18.999866Z

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.