Paper Citation Record · LEDGER

WALL-E: World Alignment by Rule Learning Improves World Model-based LLM Agents

As of 5 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2410.07484.

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

pith.paper-citation-record.v1
2410.07484 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T21:20:40.653205Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T19:40:06.142990Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

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 c6b1abd0-da8d-4060-9b40-fc87a5726517 · inbound

Mini Amusement Parks (MAPs): A Testbed for Modelling Business Decisions cites this paper.

Mini Amusement Parks (MAPs): A Testbed for Modelling Business Decisions WALL-E: World Alignment by Rule Learning Improves World Model-based LLM Agents

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-03T21:20:40.653205Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e652c211-19ce-484a-8350-3ac090c6487b · inbound

ReCAPA: Hierarchical Predictive Correction to Mitigate Cascading Failures cites this paper.

ReCAPA: Hierarchical Predictive Correction to Mitigate Cascading Failures WALL-E: World Alignment by Rule Learning Improves World Model-based LLM Agents

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-09T22:44:15.183738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b6f8d41c-eaf3-4cc7-8967-13e90e995673 · inbound

ReCAPA: Hierarchical Predictive Correction to Mitigate Cascading Failures cites this paper.

ReCAPA: Hierarchical Predictive Correction to Mitigate Cascading Failures WALL-E: World Alignment by Rule Learning Improves World Model-based LLM Agents

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:11:19.350622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c313a040-e68e-4145-ae45-7d6a9afaf05f · 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 WALL-E: World Alignment by Rule Learning Improves World Model-based LLM Agents

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-07-04T19:40:06.144683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 22123607-f8f8-470f-89ba-9012aad610e9 · inbound

Agent vs. Parametric World Models: Hybrid Planning for Reliable Language Agents cites this paper.

Agent vs. Parametric World Models: Hybrid Planning for Reliable Language Agents WALL-E: World Alignment by Rule Learning Improves World Model-based LLM Agents

Reference 8

Resolution: unresolved; no resolver link, observed 2026-07-12T11:37:35.542449Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 83c96a2d-31e5-41ee-a841-927b51b1252c · inbound

Object-Centric Environment Modeling for Agentic Tasks cites this paper.

Object-Centric Environment Modeling for Agentic Tasks WALL-E: World Alignment by Rule Learning Improves World Model-based LLM Agents

Reference 12

Resolution: unresolved; no resolver link, observed 2026-07-12T06:39:21.435864Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.