Paper Citation Record · LEDGER

WALL-E 2.0: World Alignment by NeuroSymbolic Learning improves World Model-based LLM Agents

As of 20 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 4 inbound Pith citation observations for arXiv:2504.15785.

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

pith.paper-citation-record.v1
2504.15785 v1

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:23:35.198553Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T21:20:40.795352Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T21:47:48.355586Z

Reference resolution

27 of 27 outbound references displayed

verified exact0
verified fuzzy1
unresolved26
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 23c33ece-84c8-4d02-99d0-f1905c5bdf52 · outbound

This paper cites RT-1: Robotics Transformer for Real-World Control at Scale.

WALL-E 2.0: World Alignment by NeuroSymbolic Learning improves World Model-based LLM Agents RT-1: Robotics Transformer for Real-World Control at Scale

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-16T11:23:34.750302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a93fa471-2639-4e7a-9591-7634c9722c42 · outbound

This paper cites Reasoning with Language Model is Planning with World Model.

WALL-E 2.0: World Alignment by NeuroSymbolic Learning improves World Model-based LLM Agents Reasoning with Language Model is Planning with World Model

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-16T11:23:34.804590Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b52cf0a-24b9-4fb5-b655-b0ba1119abb4 · outbound

This paper cites Language Models, Agent Models, and World Models: The LAW for Machine Reasoning and Planning.

WALL-E 2.0: World Alignment by NeuroSymbolic Learning improves World Model-based LLM Agents Language Models, Agent Models, and World Models: The LAW for Machine Reasoning and Planning

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-16T11:23:34.808439Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation de1207a2-5b59-4240-a2ee-3c79864998ca · outbound

This paper cites Optimus-1: Hybrid Multimodal Memory Empowered Agents Excel in Long-Horizon Tasks.

WALL-E 2.0: World Alignment by NeuroSymbolic Learning improves World Model-based LLM Agents Optimus-1: Hybrid Multimodal Memory Empowered Agents Excel in Long-Horizon Tasks

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-16T11:23:34.812401Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9928c843-8aa4-41ab-90c4-1c7e0afff579 · outbound

This paper cites Reason for Future, Act for Now: A Principled Framework for Autonomous LLM Agents with Provable Sample Efficiency.

WALL-E 2.0: World Alignment by NeuroSymbolic Learning improves World Model-based LLM Agents Reason for Future, Act for Now: A Principled Framework for Autonomous LLM Agents with Provable Sample Efficiency

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-16T11:23:34.916107Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7dae5880-8600-4948-89d2-cc3e1ed41073 · outbound

This paper cites ChatRule: Mining Logical Rules with Large Language Models for Knowledge Graph Reasoning.

WALL-E 2.0: World Alignment by NeuroSymbolic Learning improves World Model-based LLM Agents ChatRule: Mining Logical Rules with Large Language Models for Knowledge Graph Reasoning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-16T11:23:34.975620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af2e81d4-0d99-4897-ae59-caa088c1aef8 · outbound

This paper cites Language Models are Few-Shot Butlers.

WALL-E 2.0: World Alignment by NeuroSymbolic Learning improves World Model-based LLM Agents Language Models are Few-Shot Butlers

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-16T11:23:34.979110Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 81081c5a-76ad-4cd6-8029-7fe86da35a89 · outbound

This paper cites Can LLMs Follow Simple Rules?.

WALL-E 2.0: World Alignment by NeuroSymbolic Learning improves World Model-based LLM Agents Can LLMs Follow Simple Rules?

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-16T11:23:34.983922Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf5a3cf6-c8e9-49a2-817e-79b8603ec493 · outbound

This paper cites Proximal Policy Optimization Algorithms.

WALL-E 2.0: World Alignment by NeuroSymbolic Learning improves World Model-based LLM Agents Proximal Policy Optimization Algorithms

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-16T11:23:34.987387Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 002b68d1-30ae-46de-9e23-c4edae3391b3 · outbound

This paper cites ALFWorld: Aligning Text and Embodied Environments for Interactive Learning.

WALL-E 2.0: World Alignment by NeuroSymbolic Learning improves World Model-based LLM Agents ALFWorld: Aligning Text and Embodied Environments for Interactive Learning

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-16T11:23:34.995788Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fad37ba7-765a-48ae-95b5-27632166be69 · outbound

This paper cites WorldCoder, a Model-Based LLM Agent: Building World Models by Writing Code and Interacting with the Environment.

WALL-E 2.0: World Alignment by NeuroSymbolic Learning improves World Model-based LLM Agents WorldCoder, a Model-Based LLM Agent: Building World Models by Writing Code and Interacting with the Environment

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-16T11:23:35.063040Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 12965d76-8a4a-4c3d-a3ab-4902c32a1ac7 · outbound

This paper cites AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation.

WALL-E 2.0: World Alignment by NeuroSymbolic Learning improves World Model-based LLM Agents AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-16T11:23:35.165317Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 25cb27ca-bc7a-4a79-b0d9-50da628c93f1 · outbound

This paper cites AutoHallusion: Automatic Generation of Hallucination Benchmarks for Vision-Language Models.

WALL-E 2.0: World Alignment by NeuroSymbolic Learning improves World Model-based LLM Agents AutoHallusion: Automatic Generation of Hallucination Benchmarks for Vision-Language Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-16T11:23:35.170484Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9bc48c44-b96c-4d8e-a06a-211c0d0db9f9 · outbound

This paper cites Making Large Language Models into World Models with Precondition and Effect Knowledge.

WALL-E 2.0: World Alignment by NeuroSymbolic Learning improves World Model-based LLM Agents Making Large Language Models into World Models with Precondition and Effect Knowledge

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-16T11:23:35.174374Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fcb084c1-4fd3-45c5-8731-8d2ea0910aab · outbound

This paper cites Distilling Rule-based Knowledge into Large Language Models.

WALL-E 2.0: World Alignment by NeuroSymbolic Learning improves World Model-based LLM Agents Distilling Rule-based Knowledge into Large Language Models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-16T11:23:35.177709Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 408d6e04-f94f-4611-96b2-40f5fc579868 · outbound

This paper cites Failures Pave the Way: Enhancing Large Language Models through Tuning-free Rule Accumulation.

WALL-E 2.0: World Alignment by NeuroSymbolic Learning improves World Model-based LLM Agents Failures Pave the Way: Enhancing Large Language Models through Tuning-free Rule Accumulation

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-16T11:23:35.180569Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5d767370-c9d3-42bf-a774-1130256d6c53 · outbound

This paper cites WALL-E: World Alignment by Rule Learning Improves World Model-based LLM Agents.

WALL-E 2.0: World Alignment by NeuroSymbolic Learning improves World Model-based LLM Agents WALL-E: World Alignment by Rule Learning Improves World Model-based LLM Agents

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-16T11:23:35.183907Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa7c1634-d81e-4874-9e24-8640756080e8 · outbound

This paper cites MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models.

WALL-E 2.0: World Alignment by NeuroSymbolic Learning improves World Model-based LLM Agents MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-16T11:23:35.187414Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f824eb6-8bdb-4d25-b811-3e3b66269490 · outbound

This paper cites an unresolved cited work.

WALL-E 2.0: World Alignment by NeuroSymbolic Learning improves World Model-based LLM Agents Unresolved cited work

Reference 25

Resolution: unresolved; raw_fallback, observed 2026-08-16T11:23:35.704979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2e2f28ea-c108-4910-b38e-fdbfb4faa2ce · outbound

This paper cites an unresolved cited work.

WALL-E 2.0: World Alignment by NeuroSymbolic Learning improves World Model-based LLM Agents Unresolved cited work

Reference 26

Resolution: unresolved; raw_fallback, observed 2026-08-16T11:23:35.693880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8ce34c54-add4-4fd8-aaf1-0cd58d00fe40 · outbound

This paper cites Action failed: Not enough {resource} to make {tool_name}.

WALL-E 2.0: World Alignment by NeuroSymbolic Learning improves World Model-based LLM Agents Action failed: Not enough {resource} to make {tool_name}

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:23:35.543696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0844abc8-e8fc-4dbe-be49-c2a86f79b888 · outbound

This paper cites Reflexion: Language Agents with Verbal Reinforcement Learning.

WALL-E 2.0: World Alignment by NeuroSymbolic Learning improves World Model-based LLM Agents Reflexion: Language Agents with Verbal Reinforcement Learning

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-16T11:23:34.991247Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 842169d1-ed27-4c45-8818-7a784ebf42b1 · outbound

This paper cites Physically Grounded Vision-Language Models for Robotic Manipulation.

WALL-E 2.0: World Alignment by NeuroSymbolic Learning improves World Model-based LLM Agents Physically Grounded Vision-Language Models for Robotic Manipulation

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-16T11:23:34.792958Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 119d13e4-205d-4603-a080-767800b1e367 · outbound

This paper cites Mastering Diverse Domains through World Models.

WALL-E 2.0: World Alignment by NeuroSymbolic Learning improves World Model-based LLM Agents Mastering Diverse Domains through World Models

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-16T11:23:34.800527Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2630d68f-d42f-4cc6-b9cc-34d75cd0de1d · outbound

This paper cites Learning adaptive planning representations with natural language guidance.

WALL-E 2.0: World Alignment by NeuroSymbolic Learning improves World Model-based LLM Agents Learning adaptive planning representations with natural language guidance

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-16T11:23:35.133282Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64766794-f8cd-4386-89a4-4b03de697775 · outbound

This paper cites Benchmarking the Spectrum of Agent Capabilities.

WALL-E 2.0: World Alignment by NeuroSymbolic Learning improves World Model-based LLM Agents Benchmarking the Spectrum of Agent Capabilities

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-16T11:23:34.796804Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a0b70f27-c3c6-45b8-853a-d47a95f7ce52 · outbound

This paper cites Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AI.

WALL-E 2.0: World Alignment by NeuroSymbolic Learning improves World Model-based LLM Agents Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AI

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-16T11:23:34.827015Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 381b2162-d7f1-4777-a4eb-60a0063f497a · 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 2.0: World Alignment by NeuroSymbolic Learning improves World Model-based LLM Agents

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-03T21:20:40.795352Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da0d837b-eb56-46b4-8b67-6b4ca753ecba · inbound

Kintsugi: Learning Policies by Repairing Executable Knowledge Bases cites this paper.

Kintsugi: Learning Policies by Repairing Executable Knowledge Bases WALL-E 2.0: World Alignment by NeuroSymbolic Learning improves World Model-based LLM Agents

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-12T06:31:28.588631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7dcc7dfa-c9a0-48f2-ada5-7352a5bafeeb · inbound

Grounded Continuation: A Linear-Time Runtime Verifier for LLM Conversations cites this paper.

Grounded Continuation: A Linear-Time Runtime Verifier for LLM Conversations WALL-E 2.0: World Alignment by NeuroSymbolic Learning improves World Model-based LLM Agents

Reference 24

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T04:49:44.477072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 90503fca-99b0-4e97-b4d3-dfb8bc452255 · inbound

Baba in Wonderland: Online Self-Supervised Dynamics Discovery for Executable World Models cites this paper.

Baba in Wonderland: Online Self-Supervised Dynamics Discovery for Executable World Models WALL-E 2.0: World Alignment by NeuroSymbolic Learning improves World Model-based LLM Agents

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-05-19T21:47:48.357306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.