Paper Citation Record · LEDGER

Monte Carlo Planning with Large Language Model for Text-Based Game Agents

As of 17 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 6 inbound Pith citation observations for arXiv:2504.16855.

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

pith.paper-citation-record.v1
2504.16855 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T10:58:42.280986Z

measured 36 of 36 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 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:26:54.346148Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T20:10:08.747369Z

Reference resolution

30 of 30 outbound references displayed

verified exact5
verified fuzzy8
unresolved17
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ff778810-f747-409e-8bef-9d010d50d7d3 · outbound

This paper cites How to Avoid Being Eaten by a Grue: Structured Exploration Strategies for Textual Worlds.

Monte Carlo Planning with Large Language Model for Text-Based Game Agents How to Avoid Being Eaten by a Grue: Structured Exploration Strategies for Textual Worlds

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-16T10:58:41.471848Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21f828f2-588e-4c7c-a578-d3d47d608442 · outbound

This paper cites Case-based Reasoning for Better Generalization in Textual Reinforcement Learning.

Monte Carlo Planning with Large Language Model for Text-Based Game Agents Case-based Reasoning for Better Generalization in Textual Reinforcement Learning

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-16T10:58:41.478117Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee3a5b0f-63ef-48d4-98b8-cfaa9dfc1784 · outbound

This paper cites Textworld: A learning environment for text-based games.

Monte Carlo Planning with Large Language Model for Text-Based Game Agents Textworld: A learning environment for text-based games

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:58:43.621518Z

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 80787254-a884-480d-849f-d596b64b8d5b · outbound

This paper cites Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation.

Monte Carlo Planning with Large Language Model for Text-Based Game Agents Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-16T10:58:41.795671Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 099b7e88-e573-4b7d-ad39-ea6e39b79166 · outbound

This paper cites Interactive Fiction Game Playing as Multi-Paragraph Reading Comprehension with Reinforcement Learning.

Monte Carlo Planning with Large Language Model for Text-Based Game Agents Interactive Fiction Game Playing as Multi-Paragraph Reading Comprehension with Reinforcement Learning

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-08-16T10:58:42.843248Z

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 b292a321-9235-4402-83ee-7468f1ba8276 · outbound

This paper cites Deep Reinforcement Learning with a Natural Language Action Space.

Monte Carlo Planning with Large Language Model for Text-Based Game Agents Deep Reinforcement Learning with a Natural Language Action Space

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-16T10:58:41.809175Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ba7f4161-655c-40e7-b08d-3002ebb5e69c · outbound

This paper cites Language Understanding for Text-based Games Using Deep Reinforcement Learning.

Monte Carlo Planning with Large Language Model for Text-Based Game Agents Language Understanding for Text-based Games Using Deep Reinforcement Learning

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-16T10:58:41.821570Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3cef94a7-8688-4c36-86ab-49e460074034 · outbound

This paper cites doi: 10.18653/v1/2022.acl-short.56.

Monte Carlo Planning with Large Language Model for Text-Based Game Agents doi: 10.18653/v1/2022.acl-short.56

Reference 16

Resolution: verified exact; doi, observed 2026-08-16T10:58:42.505736Z

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 444e21d2-6db1-4318-ac3a-e15f43fa7dca · outbound

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

Monte Carlo Planning with Large Language Model for Text-Based Game Agents ALFWorld: Aligning Text and Embodied Environments for Interactive Learning

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-16T10:58:42.028796Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ca0757d6-86c9-4911-9f39-c082a24d7333 · outbound

This paper cites LgTS: Dynamic Task Sampling using LLM-generated sub-goals for Reinforcement Learning Agents.

Monte Carlo Planning with Large Language Model for Text-Based Game Agents LgTS: Dynamic Task Sampling using LLM-generated sub-goals for Reinforcement Learning Agents

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-08-16T10:58:42.723751Z

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 b6fbcc82-fd56-4b65-87b3-dfbac51c42d4 · outbound

This paper cites Simplified Belief-Dependent Reward MCTS Planning with Guaranteed Tree Consistency.

Monte Carlo Planning with Large Language Model for Text-Based Game Agents Simplified Belief-Dependent Reward MCTS Planning with Guaranteed Tree Consistency

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-16T10:58:42.636929Z

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 3c8c2c90-00ce-4457-944d-b5a6a6227c20 · outbound

This paper cites Multi-Stage Episodic Control for Strategic Exploration in Text Games.

Monte Carlo Planning with Large Language Model for Text-Based Game Agents Multi-Stage Episodic Control for Strategic Exploration in Text Games

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-16T10:58:42.069518Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9202a18f-3493-4576-bf55-fd8ad9329ee3 · outbound

This paper cites Self-Consistency Improves Chain of Thought Reasoning in Language Models.

Monte Carlo Planning with Large Language Model for Text-Based Game Agents Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-16T10:58:42.074242Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 11ba4ba3-f1e2-4850-b1d3-b40e6d62d0b8 · outbound

This paper cites Generalization in text-based games via hierarchical reinforcement learning.

Monte Carlo Planning with Large Language Model for Text-Based Game Agents Generalization in text-based games via hierarchical reinforcement learning

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:58:43.522017Z

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 f8b596aa-78d2-4acc-9c88-06d30a9cfec1 · outbound

This paper cites doi: 10.18653/v1/2021.findings-emnlp.116.

Monte Carlo Planning with Large Language Model for Text-Based Game Agents doi: 10.18653/v1/2021.findings-emnlp.116

Reference 23

Resolution: verified exact; doi, observed 2026-08-16T10:58:42.407724Z

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 6717f30d-ac4c-4add-ab36-e423cc4eb871 · outbound

This paper cites URL https://aclanthology.org/2022.acl-long.41.

Monte Carlo Planning with Large Language Model for Text-Based Game Agents URL https://aclanthology.org/2022.acl-long.41

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:58:43.428123Z

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 73c8708e-943a-48fa-920e-b5b8bb39d7bd · outbound

This paper cites URL https://aclanthology.org/ 2020.emnlp-main.704.

Monte Carlo Planning with Large Language Model for Text-Based Game Agents URL https://aclanthology.org/ 2020.emnlp-main.704

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-16T10:58:42.089689Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ca7dcac9-3f12-44e7-b429-56ceba489924 · outbound

This paper cites Ghost in the Minecraft: Generally Capable Agents for Open-World Environments via Large Language Models with Text-based Knowledge and Memory.

Monte Carlo Planning with Large Language Model for Text-Based Game Agents Ghost in the Minecraft: Generally Capable Agents for Open-World Environments via Large Language Models with Text-based Knowledge and Memory

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-16T10:58:42.196567Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac7a2f3f-01ec-4451-a99d-6083ca784488 · outbound

This paper cites Ludicorp.

Monte Carlo Planning with Large Language Model for Text-Based Game Agents Ludicorp

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:58:43.304821Z

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 15792876-41d6-465c-baaf-e3b4a7a34724 · outbound

This paper cites C.1 P ROMPTS FOR ACTION VALUE ESTIMATES You are a player in a text-based adventure game.

Monte Carlo Planning with Large Language Model for Text-Based Game Agents C.1 P ROMPTS FOR ACTION VALUE ESTIMATES You are a player in a text-based adventure game

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:58:43.116004Z

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 09c0d79b-88ec-4341-9138-205214092f14 · outbound

This paper cites The above configuration follows the work of Jang et al.

Monte Carlo Planning with Large Language Model for Text-Based Game Agents The above configuration follows the work of Jang et al

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:58:43.234751Z

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 4415f38c-9c56-43b7-9703-46f5b45b8545 · outbound

This paper cites Plan-Seq-Learn: Language Model Guided RL for Solving Long Horizon Robotics Tasks.

Monte Carlo Planning with Large Language Model for Text-Based Game Agents Plan-Seq-Learn: Language Model Guided RL for Solving Long Horizon Robotics Tasks

Reference 2006

Resolution: unresolved; no resolver link, observed 2026-08-16T10:58:41.644515Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e7829bd-555c-4b9e-a5b6-9ab89f30f83f · outbound

This paper cites A survey of monte carlo tree search methods.

Monte Carlo Planning with Large Language Model for Text-Based Game Agents A survey of monte carlo tree search methods

Reference 2012

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:58:43.740678Z

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 965940d9-8d6a-446e-9725-e09f4bf8fa0d · outbound

This paper cites A survey of text games for reinforcement learning informed by natural language.

Monte Carlo Planning with Large Language Model for Text-Based Game Agents A survey of text games for reinforcement learning informed by natural language

Reference 2015

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:58:43.566875Z

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 c4358c12-2b0b-4944-bde2-38d7a09c052c · outbound

This paper cites How Can LLM Guide RL? A Value-Based Approach.

Monte Carlo Planning with Large Language Model for Text-Based Game Agents How Can LLM Guide RL? A Value-Based Approach

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-16T10:58:42.136576Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5b5f381c-a3bc-407a-8835-ede5b20b9c36 · outbound

This paper cites Graph Constrained Reinforcement Learning for Natural Language Action Spaces.

Monte Carlo Planning with Large Language Model for Text-Based Game Agents Graph Constrained Reinforcement Learning for Natural Language Action Spaces

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-16T10:58:41.466534Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 321daf36-88ca-4450-851f-bb42a8370e9c · outbound

This paper cites Teaching Models to Express Their Uncertainty in Words.

Monte Carlo Planning with Large Language Model for Text-Based Game Agents Teaching Models to Express Their Uncertainty in Words

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-16T10:58:41.813331Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee958864-4d1e-41be-853f-35a6b9e4f201 · outbound

This paper cites RL-GPT: Integrating Reinforcement Learning and Code-as-policy.

Monte Carlo Planning with Large Language Model for Text-Based Game Agents RL-GPT: Integrating Reinforcement Learning and Code-as-policy

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-16T10:58:41.817767Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 96ac6684-00c6-4070-9c37-3faa5a4917ac · outbound

This paper cites Large Language Models Are Neurosymbolic Reasoners.

Monte Carlo Planning with Large Language Model for Text-Based Game Agents Large Language Models Are Neurosymbolic Reasoners

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-16T10:58:41.800046Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 629d17d3-0239-487e-bc18-b33b1d761149 · outbound

This paper cites Playing Text-Based Games with Common Sense.

Monte Carlo Planning with Large Language Model for Text-Based Game Agents Playing Text-Based Games with Common Sense

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-16T10:58:41.747857Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 9c4adaf8-db09-492c-8c1f-ed2f65d465a9 · inbound

Graphs Meet AI Agents: Taxonomy, Progress, and Future Opportunities cites this paper.

Graphs Meet AI Agents: Taxonomy, Progress, and Future Opportunities Monte Carlo Planning with Large Language Model for Text-Based Game Agents

Reference 187

Resolution: unresolved; no resolver link, observed 2026-08-06T23:26:54.346148Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6deae6a7-97d6-490c-9737-0517f8b9f0e8 · inbound

UIPress: Bringing Optical Token Compression to UI-to-Code Generation cites this paper.

UIPress: Bringing Optical Token Compression to UI-to-Code Generation Monte Carlo Planning with Large Language Model for Text-Based Game Agents

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-11T07:00:59.502195Z

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 29d94bbd-7169-4a71-9c86-05ae5ab5a8f2 · inbound

Multi-Agent Systems: From Classical Paradigms to Large Foundation Model-Enabled Futures cites this paper.

Multi-Agent Systems: From Classical Paradigms to Large Foundation Model-Enabled Futures Monte Carlo Planning with Large Language Model for Text-Based Game Agents

Reference 70

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T11:51:03.714474Z

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 702c3b56-2c7a-41ff-9b30-4c9f824ac5c3 · inbound

LongSeeker: Elastic Context Orchestration for Long-Horizon Search Agents cites this paper.

LongSeeker: Elastic Context Orchestration for Long-Horizon Search Agents Monte Carlo Planning with Large Language Model for Text-Based Game Agents

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-11T17:16:07.309015Z

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 5478b128-463a-499c-b114-43d706664dfe · inbound

PriorZero: Bridging Language Priors and World Models for Decision Making cites this paper.

PriorZero: Bridging Language Priors and World Models for Decision Making Monte Carlo Planning with Large Language Model for Text-Based Game Agents

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-13T05:27:18.575058Z

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 f73985d3-5427-4c3f-887e-269cd4ca2d40 · inbound

GUI agent: Guided Exploration of User-Sensitive Screens cites this paper.

GUI agent: Guided Exploration of User-Sensitive Screens Monte Carlo Planning with Large Language Model for Text-Based Game Agents

Reference 15

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T20:10:08.749357Z

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.