Paper Citation Record · LEDGER

GPT as a Monte Carlo Language Tree: A Probabilistic Perspective

As of 15 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2501.07641.

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

pith.paper-citation-record.v1
2501.07641 v2

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T20:43:03.591882Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

45 of 45 outbound references displayed

verified exact2
verified fuzzy11
unresolved32
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 65f1f354-1507-4a10-a549-8f6d45144dd4 · outbound

This paper cites GPT-4 Technical Report.

GPT as a Monte Carlo Language Tree: A Probabilistic Perspective GPT-4 Technical Report

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-10T20:43:03.386630Z

Source-reported events for the cited work

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Observation 5a0a7101-f3f4-4bf2-a6f0-2f0415c46fb3 · outbound

This paper cites Lossless data compression with neural networks.

GPT as a Monte Carlo Language Tree: A Probabilistic Perspective Lossless data compression with neural networks

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:43:04.278243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 851c3b92-74e3-46d5-9c78-e48c6dca0407 · outbound

This paper cites Graph of thoughts: Solving elaborate problems with large language mod- els.

GPT as a Monte Carlo Language Tree: A Probabilistic Perspective Graph of thoughts: Solving elaborate problems with large language mod- els

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:43:04.262028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ce68b228-0251-41ee-b7f0-b1f507f7c906 · outbound

This paper cites Gpt-neo: Large scale autoregressive language modeling with mesh-tensorflow.

GPT as a Monte Carlo Language Tree: A Probabilistic Perspective Gpt-neo: Large scale autoregressive language modeling with mesh-tensorflow

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:43:04.247535Z

Source-reported events for the cited work

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Observation 103e6564-7850-413a-b7f0-68cd82392511 · outbound

This paper cites GPT-NeoX-20B: An Open-Source Autoregressive Language Model.

GPT as a Monte Carlo Language Tree: A Probabilistic Perspective GPT-NeoX-20B: An Open-Source Autoregressive Language Model

Reference 5

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Observation 55d83e67-9160-4439-99bd-32b0fc44cca1 · outbound

This paper cites Language models are few-shot learners.

GPT as a Monte Carlo Language Tree: A Probabilistic Perspective Language models are few-shot learners

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-10T20:43:03.410260Z

Source-reported events for the cited work

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Observation d18989b6-cbd5-4d84-95e7-0dc650a9a000 · outbound

This paper cites Adapting Language Models to Compress Contexts.

GPT as a Monte Carlo Language Tree: A Probabilistic Perspective Adapting Language Models to Compress Contexts

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T20:43:03.415071Z

Source-reported events for the cited work

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Observation 9efacb2b-1d11-4b8a-9eba-292043032431 · outbound

This paper cites Language Modeling Is Compression.

GPT as a Monte Carlo Language Tree: A Probabilistic Perspective Language Modeling Is Compression

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-10T20:43:03.420206Z

Source-reported events for the cited work

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Observation e7645218-acfc-4e75-a5b5-4c42abd4ec03 · outbound

This paper cites Mathematical capabilities of chatgpt.

GPT as a Monte Carlo Language Tree: A Probabilistic Perspective Mathematical capabilities of chatgpt

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:43:04.222165Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 563c42c8-cf6b-4e2a-8b2e-c33b2957e670 · outbound

This paper cites The Pile: An 800GB Dataset of Diverse Text for Language Modeling.

GPT as a Monte Carlo Language Tree: A Probabilistic Perspective The Pile: An 800GB Dataset of Diverse Text for Language Modeling

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T20:43:03.429423Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 48890963-fe41-4a3b-8f53-356fbdb19da8 · outbound

This paper cites Ranking LLMs by compression.

GPT as a Monte Carlo Language Tree: A Probabilistic Perspective Ranking LLMs by compression

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-10T20:43:03.857329Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ef67e3ee-4872-423f-b76d-cc77cd696c9d · outbound

This paper cites Lossless and Near-Lossless Compression for Foundation Models.

GPT as a Monte Carlo Language Tree: A Probabilistic Perspective Lossless and Near-Lossless Compression for Foundation Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-10T20:43:03.439276Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ce7c11a-7582-4730-a469-228a3367d5cc · outbound

This paper cites A survey on halluci- nation in large language models: Principles, taxonomy, challenges, and open questions.

GPT as a Monte Carlo Language Tree: A Probabilistic Perspective A survey on halluci- nation in large language models: Principles, taxonomy, challenges, and open questions

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:43:04.206456Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 23c14be2-4a9e-4e07-a690-8d5e639be262 · outbound

This paper cites Compressing LLMs: The Truth is Rarely Pure and Never Simple.

GPT as a Monte Carlo Language Tree: A Probabilistic Perspective Compressing LLMs: The Truth is Rarely Pure and Never Simple

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T20:43:03.448231Z

Source-reported events for the cited work

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Observation 0886e2aa-c2be-4c22-95a7-5adfdde40f0d · outbound

This paper cites A Peek into Token Bias: Large Language Models Are Not Yet Genuine Reasoners.

GPT as a Monte Carlo Language Tree: A Probabilistic Perspective A Peek into Token Bias: Large Language Models Are Not Yet Genuine Reasoners

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-10T20:43:03.453008Z

Source-reported events for the cited work

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Observation 68cd2d54-10d9-4142-accd-fdac8df076eb · outbound

This paper cites Can large language models reason and plan? Annals of the New York Academy of Sciences , 1534(1):15–18, 2024.

GPT as a Monte Carlo Language Tree: A Probabilistic Perspective Can large language models reason and plan? Annals of the New York Academy of Sciences , 1534(1):15–18, 2024

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-10T20:43:03.457511Z

Source-reported events for the cited work

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Observation 8182b9c2-fdd1-4dae-ae3d-23b972746a49 · outbound

This paper cites Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate.

GPT as a Monte Carlo Language Tree: A Probabilistic Perspective Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T20:43:03.461952Z

Source-reported events for the cited work

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Observation 595012f4-625d-4074-9c75-a4067caa8f86 · outbound

This paper cites Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing.

GPT as a Monte Carlo Language Tree: A Probabilistic Perspective Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:43:04.181874Z

Source-reported events for the cited work

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Observation 3932ed41-6674-4adb-8ddd-fcbfc193e2e2 · outbound

This paper cites Large Language Model Guided Tree-of-Thought.

GPT as a Monte Carlo Language Tree: A Probabilistic Perspective Large Language Model Guided Tree-of-Thought

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-10T20:43:03.471718Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b0512ed8-c8e2-454e-bb9c-45531bb70d43 · outbound

This paper cites GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models.

GPT as a Monte Carlo Language Tree: A Probabilistic Perspective GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T20:43:03.476531Z

Source-reported events for the cited work

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Observation 5592b647-d398-43ab-9cc1-5a8984828e29 · outbound

This paper cites AlphaZip: Neural Network-Enhanced Lossless Text Compression.

GPT as a Monte Carlo Language Tree: A Probabilistic Perspective AlphaZip: Neural Network-Enhanced Lossless Text Compression

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-10T20:43:03.746116Z

Source-reported events for the cited work

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Observation 088f0751-8c9e-4213-84b5-8337fe5589b0 · outbound

This paper cites A Comprehensive Overview of Large Language Models.

GPT as a Monte Carlo Language Tree: A Probabilistic Perspective A Comprehensive Overview of Large Language Models

Reference 22

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Observation b62707aa-e1d7-4657-873b-175cf8b5a265 · outbound

This paper cites Train- ing language models to follow instructions with human feedback.

GPT as a Monte Carlo Language Tree: A Probabilistic Perspective Train- ing language models to follow instructions with human feedback

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-10T20:43:03.489708Z

Source-reported events for the cited work

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Observation 8dd57002-250a-49d1-a717-685676ad7834 · outbound

This paper cites Improving language understanding by generative pre-training.

GPT as a Monte Carlo Language Tree: A Probabilistic Perspective Improving language understanding by generative pre-training

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-10T20:43:03.494090Z

Source-reported events for the cited work

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Observation 2cc96693-d5b0-46e8-98f2-0ecf2f5f0f2b · outbound

This paper cites Language models are unsupervised multitask learners.

GPT as a Monte Carlo Language Tree: A Probabilistic Perspective Language models are unsupervised multitask learners

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-10T20:43:03.498210Z

Source-reported events for the cited work

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Observation 8847b491-4b66-4baa-89ce-8776f691d85d · outbound

This paper cites Analysing Mathematical Reasoning Abilities of Neural Models.

GPT as a Monte Carlo Language Tree: A Probabilistic Perspective Analysing Mathematical Reasoning Abilities of Neural Models

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-10T20:43:03.503110Z

Source-reported events for the cited work

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Observation 83dbf6d6-b241-4e24-94df-9756d822b2d9 · outbound

This paper cites Are emergent abilities of large language models a mirage? Advances in Neural Information Processing Systems , 36, 2024.

GPT as a Monte Carlo Language Tree: A Probabilistic Perspective Are emergent abilities of large language models a mirage? Advances in Neural Information Processing Systems , 36, 2024

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-10T20:43:03.508071Z

Source-reported events for the cited work

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Observation 0a79489a-84e1-4bec-88f9-f94ad93eadff · outbound

This paper cites Large language models can be easily distracted by irrelevant context.

GPT as a Monte Carlo Language Tree: A Probabilistic Perspective Large language models can be easily distracted by irrelevant context

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:43:04.130082Z

Source-reported events for the cited work

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Observation 1f37f8cc-c829-48d2-abfb-e2462a5973d2 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

GPT as a Monte Carlo Language Tree: A Probabilistic Perspective LLaMA: Open and Efficient Foundation Language Models

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-10T20:43:03.517077Z

Source-reported events for the cited work

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Observation 18298b13-7219-41ac-bf4d-0e1c0581629e · outbound

This paper cites LLMZip: Lossless Text Compression using Large Language Models.

GPT as a Monte Carlo Language Tree: A Probabilistic Perspective LLMZip: Lossless Text Compression using Large Language Models

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-10T20:43:03.521827Z

Source-reported events for the cited work

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Observation b049b2bd-6bbb-4c3c-b44e-702ad3853917 · outbound

This paper cites Gpt-j-6b: A 6 billion parameter autoregressive language model, 2021.

GPT as a Monte Carlo Language Tree: A Probabilistic Perspective Gpt-j-6b: A 6 billion parameter autoregressive language model, 2021

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-10T20:43:03.526786Z

Source-reported events for the cited work

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Observation 699bfb71-977e-4b35-805b-24aa79645a00 · outbound

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

GPT as a Monte Carlo Language Tree: A Probabilistic Perspective Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-10T20:43:03.531425Z

Source-reported events for the cited work

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Observation ce9392cc-b71a-4476-b936-5ed9a374c5b3 · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models.

GPT as a Monte Carlo Language Tree: A Probabilistic Perspective Chain-of-thought prompting elicits reasoning in large language models

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-10T20:43:03.536066Z

Source-reported events for the cited work

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Observation e19d6e03-1f3e-4f97-aa08-58f1be0ecda1 · outbound

This paper cites LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples.

GPT as a Monte Carlo Language Tree: A Probabilistic Perspective LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-10T20:43:03.540810Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 87866d2f-33cf-4630-be05-ef56dbddc1fc · outbound

This paper cites Tree of thoughts: Deliberate problem solving with large language models.

GPT as a Monte Carlo Language Tree: A Probabilistic Perspective Tree of thoughts: Deliberate problem solving with large language models

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-10T20:43:03.546192Z

Source-reported events for the cited work

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Observation 7f21aea5-c57b-4fd2-b22d-b686bfa61383 · outbound

This paper cites Explainability for large language models: A survey.

GPT as a Monte Carlo Language Tree: A Probabilistic Perspective Explainability for large language models: A survey

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:43:04.077525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c893e6c9-e416-41bd-b594-da34865db08e · outbound

This paper cites A Survey of Large Language Models.

GPT as a Monte Carlo Language Tree: A Probabilistic Perspective A Survey of Large Language Models

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-10T20:43:03.555419Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dc15ee76-a86e-4b05-9069-382e99aa2ed4 · outbound

This paper cites Judging llm-as-a-judge with mt-bench and chatbot arena.

GPT as a Monte Carlo Language Tree: A Probabilistic Perspective Judging llm-as-a-judge with mt-bench and chatbot arena

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:43:04.061937Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 92c134aa-be54-4a36-9ced-7bae051fbb35 · outbound

This paper cites A survey on model com- pression for large language models.

GPT as a Monte Carlo Language Tree: A Probabilistic Perspective A survey on model com- pression for large language models

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:43:04.048129Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 451de0d1-5a53-4e4f-bb19-d174af7ddaf4 · outbound

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GPT as a Monte Carlo Language Tree: A Probabilistic Perspective Unresolved cited work

Reference 40

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 083c3d03-9151-4622-85bc-10253fcdada7 · outbound

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GPT as a Monte Carlo Language Tree: A Probabilistic Perspective Unresolved cited work

Reference 41

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2b96de5a-0208-48d8-be98-4cdd8eb1c7cc · outbound

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GPT as a Monte Carlo Language Tree: A Probabilistic Perspective Unresolved cited work

Reference 42

Resolution: unresolved; raw_fallback, observed 2026-08-10T20:43:03.997562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 915d19f2-7c0c-4b2d-99c8-428c1469f455 · outbound

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GPT as a Monte Carlo Language Tree: A Probabilistic Perspective Unresolved cited work

Reference 43

Resolution: unresolved; raw_fallback, observed 2026-08-10T20:43:03.981800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 267c61b3-bbe0-4dc8-a4ec-3565f966ffe8 · outbound

This paper cites an unresolved cited work.

GPT as a Monte Carlo Language Tree: A Probabilistic Perspective Unresolved cited work

Reference 44

Resolution: unresolved; raw_fallback, observed 2026-08-10T20:43:03.964085Z

Source-reported events for the cited work

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Observation dcc79fce-9cd3-4bc4-a570-abc7712ea42f · outbound

This paper cites Data-Tree.

GPT as a Monte Carlo Language Tree: A Probabilistic Perspective Data-Tree

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:43:03.949847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

No inbound Pith citation observations are available.