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Paper Citation Record · LEDGER

Planning with Transformers: Chain of Computation and Structured Context Windows

As of 9 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2607.17710.

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

pith.paper-citation-record.v1
2607.17710 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T17:18:42.149205Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+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

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Source: cited_works

Reference resolution

30 of 30 outbound references displayed

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  • verified fuzzy0
  • unresolved29
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

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Outbound references

Observation c0f6a350-3e51-4f62-96a2-ea255f04d776 · outbound

This paper cites GPT-4 Technical Report.

Planning with Transformers: Chain of Computation and Structured Context Windows GPT-4 Technical Report

Reference 1

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source=pdf_text observed=2026-08-01T17:18:38.398497Z digest=sha256:783176b72c26c85b55254ea41dfd4f55f91927d307ac484144827e59ae5630ca

Observation e76e1cba-590f-4b6e-91c9-524fe72df38f · outbound

This paper cites Thestepvariable indicates the order of execution from initialization to termination.

Planning with Transformers: Chain of Computation and Structured Context Windows Thestepvariable indicates the order of execution from initialization to termination

Reference 3

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source=pdf_text observed=2026-08-01T17:18:42.149205Z digest=sha256:989d98cbddaf2c5b0a7d75553cfdd3d6067583a654e2488e6402973b24bb7d02

Observation 7a88cd51-cf8b-431e-86a8-247ef7c5bc72 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Planning with Transformers: Chain of Computation and Structured Context Windows Evaluating Large Language Models Trained on Code

Reference 6

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source=pdf_text observed=2026-08-01T17:18:38.859742Z digest=sha256:c1e59737816f2ed22a827cfc0c54e597b508745b9a633adac566f14222c97826

Observation 59ea056b-2976-4d64-95b0-82d5dde0a648 · outbound

This paper cites Looped transformers for length generalization.

Planning with Transformers: Chain of Computation and Structured Context Windows Looped transformers for length generalization

Reference 9

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source=pdf_text observed=2026-08-01T17:18:39.240563Z digest=sha256:219a95c19484c11e48e9c5dd490c19f593610bd8e7200bb2eb4a51a8615f87e2

Observation 759c045a-bf7d-4669-ab8f-bdc7f946f9f5 · outbound

This paper cites Stream of Search (SoS): Learning to Search in Language.

Planning with Transformers: Chain of Computation and Structured Context Windows Stream of Search (SoS): Learning to Search in Language

Reference 10

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source=pdf_text observed=2026-08-01T17:18:39.379408Z digest=sha256:0c99809e356ba27f3e514f556e056e28904aab38da9f3ce323f6e28b120bcb00

Observation b5f3c812-9822-40c6-8543-549e751525ae · outbound

This paper cites Reasoning with language model is planning with world model.

Planning with Transformers: Chain of Computation and Structured Context Windows Reasoning with language model is planning with world model

Reference 13

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source=pdf_text observed=2026-08-01T17:18:39.853693Z digest=sha256:cf650a29c0daa39cc912212860585bd207a3f1123a69e96d5a0df051b35f062b

Observation df9bd8ec-48ed-4742-b9d6-7832a232a5f2 · outbound

This paper cites Gaussian Error Linear Units (GELUs).

Planning with Transformers: Chain of Computation and Structured Context Windows Gaussian Error Linear Units (GELUs)

Reference 14

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source=pdf_text observed=2026-08-01T17:18:40.003679Z digest=sha256:5ab6546461cd022dfdc5ccdc3cfdabe398b9090230cd29ec9818113d93c5cc49

Observation a05fb409-82b0-4c68-a7e0-9ac355bda89f · outbound

This paper cites Qwen2.5-Coder Technical Report.

Planning with Transformers: Chain of Computation and Structured Context Windows Qwen2.5-Coder Technical Report

Reference 15

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source=pdf_text observed=2026-08-01T17:18:40.136987Z digest=sha256:abcdfc06a254711f66e19e7f8a58923428a14c861144b6a533e70ad0fbf71216

Observation b6cf2210-1138-4137-8460-ceccb9cbb8d0 · outbound

This paper cites Be- yond a*: Better LLM planning via search dynamics bootstrapping.

Planning with Transformers: Chain of Computation and Structured Context Windows Be- yond a*: Better LLM planning via search dynamics bootstrapping

Reference 16

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source=pdf_text observed=2026-08-01T17:18:40.255844Z digest=sha256:a5b772442f3d8c1c117e7d4e03aad94268ac3f1fab9aebb3335976048e7621c9

Observation 26e99e1a-2151-4ad7-b33c-2573cd8993e8 · outbound

This paper cites Alex Lewandowski, Marlos C Machado, and Dale Schuurmans.

Planning with Transformers: Chain of Computation and Structured Context Windows Alex Lewandowski, Marlos C Machado, and Dale Schuurmans

Reference 17

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source=pdf_text observed=2026-08-01T17:18:40.434145Z digest=sha256:14606656c519ee9928b9cc12a7ff56bb5ccda85839bb1a1a156cbda5c12fe965

Observation 2fdee32c-fa3f-4a4e-b882-29a80e16a39c · outbound

This paper cites Llm-a*: Large language model enhanced incremental heuristic search on path planning.

Planning with Transformers: Chain of Computation and Structured Context Windows Llm-a*: Large language model enhanced incremental heuristic search on path planning

Reference 18

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source=pdf_text observed=2026-08-01T17:18:40.555499Z digest=sha256:f4ca0aa7f4e20fbcba7a01ba7828cd6bb7132af27f599d12bcef7c4584652a4e

Observation 14b8ecb1-2806-4b59-a7a6-96ae8d8ee815 · outbound

This paper cites Show Your Work: Scratchpads for Intermediate Computation with Language Models.

Planning with Transformers: Chain of Computation and Structured Context Windows Show Your Work: Scratchpads for Intermediate Computation with Language Models

Reference 19

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source=pdf_text observed=2026-08-01T17:18:40.678943Z digest=sha256:968cf21e37061c7a3176cf1946906d3bfc41d975abe9e2a19e5911fa560a80fe

Observation a6076de6-7838-4fe8-ad31-24a72e64d59e · outbound

This paper cites Understanding the Capabilities of Large Language Models for Automated Planning.

Planning with Transformers: Chain of Computation and Structured Context Windows Understanding the Capabilities of Large Language Models for Automated Planning

Reference 20

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source=pdf_text observed=2026-08-01T17:18:40.860933Z digest=sha256:77e7b4fd09f6afde22236e373a198c20070e889bc6c726cb0b5da1f0f79a1a23

Observation 9bd159d0-e3e5-41ef-a4e8-8f73c8b44b43 · outbound

This paper cites Memory Augmented Large Language Models are Computationally Universal.

Planning with Transformers: Chain of Computation and Structured Context Windows Memory Augmented Large Language Models are Computationally Universal

Reference 21

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source=pdf_text observed=2026-08-01T17:18:40.997799Z digest=sha256:6e4e5e565928bebe17f4fc7b9113fe23e60a60757bf08b95fe141b654a0b6644

Observation 657ce2b3-87b2-4d9f-88b6-9d2c2019fde4 · outbound

This paper cites Challenging big-bench tasks and whether chain-of-thought can solve them.

Planning with Transformers: Chain of Computation and Structured Context Windows Challenging big-bench tasks and whether chain-of-thought can solve them

Reference 22

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source=pdf_text observed=2026-08-01T17:18:41.123806Z digest=sha256:b56031cadd6f08cf53a74546da795b8eb1682a1fb64752ed2ac25a340f9ea4bb

Observation 58b2cd81-6b5d-4086-aa5f-b8b2a56cd1ae · outbound

This paper cites Llms as planning formalizers: A survey for leveraging large language models to construct automated planning models.

Planning with Transformers: Chain of Computation and Structured Context Windows Llms as planning formalizers: A survey for leveraging large language models to construct automated planning models

Reference 23

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source=pdf_text observed=2026-08-01T17:18:41.228184Z digest=sha256:becd42ddac9647a29241b04ff3a98bb840ad87d7e5eeaa7bb5db1e0cd8f69cba

Observation 644d953f-e63c-46ac-9622-f0479613857a · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

Planning with Transformers: Chain of Computation and Structured Context Windows Gemini: A Family of Highly Capable Multimodal Models

Reference 24

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source=pdf_text observed=2026-08-01T17:18:41.350098Z digest=sha256:243e38b96624068cac2c40064524d011c66da7980e57c8b3f66b144bf497313a

Observation 8037faa6-0363-486b-bcae-abaf21e32e15 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Planning with Transformers: Chain of Computation and Structured Context Windows Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 25

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source=pdf_text observed=2026-08-01T17:18:41.506160Z digest=sha256:cb2330771c32038983fcb936df9831a74b6b6503a3265c9404b52d175e3e3a82

Observation d16c0d3b-06cb-42bb-9109-5ec14bd1f4e3 · outbound

This paper cites BloombergGPT: A Large Language Model for Finance.

Planning with Transformers: Chain of Computation and Structured Context Windows BloombergGPT: A Large Language Model for Finance

Reference 26

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source=pdf_text observed=2026-08-01T17:18:41.627558Z digest=sha256:cc50f4d73f9fe1065cc237b2da70a9b0ee0530f14e8a79c58fe431373f5556d1

Observation 24f9c260-e793-4b9a-9459-a04171579dd1 · outbound

This paper cites Trading-r1: Financial trading with llm reasoning via reinforcement learning.arXiv preprint arXiv:2509.11420,.

Planning with Transformers: Chain of Computation and Structured Context Windows Trading-r1: Financial trading with llm reasoning via reinforcement learning.arXiv preprint arXiv:2509.11420,

Reference 27

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Observation 766fbe35-09e1-45b9-b5aa-574bcd3f1466 · outbound

This paper cites Training language models with memory augmentation.

Planning with Transformers: Chain of Computation and Structured Context Windows Training language models with memory augmentation

Reference 28

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Observation 1e6e579c-1af4-44bb-97cc-75c1c014453e · outbound

This paper cites doi: https://doi.org/10.

Planning with Transformers: Chain of Computation and Structured Context Windows doi: https://doi.org/10

Reference 1979

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Observation 2f0beeae-663b-4ca6-a5b7-0b02a25101da · outbound

This paper cites an unresolved cited work.

Planning with Transformers: Chain of Computation and Structured Context Windows Unresolved cited work

Reference 2016

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source=pdf_text observed=2026-08-01T17:18:42.023796Z digest=sha256:21b5cef6f4d21b4d8f4d0a43038bb2c4bd6b7786ac4664734420f7708d45aa6f

Observation 57965025-4a3e-422b-8666-115f9fbd73fd · outbound

This paper cites Exploring and Benchmarking the Planning Capabilities of Large Language Models.

Planning with Transformers: Chain of Computation and Structured Context Windows Exploring and Benchmarking the Planning Capabilities of Large Language Models

Reference 2020

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source=pdf_text observed=2026-08-01T17:18:38.657377Z digest=sha256:dddb4e0b524b920e253ccda6d32aa8c6f70b34880c957b06d4d634f9bdb152aa

Observation 1414f32a-f96b-4a5f-b73b-f013f5bda1a7 · outbound

This paper cites Dast: Context-aware compression in llms via dynamic allocation of soft tokens.

Planning with Transformers: Chain of Computation and Structured Context Windows Dast: Context-aware compression in llms via dynamic allocation of soft tokens

Reference 2021

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source=pdf_text observed=2026-08-01T17:18:38.984690Z digest=sha256:8aabaea79f28ae01ef1fdfc051c5a000a89e360694c175f61cd67ebd4fe79234

Observation 83995dcd-5f03-48f0-bf84-dec80b22fdfb · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Planning with Transformers: Chain of Computation and Structured Context Windows DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 2022

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source=pdf_text observed=2026-08-01T17:18:39.697116Z digest=sha256:48c9d551567ae9e8becad7c5188a0d7cdffadb4b8f2fc73ac030161c9b5579bd

Observation 24251afb-05f7-4bf9-81a9-b4cee30c161d · outbound

This paper cites Can large language models be good path planners? a benchmark and investigation on spatial-temporal reasoning.

Planning with Transformers: Chain of Computation and Structured Context Windows Can large language models be good path planners? a benchmark and investigation on spatial-temporal reasoning

Reference 2023

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source=pdf_text observed=2026-08-01T17:18:38.453516Z digest=sha256:4c9f2bdfc345ef491025cedac0b6b544a95dcfaaa725c4cd16dd21f05d7ec234

Observation 181380ca-30ec-4039-a663-f2cc6942c12d · outbound

This paper cites Analysis of Optimality of Large Language Models on Planning Problems.

Planning with Transformers: Chain of Computation and Structured Context Windows Analysis of Optimality of Large Language Models on Planning Problems

Reference 2024

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Observation 773bc3dd-48e0-4b02-8c57-bf39fd4bee62 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Planning with Transformers: Chain of Computation and Structured Context Windows Training Verifiers to Solve Math Word Problems

Reference 2025

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source=pdf_text observed=2026-08-01T17:18:39.137340Z digest=sha256:618c4653754077e9b55166525eeeafe3c70523f0cd7fd26081daf8b1cb416aad

Observation d8bc1edb-79e0-468a-adf8-afb783ba7987 · outbound

This paper cites URLhttps://ojs.aaai.org/index.php/ AAAI/article/view/42130.

Planning with Transformers: Chain of Computation and Structured Context Windows URLhttps://ojs.aaai.org/index.php/ AAAI/article/view/42130

Reference 2026

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source=pdf_text observed=2026-08-01T17:18:38.539613Z digest=sha256:fd11425fae76dcb19ed30307dfd5dc47f198199ee405b08e7f399c629fda78b6

Pith citing papers

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