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Paper Citation Record · LEDGER

Chained Recursive Language Models for Multi-Iteration Reasoning

As of 10 August 2026, this Paper Citation Record lists 71 of 71 outbound references and 0 inbound Pith citation observations for arXiv:2608.05124.

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

pith.paper-citation-record.v1
2608.05124 v1

Coverage vector

measured 71 of 71 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T04:47:22.379825Z

measured 71 of 71 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

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

Source: cited_works

Reference resolution

71 of 71 outbound references displayed

  • verified exact0
  • verified fuzzy5
  • unresolved65
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 737f6840-d2ec-43dd-a857-a0ca3be9f53b · outbound

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

Chained Recursive Language Models for Multi-Iteration Reasoning DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 1

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source=arxiv_source observed=2026-08-06T04:47:16.859775Z digest=sha256:28a50bb642975de1121ae76b533f4fab739e4b5af4e5a1b7c20e54817239599c

Observation 759eb8bb-33e0-4479-9228-5485f17e266b · outbound

This paper cites Iteration of Thought: Leveraging Inner Dialogue for Autonomous Large Language Model Reasoning.

Chained Recursive Language Models for Multi-Iteration Reasoning Iteration of Thought: Leveraging Inner Dialogue for Autonomous Large Language Model Reasoning

Reference 2

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source=arxiv_source observed=2026-08-06T04:47:16.897740Z digest=sha256:a89ee083384196b01d965906ccf8c463c198d71214a5f8e0ccf71db32d050f74

Observation 0a70cbe9-9b52-4f1b-8916-9ca7f7c6f9b6 · outbound

This paper cites arXiv preprint arXiv:2503.06692 , year=.

Chained Recursive Language Models for Multi-Iteration Reasoning arXiv preprint arXiv:2503.06692 , year=

Reference 3

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source=arxiv_source observed=2026-08-06T04:47:16.966657Z digest=sha256:dfe14183d07dd8ba772e93f04def9c88650b78bec7911b33b59c6ac10988b1a4

Observation 28cdc79b-ece3-4c8e-8b47-9b26625ce920 · outbound

This paper cites Think Twice: Enhancing LLM Reasoning by Scaling Multi-round Test-time Thinking.

Chained Recursive Language Models for Multi-Iteration Reasoning Think Twice: Enhancing LLM Reasoning by Scaling Multi-round Test-time Thinking

Reference 4

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source=arxiv_source observed=2026-08-06T04:47:17.027482Z digest=sha256:43849f87313808bdb17a6859ee172406ab7efd99857c060697d9bfbe8e36464e

Observation a95fed0d-e460-44bb-847c-e416f22ac0de · outbound

This paper cites Is Depth All You Need? An Exploration of Iterative Reasoning in LLMs.

Chained Recursive Language Models for Multi-Iteration Reasoning Is Depth All You Need? An Exploration of Iterative Reasoning in LLMs

Reference 5

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source=arxiv_source observed=2026-08-06T04:47:17.211671Z digest=sha256:c5ed6e1434f01971123ed2e338dd67d4518d1c0a62fea6d6374a0237365896ee

Observation f7674e92-eec6-4e79-b192-36a5a2754d15 · outbound

This paper cites Synthetic Data Generation & Multi-Step RL for Reasoning & Tool Use.

Chained Recursive Language Models for Multi-Iteration Reasoning Synthetic Data Generation & Multi-Step RL for Reasoning & Tool Use

Reference 6

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source=arxiv_source observed=2026-08-06T04:47:17.289396Z digest=sha256:0ac6a32bc541770fe8e101179793560d238d9b96f78ae33647751f30ccf839aa

Observation 5c8ab0b2-ed54-4b69-9ebc-236fc3a34f0b · outbound

This paper cites Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models.

Chained Recursive Language Models for Multi-Iteration Reasoning Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models

Reference 7

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source=arxiv_source observed=2026-08-06T04:47:17.373551Z digest=sha256:ee2ae48e576c32644659f7af844eddbe436e6f9ca0437ecfe3b4d2a849d136b9

Observation 92709acd-b478-4318-8312-9c223723043e · outbound

This paper cites Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models.

Chained Recursive Language Models for Multi-Iteration Reasoning Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models

Reference 8

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source=arxiv_source observed=2026-08-06T04:47:17.450986Z digest=sha256:c2d50924625676b383d9c27096b8af652368308fc88c93aae3556abe9b121dca

Observation fab47b99-eb06-4042-9a9f-a6d276321fa5 · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

Chained Recursive Language Models for Multi-Iteration Reasoning DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 9

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source=arxiv_source observed=2026-08-06T04:47:17.512584Z digest=sha256:57c7d42bc84002cf1953d8eb07f7bb3f52bb0a2e090442b3c1af708098a9eb0d

Observation 18da242d-86aa-43ef-80fa-fbb19d1ce756 · outbound

This paper cites s1: Simple test-time scaling.

Chained Recursive Language Models for Multi-Iteration Reasoning s1: Simple test-time scaling

Reference 10

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source=arxiv_source observed=2026-08-06T04:47:17.688553Z digest=sha256:067524cfba50e6c74a41853957ee76b0b238c25930bff7508dd508aa000690f6

Observation 2861797a-ba91-4221-b39d-46f6ceeb66f0 · outbound

This paper cites Lost in the Middle: How Language Models Use Long Contexts.

Chained Recursive Language Models for Multi-Iteration Reasoning Lost in the Middle: How Language Models Use Long Contexts

Reference 11

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source=arxiv_source observed=2026-08-06T04:47:17.770183Z digest=sha256:e2d55acc618396f3359bb82ce20c1fece05120c577e6c4884b7d75d6ae46ae9f

Observation 0369d74d-778a-4bfa-b08f-aa5751ee14a8 · outbound

This paper cites Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention.

Chained Recursive Language Models for Multi-Iteration Reasoning Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention

Reference 12

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source=arxiv_source observed=2026-08-06T04:47:17.867723Z digest=sha256:07e9e7792c7cb7990f57a58adca88a5acb65d6172b306929495a611c382281ff

Observation 1c8edcd2-3a40-4889-88ca-9df843a60d1a · outbound

This paper cites Advances in neural information processing systems , volume=.

Chained Recursive Language Models for Multi-Iteration Reasoning Advances in neural information processing systems , volume=

Reference 13

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source=arxiv_source observed=2026-08-06T04:47:17.941553Z digest=sha256:16e02d062a3f4f7e41774b830b4dfde8f51f6febae5f50c9125deed1380480ac

Observation 6810cae0-d7d9-4b30-85f4-daa9daceb183 · outbound

This paper cites Infinite Retrieval: Attention Enhanced LLMs in Long-Context Processing.

Chained Recursive Language Models for Multi-Iteration Reasoning Infinite Retrieval: Attention Enhanced LLMs in Long-Context Processing

Reference 14

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source=arxiv_source observed=2026-08-06T04:47:18.046346Z digest=sha256:eb2ea992f288a464d33b98b9564c5ea38bd1796b23f8ff4966d376d0e2b9f17c

Observation 6d85d4cd-a76c-4f89-9272-a79fe5c647ec · outbound

This paper cites Advances in neural information processing systems , volume=.

Chained Recursive Language Models for Multi-Iteration Reasoning Advances in neural information processing systems , volume=

Reference 15

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source=arxiv_source observed=2026-08-06T04:47:18.132635Z digest=sha256:60114a6e7d6576a73e29fc1aa991b5c4d39364a5828c5ea563d1f2b1b6919bda

Observation 0d5fba01-f539-49c5-bb77-0ff2e354f696 · outbound

This paper cites Not All Thoughts are Generated Equal: Efficient LLM Reasoning via Multi-Turn Reinforcement Learning.

Chained Recursive Language Models for Multi-Iteration Reasoning Not All Thoughts are Generated Equal: Efficient LLM Reasoning via Multi-Turn Reinforcement Learning

Reference 16

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source=arxiv_source observed=2026-08-06T04:47:18.195206Z digest=sha256:d4eda4b63f72cb4e890f622475286a304e0e05367d5fa48e4cc456ffa31c7eca

Observation 44d892b0-0fe0-41f5-bf3f-e4fbb8120606 · outbound

This paper cites Mixture-of-Agents Enhances Large Language Model Capabilities.

Chained Recursive Language Models for Multi-Iteration Reasoning Mixture-of-Agents Enhances Large Language Model Capabilities

Reference 17

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source=arxiv_source observed=2026-08-06T04:47:18.267488Z digest=sha256:af89450b7e42e9419584258a18a124dc19a9fd74fb20102804295aab2dc11090

Observation eb714716-de51-402f-a343-7b548a63a031 · outbound

This paper cites Distributed Mixture-of-Agents for Edge Inference with Large Language Models.

Chained Recursive Language Models for Multi-Iteration Reasoning Distributed Mixture-of-Agents for Edge Inference with Large Language Models

Reference 18

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source=arxiv_source observed=2026-08-06T04:47:18.331277Z digest=sha256:b809726ef531e33b8c47ddea32c266861e122b863f0445389e59c83a375c03e4

Observation 3a2e497b-4c72-45d3-bb6f-94f52bc52189 · outbound

This paper cites Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters.

Chained Recursive Language Models for Multi-Iteration Reasoning Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 19

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source=arxiv_source observed=2026-08-06T04:47:18.417075Z digest=sha256:69ee1f03fd755699fdbddb7faeff3cdc398666efae96b35bef33b979be1f098b

Observation b937e26d-8a91-488b-83d5-95977cfe4475 · outbound

This paper cites OpenAI o1 System Card.

Chained Recursive Language Models for Multi-Iteration Reasoning OpenAI o1 System Card

Reference 20

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source=arxiv_source observed=2026-08-06T04:47:18.500327Z digest=sha256:fde8c94e14e2acd0b478936a3efdc9a0638bccf55e15c8dd77b2dac3a6054def

Observation 25dc0c63-e083-4c0f-b457-a0fa57b5361b · outbound

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

Chained Recursive Language Models for Multi-Iteration Reasoning Gemini: A Family of Highly Capable Multimodal Models

Reference 21

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source=arxiv_source observed=2026-08-06T04:47:18.632265Z digest=sha256:c96d497c2e7e53b484af4e518f64bdda1f572c30cca4cb0e5eebbe061238018c

Observation a0c45d9e-a497-4f10-807c-d91dc0646151 · outbound

This paper cites Measuring Mathematical Problem Solving With the MATH Dataset.

Chained Recursive Language Models for Multi-Iteration Reasoning Measuring Mathematical Problem Solving With the MATH Dataset

Reference 22

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Observation fe2f7c69-da22-4228-be16-e09dc7f046a2 · outbound

This paper cites The Twelfth International Conference on Learning Representations , year=.

Chained Recursive Language Models for Multi-Iteration Reasoning The Twelfth International Conference on Learning Representations , year=

Reference 23

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Observation cee319e6-05bf-4131-85cb-7b17922f6dcb · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Chained Recursive Language Models for Multi-Iteration Reasoning Training Verifiers to Solve Math Word Problems

Reference 24

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Observation c0610019-3919-4019-bf98-73cd47d9ae28 · outbound

This paper cites an unresolved cited work.

Chained Recursive Language Models for Multi-Iteration Reasoning Unresolved cited work

Reference 25

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Observation be17cbfe-0683-41fe-8a38-43b442099c7f · outbound

This paper cites an unresolved cited work.

Chained Recursive Language Models for Multi-Iteration Reasoning Unresolved cited work

Reference 26

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Observation 5b992752-2dba-45e9-8ecd-bafe48547af8 · outbound

This paper cites Hugging Face repository , volume=.

Chained Recursive Language Models for Multi-Iteration Reasoning Hugging Face repository , volume=

Reference 27

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source=arxiv_source observed=2026-08-06T04:47:19.123734Z digest=sha256:d705b2720e5c534e25bb89c6d4d5c5460aa8e2c1c2b7955ab4b1e27330e1460d

Observation c883fa17-c793-4a86-b1cd-c2f04999ba07 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Chained Recursive Language Models for Multi-Iteration Reasoning Evaluating Large Language Models Trained on Code

Reference 28

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Observation 58f86984-bd6e-4545-8dff-73fa65ccf8df · outbound

This paper cites , author=.

Chained Recursive Language Models for Multi-Iteration Reasoning , author=

Reference 29

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source=arxiv_source observed=2026-08-06T04:47:19.291631Z digest=sha256:db601d174bc5ee3d267e9fcd2b0993376d484e0c9c8d96f3499e198ecc996f9a

Observation cfb6be75-6a08-4b86-8c6d-d723df06f34e · outbound

This paper cites SMoA: Improving Multi-agent Large Language Models with Sparse Mixture-of-Agents.

Chained Recursive Language Models for Multi-Iteration Reasoning SMoA: Improving Multi-agent Large Language Models with Sparse Mixture-of-Agents

Reference 30

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source=arxiv_source observed=2026-08-06T04:47:19.356870Z digest=sha256:494a07c201505e69c60be96ddb7b32e0c06449d25a88ec7e8aa1e06b47c37ab8

Observation 41f6081d-1b0e-410a-a46f-139c9448904b · outbound

This paper cites Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial?.

Chained Recursive Language Models for Multi-Iteration Reasoning Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial?

Reference 31

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source=arxiv_source observed=2026-08-06T04:47:19.451802Z digest=sha256:712c81e592acb49102d6021f5e5a56ee00232cbd3ecab3afe6f62787d7e52a99

Observation 05837a97-f0f1-4876-babe-8883dd567618 · outbound

This paper cites GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning.

Chained Recursive Language Models for Multi-Iteration Reasoning GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning

Reference 32

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source=arxiv_source observed=2026-08-06T04:47:19.523559Z digest=sha256:567057e845462d65d290b6f1f687c600560f25a31c0804807076c7a825ba0ae9

Observation 5f3797c4-0073-47b3-a2f1-8f728bd24a82 · outbound

This paper cites DRP: Distilled Reasoning Pruning with Skill-aware Step Decomposition for Efficient Large Reasoning Models.

Chained Recursive Language Models for Multi-Iteration Reasoning DRP: Distilled Reasoning Pruning with Skill-aware Step Decomposition for Efficient Large Reasoning Models

Reference 33

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source=arxiv_source observed=2026-08-06T04:47:19.611123Z digest=sha256:7e556e879c3c220ede0a3a2713cb33d2b67e4e7878be0e6f2a3144a74f7f456b

Observation 3422ee3e-68ac-4d9c-b896-90f5233bf2fa · outbound

This paper cites Reasoning with Sampling: Your Base Model is Smarter Than You Think.

Chained Recursive Language Models for Multi-Iteration Reasoning Reasoning with Sampling: Your Base Model is Smarter Than You Think

Reference 34

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source=arxiv_source observed=2026-08-06T04:47:19.688690Z digest=sha256:7d2cb8013b1fa80b79dc084a3796427856e1a6129cde7871e2568a44c11dc2fa

Observation 2f8f701e-466f-4c1e-b808-9a79b09dd800 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Chained Recursive Language Models for Multi-Iteration Reasoning Distilling the Knowledge in a Neural Network

Reference 35

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source=arxiv_source observed=2026-08-06T04:47:19.757497Z digest=sha256:8650fbc0233aea7d19355664974a99eca377e78acb1506b9db219d9998bcd038

Observation 562486d0-4988-480a-bda5-1f9e297bc0a8 · outbound

This paper cites The twelfth international conference on learning representations , year=.

Chained Recursive Language Models for Multi-Iteration Reasoning The twelfth international conference on learning representations , year=

Reference 36

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source=arxiv_source observed=2026-08-06T04:47:19.824735Z digest=sha256:c1b4310f967df911711468f2e922cd3fc7860c03ecf906a27c810405daa0fa22

Observation 447b0204-a018-4e1f-9ac3-92b602222fc4 · outbound

This paper cites Thinking Machines Lab: Connectionism , year =.

Chained Recursive Language Models for Multi-Iteration Reasoning Thinking Machines Lab: Connectionism , year =

Reference 37

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source=arxiv_source observed=2026-08-06T04:47:19.922473Z digest=sha256:ff9903902f29cdeb041826ffd8278d1a1fdc0336ac22fdcf5bdb6619372cc177

Observation e2df9918-b3f8-4574-ae63-7b42fac7114c · outbound

This paper cites Proceedings of the 2024 conference on empirical methods in natural language processing , pages=.

Chained Recursive Language Models for Multi-Iteration Reasoning Proceedings of the 2024 conference on empirical methods in natural language processing , pages=

Reference 38

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source=arxiv_source observed=2026-08-06T04:47:19.997065Z digest=sha256:77862982ebbe13dc105e5677e56de48a42a64ecfa1d12ae7468962f220aef352

Observation c881b837-ffcd-4ecb-906f-22d2e63314d0 · outbound

This paper cites Incomplete Ideas (blog) , volume=.

Chained Recursive Language Models for Multi-Iteration Reasoning Incomplete Ideas (blog) , volume=

Reference 39

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source=arxiv_source observed=2026-08-06T04:47:20.070067Z digest=sha256:c32c13e4bb2a8769e563a7a9e93c5ee81431702e350c463a7973252c66291944

Observation c6c1f179-9dd1-4189-8fe9-998b6f0c0655 · outbound

This paper cites A Cookbook of Self-Supervised Learning.

Chained Recursive Language Models for Multi-Iteration Reasoning A Cookbook of Self-Supervised Learning

Reference 40

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source=arxiv_source observed=2026-08-06T04:47:20.130080Z digest=sha256:a7a903b1798a4305b1c243d63ea63cece49bf09428a633226ff9eca5e28ef709

Observation 3c97eea0-6d7a-48ef-842c-4b6401064331 · outbound

This paper cites arXiv preprint arXiv:2509.14745 , year=.

Chained Recursive Language Models for Multi-Iteration Reasoning arXiv preprint arXiv:2509.14745 , year=

Reference 41

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source=arxiv_source observed=2026-08-06T04:47:20.213574Z digest=sha256:6171a74b1b69419409d266465aa75ddc052b591da3874edd78042849fe5e6363

Observation af93f8de-f62a-4e6a-b048-9c42300c317c · outbound

This paper cites arXiv preprint arXiv:2510.12399 , year=.

Chained Recursive Language Models for Multi-Iteration Reasoning arXiv preprint arXiv:2510.12399 , year=

Reference 42

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source=arxiv_source observed=2026-08-06T04:47:20.295745Z digest=sha256:dfdc97a2277013aa68cf715c319b964ddeed766145a4979efb71fec5b2d7c5f5

Observation 43a14b1e-8e5c-4982-ac3c-7aa5ebb0f01d · outbound

This paper cites arXiv preprint arXiv:2507.15855 , volume=.

Chained Recursive Language Models for Multi-Iteration Reasoning arXiv preprint arXiv:2507.15855 , volume=

Reference 43

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source=arxiv_source observed=2026-08-06T04:47:20.393621Z digest=sha256:f3101b4749f4dc2815b244633407b21744dfcd15ed085dbefa9c09a174aaf4a6

Observation 9bfd5700-7d75-49d1-83e1-19e3eb557cc4 · outbound

This paper cites ARC Prize 2024: Technical Report.

Chained Recursive Language Models for Multi-Iteration Reasoning ARC Prize 2024: Technical Report

Reference 44

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source=arxiv_source observed=2026-08-06T04:47:20.468406Z digest=sha256:6ce2d8e1e54dce921676e83d7bd67f71e1431de105774b9dc987e5d2acd8139c

Observation e5c121c4-403c-477a-81cc-cedaf3a826d9 · outbound

This paper cites arXiv preprint arXiv:2511.16072 , year=.

Chained Recursive Language Models for Multi-Iteration Reasoning arXiv preprint arXiv:2511.16072 , year=

Reference 45

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source=arxiv_source observed=2026-08-06T04:47:20.529038Z digest=sha256:9e11299ccb4ab4d258869db67f80dadb20629256e1e5025c29796e3cd3891079

Observation c24fcda2-3087-4a75-8fcd-64aa785260a5 · outbound

This paper cites BioRxiv , pages=.

Chained Recursive Language Models for Multi-Iteration Reasoning BioRxiv , pages=

Reference 46

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verified fuzzy
raw_fallback, observed 2026-08-06T04:47:25.140401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-06T04:47:20.572563Z digest=sha256:e1467ed0e0ccfc30c7f6a21172d466bc4010a6e87c7bfc8ed285d1c2b6b0c03b

Observation e89af2a8-5056-4ae4-8256-5acda431faae · outbound

This paper cites Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?.

Chained Recursive Language Models for Multi-Iteration Reasoning Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?

Reference 47

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source=arxiv_source observed=2026-08-06T04:47:20.628471Z digest=sha256:948cb62535f438404e2bd34ccdd765e25df02c3fce56fe4d6a427094d16ba077

Observation d3b6823f-32bc-40d8-80c2-4e69cafdd890 · outbound

This paper cites 2nd AI for Math Workshop@ ICML 2025 , year=.

Chained Recursive Language Models for Multi-Iteration Reasoning 2nd AI for Math Workshop@ ICML 2025 , year=

Reference 48

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verified fuzzy
raw_fallback, observed 2026-08-06T04:47:25.128241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-06T04:47:20.696413Z digest=sha256:b8f173a2cde0c3de78e499c7489d9d734d81db6cf5b80af3ca9e574505f3d6e0

Observation ffb69299-3551-4af8-a453-35fbadc83bea · outbound

This paper cites Ieee Access , volume=.

Chained Recursive Language Models for Multi-Iteration Reasoning Ieee Access , volume=

Reference 49

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verified fuzzy
raw_fallback, observed 2026-08-06T04:47:25.116679Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-06T04:47:20.759091Z digest=sha256:12c7085bdb95cfee24b48d3beeb6d9d16625ffb38750e40ef5ffc44be365a1fd

Observation 9377a9aa-d07b-471f-9e3e-952bb34c2777 · outbound

This paper cites Nature , volume=.

Chained Recursive Language Models for Multi-Iteration Reasoning Nature , volume=

Reference 50

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-06T04:47:20.871888Z digest=sha256:d69d4033af22c133f1937c890c39c71aa783097f8f8473de7e16614ad16f058a

Observation 14511f44-be93-4516-8f8f-3c63064c8600 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

Chained Recursive Language Models for Multi-Iteration Reasoning Advances in Neural Information Processing Systems , volume=

Reference 51

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source=arxiv_source observed=2026-08-06T04:47:20.933434Z digest=sha256:98be075717c88586a6ba7dcb23097c23f3a73ae3afd0b3df7460bb2f823d3083

Observation bcdd1b14-b89a-4391-8bf8-d0ac79661f03 · outbound

This paper cites Think, Prune, Train, Improve: Scaling Reasoning without Scaling Models.

Chained Recursive Language Models for Multi-Iteration Reasoning Think, Prune, Train, Improve: Scaling Reasoning without Scaling Models

Reference 52

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source=arxiv_source observed=2026-08-06T04:47:21.021997Z digest=sha256:38b76e2247bdfd46e7c09fc1ffd56b5eb3f60a5a649af10732bd16a0ff44673b

Observation b5f95e2e-9572-4071-91f2-a7db4c64e899 · outbound

This paper cites BOLT: Bootstrap Long Chain-of-Thought in Language Models without Distillation.

Chained Recursive Language Models for Multi-Iteration Reasoning BOLT: Bootstrap Long Chain-of-Thought in Language Models without Distillation

Reference 53

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source=arxiv_source observed=2026-08-06T04:47:21.087601Z digest=sha256:10eb15c08eb4e403732461705c96dbd9c5016ce40de4a5cbcbe2922f4dd1ebc9

Observation a2382025-d0e8-4690-b833-67ae65a102e2 · outbound

This paper cites Understanding R1-Zero-Like Training: A Critical Perspective.

Chained Recursive Language Models for Multi-Iteration Reasoning Understanding R1-Zero-Like Training: A Critical Perspective

Reference 54

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source=arxiv_source observed=2026-08-06T04:47:21.166795Z digest=sha256:7e37817e4c51c20a68f071d3ab4210bd01ac35da10fee8dd6cd828828fa1185c

Observation f49cfc3c-6ee3-49c2-9065-81425ab28c50 · outbound

This paper cites DAPO: An Open-Source LLM Reinforcement Learning System at Scale.

Chained Recursive Language Models for Multi-Iteration Reasoning DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Reference 55

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no resolver link, observed 2026-08-06T04:47:21.300563Z

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source=arxiv_source observed=2026-08-06T04:47:21.300563Z digest=sha256:b3cd15d2da2e863cd257c29c0558b42b8566b7dbd9b485c28bb738cd5b7728de

Observation a978ae39-7bb4-4d6a-9c28-a05983773ecb · outbound

This paper cites Group Sequence Policy Optimization.

Chained Recursive Language Models for Multi-Iteration Reasoning Group Sequence Policy Optimization

Reference 56

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no resolver link, observed 2026-08-06T04:47:21.360346Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-06T04:47:21.360346Z digest=sha256:cf7befaa0c7326188e22d8bfc2394cc776254690ba8c8019676119a9ff9e9b4d

Observation e46d6d04-e806-4a42-a0fc-2a21cdc96df1 · outbound

This paper cites arXiv preprint arXiv:2511.07919 , year=.

Chained Recursive Language Models for Multi-Iteration Reasoning arXiv preprint arXiv:2511.07919 , year=

Reference 57

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source=arxiv_source observed=2026-08-06T04:47:21.430444Z digest=sha256:fcacb53043fc3a4b8286ec00de3e5d2cfe77deafa804dc227183d0e22cc58e2b

Observation b3c28e03-cda6-45f5-a5ba-5e77a79c70df · outbound

This paper cites ReAct: Synergizing Reasoning and Acting in Language Models.

Chained Recursive Language Models for Multi-Iteration Reasoning ReAct: Synergizing Reasoning and Acting in Language Models

Reference 58

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no resolver link, observed 2026-08-06T04:47:21.531113Z

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source=arxiv_source observed=2026-08-06T04:47:21.531113Z digest=sha256:ceb1ca5a6d4ec6ef5247f2ac6387486272bdccfa902c6ac4088135541e351078

Observation 4667ec5b-ddb0-495a-a7fd-76d35023bbd2 · outbound

This paper cites , author=.

Chained Recursive Language Models for Multi-Iteration Reasoning , author=

Reference 59

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no resolver link, observed 2026-08-06T04:47:21.581118Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-06T04:47:21.581118Z digest=sha256:005c76dd12c540a95928c498f25f8569df3e4a2df67a2fc2a17773e2280e9a11

Observation 1084bc54-4b21-4e03-92d0-0d1bf5765b62 · outbound

This paper cites Advances in neural information processing systems , volume=.

Chained Recursive Language Models for Multi-Iteration Reasoning Advances in neural information processing systems , volume=

Reference 60

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no resolver link, observed 2026-08-06T04:47:21.646506Z

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source=arxiv_source observed=2026-08-06T04:47:21.646506Z digest=sha256:3c7c330c4ea35da3c1c0594650fb0f2fc670fd0e65ec6abb186c4a9500a4c755

Observation 72dfeccd-a820-4daf-aec8-875f72e602c1 · outbound

This paper cites DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines.

Chained Recursive Language Models for Multi-Iteration Reasoning DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines

Reference 61

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source=arxiv_source observed=2026-08-06T04:47:21.779953Z digest=sha256:2d84d7a92e9498d160955c497c115495fb7a9ad87e7a00c69ae7c09f58ba317f

Observation e61245c9-5e4d-4c81-85c8-640af7acbf73 · outbound

This paper cites Recursive Language Models.

Chained Recursive Language Models for Multi-Iteration Reasoning Recursive Language Models

Reference 62

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source=arxiv_source observed=2026-08-06T04:47:21.854499Z digest=sha256:08994f12e32bac04c81c6fecbd637cc5225b246b7d0955e96ed52d3b16e1041f

Observation 991464f3-e9ef-44c3-ba26-0b7d39387354 · outbound

This paper cites RULER: What's the Real Context Size of Your Long-Context Language Models?.

Chained Recursive Language Models for Multi-Iteration Reasoning RULER: What's the Real Context Size of Your Long-Context Language Models?

Reference 63

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source=arxiv_source observed=2026-08-06T04:47:21.923173Z digest=sha256:8e5e328421d341d8fe4277b4e336b8079772f2829bd902afe7dc7dfb4c669d8b

Observation caff3b17-5120-4619-90be-dc8cc12e006d · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

Chained Recursive Language Models for Multi-Iteration Reasoning Advances in Neural Information Processing Systems , volume=

Reference 64

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T04:47:21.987337Z digest=sha256:ec2efbe8ba05b4ad080f0ec3f389d1a211d0b47e4a1381e33d913790e80f8688

Observation eb86cb29-7e40-4ff4-93ed-857f4dfaa1cf · outbound

This paper cites Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=.

Chained Recursive Language Models for Multi-Iteration Reasoning Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

Reference 65

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source=arxiv_source observed=2026-08-06T04:47:22.051181Z digest=sha256:366c9bab309101540886b3a76591114b1ca8b4bccf9320ba461c6901c93238ff

Observation 4f7a7469-2ce4-483f-a550-d782e7c08f21 · outbound

This paper cites arXiv preprint arXiv:2511.02817 , year=.

Chained Recursive Language Models for Multi-Iteration Reasoning arXiv preprint arXiv:2511.02817 , year=

Reference 66

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-06T04:47:22.093928Z digest=sha256:b2cba904acbbf4118c8f71f80547e9993cf80c1fcbdee5f6185f93d5318d8272

Observation b4e1a789-1ebe-4abe-9804-0ec72c5e189c · outbound

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

Chained Recursive Language Models for Multi-Iteration Reasoning Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 67

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no resolver link, observed 2026-08-06T04:47:22.157851Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T04:47:22.157851Z digest=sha256:672a7acdbfef47df5a283763071bd45f31f4e85518f64362e66224e315fa4986

Observation 99e3ec50-91e5-46ff-841d-7a3016c32423 · outbound

This paper cites Journal of Artificial Intelligence Research , volume=.

Chained Recursive Language Models for Multi-Iteration Reasoning Journal of Artificial Intelligence Research , volume=

Reference 68

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verified fuzzy
raw_fallback, observed 2026-08-06T04:47:25.066769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-06T04:47:22.216266Z digest=sha256:b83c0b176fd348c090c2eda21e9224aff0be90dee8385a2c8b80d1611050b4c2

Observation 6f4aa34e-4943-4fd3-9cf1-3471fe8f1e56 · outbound

This paper cites URL https://research.

Chained Recursive Language Models for Multi-Iteration Reasoning URL https://research

Reference 69

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verified fuzzy
raw_fallback, observed 2026-08-06T04:47:25.046923Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-06T04:47:22.257067Z digest=sha256:7f505ae66c1e9528106a4f994a102375fdc521035359bbe1dbf9df0e46e99d10

Observation 660acd40-3883-4f66-82d5-afc82927c2c3 · outbound

This paper cites Advances in neural information processing systems , volume=.

Chained Recursive Language Models for Multi-Iteration Reasoning Advances in neural information processing systems , volume=

Reference 70

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no resolver link, observed 2026-08-06T04:47:22.314068Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T04:47:22.314068Z digest=sha256:2a785567c077430c0959acdc8fde5c853de4f34bff92efa329fa67a13798c62a

Observation d352772c-2274-4886-beb5-86e42a898c80 · outbound

This paper cites an unresolved cited work.

Chained Recursive Language Models for Multi-Iteration Reasoning Unresolved cited work

Reference 71

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unresolved
raw_fallback, observed 2026-08-06T04:47:24.782527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-06T04:47:22.379825Z digest=sha256:265f6484dfb825f5a9b31652415221b69d55585c55f3ca634a3af0e521cb731e

Pith citing papers

No inbound Pith citation observations are available.