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Paper Citation Record · LEDGER

LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization

As of 8 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2505.17447.

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

pith.paper-citation-record.v1
2505.17447 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:51:07.554593Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

41 of 41 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved41
  • parse uncertain0
  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ddc99e0e-bc13-4ba4-900f-ba774c20b09d · outbound

This paper cites online" 'onlinestring :=.

LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization online" 'onlinestring :=

Reference 1

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Unavailable: canonical work link unavailable.

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Observation 2a5020b2-2df5-4195-85ed-0140fe5887a4 · outbound

This paper cites write newline.

LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization write newline

Reference 2

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source=arxiv_source observed=2026-08-07T14:51:03.621425Z digest=sha256:5166c148b7bfabdee5ede6458827940305ae61382ec3d716563c47140fc17b46

Observation f63710ba-99d6-4b5c-a8e3-0d4acbc3a502 · outbound

This paper cites an unresolved cited work.

LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization Unresolved cited work

Reference 3

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source=arxiv_source observed=2026-08-07T14:51:03.675051Z digest=sha256:ebf7c6a9f547b05111f68cace06fa3b6418efcc31aaacb15c6e4cc8ce5f54b0b

Observation b60f4b91-9aa0-4865-8d19-39494c28af2d · outbound

This paper cites an unresolved cited work.

LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization Unresolved cited work

Reference 4

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source=arxiv_source observed=2026-08-07T14:51:03.730545Z digest=sha256:9e23814ac9a974944c8eaf59b2f40154a3d2482b28b72d0b8109b0dd441640fa

Observation 5c4289df-aa49-4bfd-b4c8-0b1c6b55eba3 · outbound

This paper cites RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generation.

LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generation

Reference 5

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source=arxiv_source observed=2026-08-07T14:51:03.812840Z digest=sha256:c4ce2d8e958d3526265642ad92a269169a9c4656b6e10a2f31e46bc5c7d958a1

Observation 05513c23-67ce-46a0-b5ea-3aac191e6fce · outbound

This paper cites an unresolved cited work.

LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization Unresolved cited work

Reference 6

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no resolver link, observed 2026-08-07T14:51:03.904352Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:51:03.904352Z digest=sha256:11801fe4b91e5689e04c060ee041660b201de21b1045b99a84421a7c9b6ecdfa

Observation 8616e3d9-ac94-43d1-8a5f-803ff7274e3f · outbound

This paper cites ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning.

LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning

Reference 7

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source=arxiv_source observed=2026-08-07T14:51:03.977658Z digest=sha256:7c18f5ef47e9d91bdbf475493243e27a18a962d70e39dbad9cc099aae8220482

Observation ac84ba15-6942-4c3b-9d3f-13e9829e234a · outbound

This paper cites an unresolved cited work.

LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization Unresolved cited work

Reference 8

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source=arxiv_source observed=2026-08-07T14:51:04.037602Z digest=sha256:85244ca9486d45ccd1dfe0a9f5b45e2efeb9a7ec0932b91fa987e77c773d2a12

Observation 540c1f42-7d17-44a9-86f0-1cca27c350c8 · outbound

This paper cites an unresolved cited work.

LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization Unresolved cited work

Reference 9

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raw_fallback, observed 2026-08-07T14:51:08.656097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation bb8354ef-1b54-4839-a3c2-5a641560918a · outbound

This paper cites Retrieval-Augmented Generation for Large Language Models: A Survey.

LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization Retrieval-Augmented Generation for Large Language Models: A Survey

Reference 10

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:51:04.229471Z digest=sha256:48925c0c2d5790a285ad9878a7c9c320aac8d895bfad34e6ae5e59552aaf849e

Observation 900a0dd0-d517-4780-9f8a-374166331685 · outbound

This paper cites The Llama 3 Herd of Models.

LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization The Llama 3 Herd of Models

Reference 11

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Observation 92ae1244-063b-4228-ad8c-916069617028 · outbound

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

LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 12

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Observation c34138c0-57cd-4293-9dfa-b2fcf80b6046 · outbound

This paper cites an unresolved cited work.

LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization Unresolved cited work

Reference 13

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:51:04.526473Z digest=sha256:906970508b1543af77a794bc6a3c5a3d64f1966da07600fa3f6d0c39a0212630

Observation 1855f7dc-7d70-4c0a-b183-69295266ad13 · outbound

This paper cites an unresolved cited work.

LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization Unresolved cited work

Reference 14

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Observation c8fbd589-da3b-4385-97f8-7161bcd416ec · outbound

This paper cites an unresolved cited work.

LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization Unresolved cited work

Reference 15

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source=arxiv_source observed=2026-08-07T14:51:04.805360Z digest=sha256:2e5f7075b8fb569e471ae8adee4fca0f8c4ce5d677035ae4316aac09fa38be64

Observation 40be5fa4-2d6b-459f-993b-2d9175aed31e · outbound

This paper cites OpenAI o1 System Card.

LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization OpenAI o1 System Card

Reference 16

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:51:04.895464Z digest=sha256:5958ff25fcbcc23106307cd9d0137e6298086c4fdd9c62305e84fba0e94d91e1

Observation b5bcb7d1-23e8-4f43-9f89-467855450021 · outbound

This paper cites A Survey on Large Language Models for Code Generation.

LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization A Survey on Large Language Models for Code Generation

Reference 17

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source=arxiv_source observed=2026-08-07T14:51:05.017006Z digest=sha256:d7a86afd33b128b0a2bc700b44ea58ea0349a5b52665a48043e0d32349e5a90c

Observation 35ed828b-0ddc-4fb6-b7ba-584e48762e1e · outbound

This paper cites Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning.

LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning

Reference 18

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source=arxiv_source observed=2026-08-07T14:51:05.120661Z digest=sha256:6e1f73e9e716d6683665da7faa8443a0987351941214c7b2ae44e4f005db2d4d

Observation 49c9fbde-d648-4579-a270-5f3f5de0c23b · outbound

This paper cites an unresolved cited work.

LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization Unresolved cited work

Reference 19

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source=arxiv_source observed=2026-08-07T14:51:05.197470Z digest=sha256:ac5443ea58e856de7bda38bd9d9dc50f4895ea563b3c89454ed90de1fad32256

Observation 3d4eebcd-b1c4-4713-8fee-3751abedfb3f · outbound

This paper cites Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov.

LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov

Reference 20

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Observation 99749a29-fa3b-4cd9-8fdb-e6668e60a372 · outbound

This paper cites u ttler, Mike Lewis, Wen-tau Yih, Tim Rockt \.

LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization u ttler, Mike Lewis, Wen-tau Yih, Tim Rockt \

Reference 21

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Observation 319efd8c-ff91-4ca4-bc9d-bc117fb75a7a · outbound

This paper cites RA-ISF: Learning to Answer and Understand from Retrieval Augmentation via Iterative Self-Feedback.

LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization RA-ISF: Learning to Answer and Understand from Retrieval Augmentation via Iterative Self-Feedback

Reference 22

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Observation a82f470d-011e-4a3b-89e3-a38dc9318fd6 · outbound

This paper cites WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct.

LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct

Reference 23

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Observation 3c54ff20-26cc-4bae-93be-5128e3dd5c00 · outbound

This paper cites S$^2$R: Teaching LLMs to Self-verify and Self-correct via Reinforcement Learning.

LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization S$^2$R: Teaching LLMs to Self-verify and Self-correct via Reinforcement Learning

Reference 24

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Observation 9075dbc7-dca6-4ce0-a0d4-8b7fa5dc78d8 · outbound

This paper cites an unresolved cited work.

LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization Unresolved cited work

Reference 25

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Observation d4b9e156-55b7-4c03-ade1-02da44096a7c · outbound

This paper cites an unresolved cited work.

LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization Unresolved cited work

Reference 26

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Observation a29c04bd-6c1d-42f9-a21a-d4d0980ac8e5 · outbound

This paper cites an unresolved cited work.

LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization Unresolved cited work

Reference 27

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Unavailable: canonical work link unavailable.

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Observation 5723cd8b-a28d-4dac-905e-c07f1810dd56 · outbound

This paper cites Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy.

LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy

Reference 28

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Observation 8b1495bc-c7dd-4652-a8ac-7ac9a8c70df7 · outbound

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

LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 29

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Unavailable: canonical work link unavailable.

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Observation 52e00139-9598-411e-8331-62e17e974472 · outbound

This paper cites Retrieval Augmentation Reduces Hallucination in Conversation.

LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization Retrieval Augmentation Reduces Hallucination in Conversation

Reference 30

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Unavailable: canonical work link unavailable.

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Observation 1665647a-2844-413b-a7f2-10e694823c9d · outbound

This paper cites an unresolved cited work.

LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization Unresolved cited work

Reference 31

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Unavailable: canonical work link unavailable.

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Observation cc1e4a98-1fd4-4fe4-a39c-40d0387cb343 · outbound

This paper cites an unresolved cited work.

LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization Unresolved cited work

Reference 32

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no resolver link, observed 2026-08-07T14:51:06.582517Z

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Unavailable: canonical work link unavailable.

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Observation db6e6dde-4a6b-476f-82e5-ce37d48792bc · outbound

This paper cites Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions.

LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions

Reference 33

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:51:06.698778Z digest=sha256:395cd9c0ede9b7240257147c035891e330cd20032853794a18467e098083dad6

Observation f94e2822-8781-4923-b20e-d97ab1329a90 · outbound

This paper cites an unresolved cited work.

LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization Unresolved cited work

Reference 34

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Unavailable: canonical work link unavailable.

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Observation 0bfcb240-7aae-4869-869a-c7dcc966aa3c · outbound

This paper cites Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou.

LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou

Reference 35

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source=arxiv_source observed=2026-08-07T14:51:06.930624Z digest=sha256:d037f432bf720dea81f30910f7f62b677f2403515735169e1213658b934f3814

Observation 2bb37733-bf38-4ee2-8a90-25746e5b2b85 · outbound

This paper cites Qwen2.5 Technical Report.

LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization Qwen2.5 Technical Report

Reference 36

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source=arxiv_source observed=2026-08-07T14:51:07.021431Z digest=sha256:108277f15ebead4f6ffe844d8337a3f4cd1dccc111780bda80f452dbe6ea0222

Observation cba88396-bf16-413e-9c65-1101c3478569 · outbound

This paper cites an unresolved cited work.

LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization Unresolved cited work

Reference 37

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source=arxiv_source observed=2026-08-07T14:51:07.125296Z digest=sha256:3ee893eb3123daad0d8acaaeaa539b1a77271a467914ca7774166c210d44735d

Observation 243c624a-b41a-4599-9eb5-b087c50bb841 · outbound

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

LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization ReAct: Synergizing Reasoning and Acting in Language Models

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-07T14:51:07.233790Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:51:07.233790Z digest=sha256:6e8d014124911418d56d056211aad2c2bf9ee0625d17319506c226eef36c4f4f

Observation ca05bcd1-45da-4748-8601-9d36289bd9e2 · outbound

This paper cites an unresolved cited work.

LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization Unresolved cited work

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-07T14:51:07.386132Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:51:07.386132Z digest=sha256:3c05b0551b0ecc1e3d4e20cf0292565303e3faef7458716829beccc66f6c6e23

Observation 2221b3fc-dfa4-4292-87c1-568ea50a5ab6 · outbound

This paper cites A Survey of Large Language Model Agents for Question Answering.

LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization A Survey of Large Language Model Agents for Question Answering

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-07T14:51:07.477804Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:51:07.477804Z digest=sha256:e42c8ef2b1d3c33c80688ff3ac62e28ad97b32c9c35454649a2b6131888454b6

Observation 85a0d163-ea54-49fd-86bc-68e26e5a3256 · outbound

This paper cites A Survey of Large Language Models.

LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization A Survey of Large Language Models

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-07T14:51:07.554593Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:51:07.554593Z digest=sha256:5af8aefb6a2f62a33537d8c31759cf6b9bc496bab7abaec0a66b4b6890fe2387

Pith citing papers

No inbound Pith citation observations are available.