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Paper Citation Record · LEDGER

REARANK: Reasoning Re-ranking Agent via Reinforcement Learning

As of 15 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 7 inbound Pith citation observations for arXiv:2505.20046.

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

pith.paper-citation-record.v1
2505.20046 v1

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:07:15.898573Z

measured 51 of 51 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 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:22:24.214754Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T23:56:55.107569Z

Reference resolution

44 of 44 outbound references displayed

  • verified exact1
  • verified fuzzy0
  • unresolved43
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e1c5c600-b5ad-4632-bbbb-6769888ab87c · outbound

This paper cites GPT-4 Technical Report.

REARANK: Reasoning Re-ranking Agent via Reinforcement Learning GPT-4 Technical Report

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:07:11.863636Z digest=sha256:b85a3710654d937030f79d77b12211979c80e45f2c764b3ea7ae7afcbe0f5267

Observation 75c105d8-9080-4f43-b9eb-44f8cfd06f79 · outbound

This paper cites LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders.

REARANK: Reasoning Re-ranking Agent via Reinforcement Learning LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 2

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source=arxiv_source observed=2026-08-07T14:07:11.928833Z digest=sha256:4ca5c824b13189ea3a97a59cfd232aabbbe68dea9d1e3b1b54b096a6a39f0b56

Observation 77130b62-65b6-4a3f-9646-9942139c28d1 · outbound

This paper cites an unresolved cited work.

REARANK: Reasoning Re-ranking Agent via Reinforcement Learning Unresolved cited work

Reference 3

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source=arxiv_source observed=2026-08-07T14:07:12.013949Z digest=sha256:e6a58e06855456c201735a97233d60f9bff7d3b4c20e4ca1c4ee179533423cee

Observation 0eca5c43-d392-4839-83b5-154c336b9c1e · outbound

This paper cites Overview of the TREC 2019 deep learning track.

REARANK: Reasoning Re-ranking Agent via Reinforcement Learning Overview of the TREC 2019 deep learning track

Reference 5

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source=arxiv_source observed=2026-08-07T14:07:12.186322Z digest=sha256:414da27ba9dbf5f9e9396dccd94443dce169e69c754d237a7c916897d4205e0c

Observation 4f780dec-d5f7-4ec3-88cf-b6f21387737d · outbound

This paper cites an unresolved cited work.

REARANK: Reasoning Re-ranking Agent via Reinforcement Learning Unresolved cited work

Reference 6

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source=arxiv_source observed=2026-08-07T14:07:12.278805Z digest=sha256:5e0b838a3f043399639989485d957291f244e5e9268319740989e76270c0a41a

Observation 1819c3a8-98cd-4df3-884f-2a4c8a9a5426 · outbound

This paper cites an unresolved cited work.

REARANK: Reasoning Re-ranking Agent via Reinforcement Learning Unresolved cited work

Reference 7

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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.

source=arxiv_source observed=2026-08-07T14:07:12.333196Z digest=sha256:b175d28045e654bb9bb71a55a0bb9939d01b6659479330b419962931a35fd71a

Observation 8f5bad58-10d1-4446-b8cc-811dc36790f4 · outbound

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

REARANK: Reasoning Re-ranking Agent via Reinforcement Learning DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 8

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source=arxiv_source observed=2026-08-07T14:07:12.431132Z digest=sha256:27a8f251a36630212ea0254a99b84b24f50b20ddc533a6d5d5ff4997192193b1

Observation 8f76fa81-6b56-4342-95fa-5de23e6af88d · outbound

This paper cites an unresolved cited work.

REARANK: Reasoning Re-ranking Agent via Reinforcement Learning Unresolved cited work

Reference 9

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source=arxiv_source observed=2026-08-07T14:07:12.554615Z digest=sha256:35330dd3d726194f3914679d1399107862d5bfd3a50b29e69b81bfe47fb2b3f2

Observation 5ebc3e12-4cd2-4b97-a5ce-3c9056502b12 · outbound

This paper cites NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models.

REARANK: Reasoning Re-ranking Agent via Reinforcement Learning NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models

Reference 10

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source=arxiv_source observed=2026-08-07T14:07:12.642594Z digest=sha256:df5cc621f688073785f8cf58f3aebeb7db5105cfac798cf7ec9780bd959485cf

Observation 1cb00cf5-5414-4f55-919f-483caf7995b2 · outbound

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

REARANK: Reasoning Re-ranking Agent via Reinforcement Learning u ttler, Mike Lewis, Wen-tau Yih, Tim Rockt \

Reference 11

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source=arxiv_source observed=2026-08-07T14:07:12.728412Z digest=sha256:873e38c2402d9b2c8dabfe2ca7bf1da7050975fad6d70c6ded4bbfb7c913480f

Observation 2fa802e0-ff55-4d3e-a7f4-23a95b019711 · outbound

This paper cites Holistic Evaluation of Language Models.

REARANK: Reasoning Re-ranking Agent via Reinforcement Learning Holistic Evaluation of Language Models

Reference 12

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source=arxiv_source observed=2026-08-07T14:07:12.880824Z digest=sha256:7d73fb5fc4f909807c37dd6b8495138b41747b2cb5a1c1631bcfc1830bb3098f

Observation d4379efc-d361-4030-9224-85a752862fcf · outbound

This paper cites an unresolved cited work.

REARANK: Reasoning Re-ranking Agent via Reinforcement Learning Unresolved cited work

Reference 13

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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.

source=arxiv_source observed=2026-08-07T14:07:12.949364Z digest=sha256:4e3772676bb4c5a9c440bca92bbaf8e19fefeb914601ff6f1d0e08c6a756c151

Observation 019d401d-c136-41a9-9146-769b6c9d9e0d · outbound

This paper cites an unresolved cited work.

REARANK: Reasoning Re-ranking Agent via Reinforcement Learning Unresolved cited work

Reference 14

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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.

source=arxiv_source observed=2026-08-07T14:07:13.030726Z digest=sha256:bab7298c375362542f35c9a3ef957ff5969a3f8c4ea2b46c19f17f0100a456b3

Observation 06b560b1-34e2-47b0-aea1-c199707b655e · outbound

This paper cites Zero-Shot Listwise Document Reranking with a Large Language Model.

REARANK: Reasoning Re-ranking Agent via Reinforcement Learning Zero-Shot Listwise Document Reranking with a Large Language Model

Reference 15

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:07:13.130568Z digest=sha256:b7417ae6486815f4afa76c4fec3deb1087f8f7e9a962e6a44abb577085d2faf7

Observation 14b0a914-6e7a-4a45-90cf-3d6ed6589b3e · outbound

This paper cites an unresolved cited work.

REARANK: Reasoning Re-ranking Agent via Reinforcement Learning Unresolved cited work

Reference 16

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:07:13.230177Z digest=sha256:3ed1e5b7f08a0d60165b18e10fd419e898241b2d03bcd1b5c1eb57bfb2445607

Observation eb040adb-49b5-44bd-9cbd-665633845071 · outbound

This paper cites RankVicuna: Zero-Shot Listwise Document Reranking with Open-Source Large Language Models.

REARANK: Reasoning Re-ranking Agent via Reinforcement Learning RankVicuna: Zero-Shot Listwise Document Reranking with Open-Source Large Language Models

Reference 17

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source=arxiv_source observed=2026-08-07T14:07:13.357576Z digest=sha256:ebdc1ee7bd53074452267ac7745469ba367f6bf7ecca044fb669edea8561ed9b

Observation 32b928cf-b0f8-40d2-96ea-82ae0738ce61 · outbound

This paper cites RankZephyr: Effective and Robust Zero-Shot Listwise Reranking is a Breeze!.

REARANK: Reasoning Re-ranking Agent via Reinforcement Learning RankZephyr: Effective and Robust Zero-Shot Listwise Reranking is a Breeze!

Reference 18

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source=arxiv_source observed=2026-08-07T14:07:13.482724Z digest=sha256:2b585a2e5a4d45c7a1ec177e16d00c6273ee724c4cae26055408a0dca3856c6a

Observation 6de707b5-b85b-4d11-96be-4848270a9941 · outbound

This paper cites Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting.

REARANK: Reasoning Re-ranking Agent via Reinforcement Learning Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting

Reference 19

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source=arxiv_source observed=2026-08-07T14:07:13.543503Z digest=sha256:ee299ca8049a065de5a49edae34340e6b2e23ad58d0c6067d7e7b035b4c3f169

Observation cbc0ee9f-6c7d-45dd-ab55-c5bbc81a80f5 · outbound

This paper cites Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks.

REARANK: Reasoning Re-ranking Agent via Reinforcement Learning Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Reference 20

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source=arxiv_source observed=2026-08-07T14:07:13.632045Z digest=sha256:bf0a588e387dda0ffcd40601555cacce56df9c97eecf3ba54af6533d864b1288

Observation 6adf4a41-c3ce-4170-938a-7925133c8d96 · outbound

This paper cites Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation.

REARANK: Reasoning Re-ranking Agent via Reinforcement Learning Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation

Reference 21

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source=arxiv_source observed=2026-08-07T14:07:13.710293Z digest=sha256:d4181a5bc353f9ee3986d0326e2e484e8c2f5331d506d4826aaae07da38b1a44

Observation 6b27a86c-5878-447a-a20b-760e2a108768 · outbound

This paper cites an unresolved cited work.

REARANK: Reasoning Re-ranking Agent via Reinforcement Learning Unresolved cited work

Reference 22

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source=arxiv_source observed=2026-08-07T14:07:13.820059Z digest=sha256:579ee484e82b400286db58dfbafb9328f2cf2cfbdd762fe88c4952b9208216f5

Observation df95bbe0-19a8-4339-a387-142647be3a9f · outbound

This paper cites Improving Passage Retrieval with Zero-Shot Question Generation.

REARANK: Reasoning Re-ranking Agent via Reinforcement Learning Improving Passage Retrieval with Zero-Shot Question Generation

Reference 23

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source=arxiv_source observed=2026-08-07T14:07:13.912679Z digest=sha256:61e2180355c6fd0e9414b68de46a58d45dea41d7e32e29f48448b398ffc8ec46

Observation 0296164c-0b35-4e30-9a80-89f529fd4188 · outbound

This paper cites High-Dimensional Continuous Control Using Generalized Advantage Estimation.

REARANK: Reasoning Re-ranking Agent via Reinforcement Learning High-Dimensional Continuous Control Using Generalized Advantage Estimation

Reference 24

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source=arxiv_source observed=2026-08-07T14:07:14.028720Z digest=sha256:131e49fa4932b8d337006cc3ea912eca81ce48720f865b00f28c91f980fc40d4

Observation 103375a3-cf07-496d-bc5b-940c4656a681 · outbound

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

REARANK: Reasoning Re-ranking Agent via Reinforcement Learning DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 25

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source=arxiv_source observed=2026-08-07T14:07:14.134085Z digest=sha256:2c3f0a54e841c16bfdb85392d851287efe57266cd89b77c597ff25cda9e3b744

Observation e59a1758-8aed-4e56-92bc-2bbd02c4d1f6 · outbound

This paper cites HybridFlow: A Flexible and Efficient RLHF Framework.

REARANK: Reasoning Re-ranking Agent via Reinforcement Learning HybridFlow: A Flexible and Efficient RLHF Framework

Reference 26

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source=arxiv_source observed=2026-08-07T14:07:14.250669Z digest=sha256:4a2c260ebba70d2816a00e8e73cbb350738e0225c4973eee23ae5ca91435d8a0

Observation 3608c238-937c-4e7d-85e2-0c785f42a213 · outbound

This paper cites BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval.

REARANK: Reasoning Re-ranking Agent via Reinforcement Learning BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval

Reference 27

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source=arxiv_source observed=2026-08-07T14:07:14.344080Z digest=sha256:d1c87d0ceb0195b20d1ba79459a7f12ebcf6043841e85a5f8425b734fd7260a6

Observation a60c737a-9a62-4961-a1bb-ca5ad58ffc21 · outbound

This paper cites Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents.

REARANK: Reasoning Re-ranking Agent via Reinforcement Learning Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents

Reference 28

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source=arxiv_source observed=2026-08-07T14:07:14.431147Z digest=sha256:fc5e9ddc506226fa9dca36813055e1ab4c767e358d57fdb678d7f2af16a16b7c

Observation becac842-21d4-47cc-a338-ae3d3902620a · outbound

This paper cites BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models.

REARANK: Reasoning Re-ranking Agent via Reinforcement Learning BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models

Reference 29

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source=arxiv_source observed=2026-08-07T14:07:14.526870Z digest=sha256:aecea092fa456314f2732163de5c39e674b05c0a1c019e28926e12a7e691e730

Observation fda85731-37e3-4a93-acdc-381f04a3d1db · outbound

This paper cites an unresolved cited work.

REARANK: Reasoning Re-ranking Agent via Reinforcement Learning Unresolved cited work

Reference 30

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raw_fallback, observed 2026-08-07T14:07:17.100374Z

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.

source=arxiv_source observed=2026-08-07T14:07:14.614063Z digest=sha256:77581e7d34722c0b1d3d7e26062ff84da45c7ded0d11434ce05d62aed13e85c2

Observation ea2088e5-e1a7-40ac-a035-46f613437a04 · outbound

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

REARANK: Reasoning Re-ranking Agent via Reinforcement Learning Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 31

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source=arxiv_source observed=2026-08-07T14:07:14.696818Z digest=sha256:b50d7e4dafee879b99d0d03bafee248467b376f898253f4cdd474a150d8ff1f5

Observation f0ac9cea-bce0-49d1-9bde-c1bcddb7f672 · outbound

This paper cites Emergent Abilities of Large Language Models.

REARANK: Reasoning Re-ranking Agent via Reinforcement Learning Emergent Abilities of Large Language Models

Reference 32

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source=arxiv_source observed=2026-08-07T14:07:14.782602Z digest=sha256:09f07eaa77d7a94688f2824c48269f082a6becccb9395ae56e6d4b8a1cecc2a9

Observation 501bf601-8980-4164-ab27-58d4b8670a56 · outbound

This paper cites an unresolved cited work.

REARANK: Reasoning Re-ranking Agent via Reinforcement Learning Unresolved cited work

Reference 33

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:07:14.862808Z digest=sha256:1b749fcbf055519a2f877c7d159df3377c8ceab6bed3db887bc17f949c01ae42

Observation 56ca47ee-e6b4-4438-bff4-72819e86f1c6 · outbound

This paper cites Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval.

REARANK: Reasoning Re-ranking Agent via Reinforcement Learning Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval

Reference 34

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source=arxiv_source observed=2026-08-07T14:07:14.938648Z digest=sha256:cc6c474dc9dc72c4c37ffa913b777eeb92ca3c20a10667e84455e1bb8da46173

Observation 47b725ec-579a-41bf-9a7f-93cddda09935 · outbound

This paper cites Qwen3 Technical Report.

REARANK: Reasoning Re-ranking Agent via Reinforcement Learning Qwen3 Technical Report

Reference 35

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:07:15.031918Z digest=sha256:bdeab2811249d2e82beb813878ee0a20cf43bcee6938525936e6b24e45751c91

Observation 9b5abaf1-5858-4275-9b24-27f187242f37 · outbound

This paper cites Qwen2.5 Technical Report.

REARANK: Reasoning Re-ranking Agent via Reinforcement Learning Qwen2.5 Technical Report

Reference 36

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source=arxiv_source observed=2026-08-07T14:07:15.119898Z digest=sha256:9262e5aa11ad8531cc2107a1a3ec11642f9e89ca011d4369514764cc41f5be8e

Observation aed25547-7cdf-423d-a46d-6f5ddd45e1d0 · outbound

This paper cites LIMO: Less is More for Reasoning.

REARANK: Reasoning Re-ranking Agent via Reinforcement Learning LIMO: Less is More for Reasoning

Reference 37

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:07:15.204385Z digest=sha256:ed1a601bd5662ba566621a53e116fdded617b2598e72acc4c0e902c93ada8610

Observation 68e4fece-e2f6-47de-80b8-99a6a2635ef0 · outbound

This paper cites an unresolved cited work.

REARANK: Reasoning Re-ranking Agent via Reinforcement Learning Unresolved cited work

Reference 38

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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.

source=arxiv_source observed=2026-08-07T14:07:15.268427Z digest=sha256:0e5d6c1a4a41a724bbf59a0c8c223bf6454b0017c0d1912543c6a58bbf71586f

Observation e0e7d205-670b-414d-9dc3-fd0d33be3fa2 · outbound

This paper cites MoqaGPT : Zero-Shot Multi-modal Open-domain Question Answering with Large Language Model.

REARANK: Reasoning Re-ranking Agent via Reinforcement Learning MoqaGPT : Zero-Shot Multi-modal Open-domain Question Answering with Large Language Model

Reference 39

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no resolver link, observed 2026-08-07T14:07:15.313276Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 0d263f1a-8584-4870-8589-6f015d4fab51 · outbound

This paper cites Exploring the Best Practices of Query Expansion with Large Language Models.

REARANK: Reasoning Re-ranking Agent via Reinforcement Learning Exploring the Best Practices of Query Expansion with Large Language Models

Reference 40

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no resolver link, observed 2026-08-07T14:07:15.431975Z

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source=arxiv_source observed=2026-08-07T14:07:15.431975Z digest=sha256:257aa7f438be7d2866ec98bc520036d52d579cc415d953557efd5e8f764f2ea9

Observation b3bd474b-6d74-4e3b-b6d4-ce42b5804e26 · outbound

This paper cites Assessing and Learning Alignment of Unimodal Vision and Language Models.

REARANK: Reasoning Re-ranking Agent via Reinforcement Learning Assessing and Learning Alignment of Unimodal Vision and Language Models

Reference 41

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verified exact
local_arxiv, observed 2026-08-07T14:07:16.104172Z

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.

source=arxiv_source observed=2026-08-07T14:07:15.516659Z digest=sha256:ffe0df3759ea762dedbb1bf300f5c743ccd0527f3037949a09609d61f5ccb024

Observation 9a046267-0fb6-4f94-9061-a14fe4bd8d06 · outbound

This paper cites Rank-R1: Enhancing Reasoning in LLM-based Document Rerankers via Reinforcement Learning.

REARANK: Reasoning Re-ranking Agent via Reinforcement Learning Rank-R1: Enhancing Reasoning in LLM-based Document Rerankers via Reinforcement Learning

Reference 42

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no resolver link, observed 2026-08-07T14:07:15.608784Z

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source=arxiv_source observed=2026-08-07T14:07:15.608784Z digest=sha256:324ec7af574b49f8016773bab63dfa82eb4c960ad7589f1a931d0d3e35f4375c

Observation e8854213-5ce7-4bcc-9724-c58a87ecd53d · outbound

This paper cites an unresolved cited work.

REARANK: Reasoning Re-ranking Agent via Reinforcement Learning Unresolved cited work

Reference 43

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source=arxiv_source observed=2026-08-07T14:07:15.689635Z digest=sha256:287c6dba77ab59f7675652a1d85921a1818ba0d7fcfec9e6143e1b7f96966534

Observation 099797bc-7ed4-4034-9836-dfdaf626f65b · outbound

This paper cites online" 'onlinestring :=.

REARANK: Reasoning Re-ranking Agent via Reinforcement Learning online" 'onlinestring :=

Reference 44

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no resolver link, observed 2026-08-07T14:07:15.782529Z

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source=arxiv_source observed=2026-08-07T14:07:15.782529Z digest=sha256:d11e680398131c7f8af5a1ff41af8d0ef876cf23696dafede40edb1c3b7604fc

Observation e2ae1378-e3c4-43df-9280-daffdc4c725b · outbound

This paper cites write newline.

REARANK: Reasoning Re-ranking Agent via Reinforcement Learning write newline

Reference 45

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no resolver link, observed 2026-08-07T14:07:15.898573Z

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source=arxiv_source observed=2026-08-07T14:07:15.898573Z digest=sha256:2d45905941aefe443f7723cc2c83ae684fa26a33440b13e1494f3edb1f046f0d

Pith citing papers

Observation 904dc180-4f69-4252-a47e-f5f07c2258ee · inbound

A Vision for Geo-Temporal Deep Research Systems: Towards Comprehensive, Transparent, and Reproducible Geo-Temporal Information Synthesis cites this paper.

A Vision for Geo-Temporal Deep Research Systems: Towards Comprehensive, Transparent, and Reproducible Geo-Temporal Information Synthesis REARANK: Reasoning Re-ranking Agent via Reinforcement Learning

Reference 54

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no resolver link, observed 2026-08-07T00:22:24.214754Z

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source=pdf_text observed=2026-08-07T00:22:24.214754Z digest=sha256:26d7ee55a1996e1717b8e27064c345b8180f4b2c6561852f722caec7a391541c

Observation 65b7558d-6e21-4157-a9f1-ddb5a96089ed · inbound

CogniSQL-R1-Zero: Lightweight Reinforced Reasoning for Efficient SQL Generation cites this paper.

CogniSQL-R1-Zero: Lightweight Reinforced Reasoning for Efficient SQL Generation REARANK: Reasoning Re-ranking Agent via Reinforcement Learning

Reference 16

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no resolver link, observed 2026-08-06T19:18:36.607918Z

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source=pdf_text observed=2026-08-06T19:18:36.607918Z digest=sha256:7700f6b67306ef0230c62e2563a182ae90100baf76df492703ffc9d976ac615d

Observation 9cdb853f-3249-462e-abae-dd7dff889d00 · inbound

ReasonRank: Empowering Passage Ranking with Strong Reasoning Ability cites this paper.

ReasonRank: Empowering Passage Ranking with Strong Reasoning Ability REARANK: Reasoning Re-ranking Agent via Reinforcement Learning

Reference 5

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verified exact
arxiv_id, observed 2026-05-18T23:56:55.111132Z

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.

source=pdf_text observed=2026-05-18T23:54:11.058239Z digest=sha256:f28ec5692fbe492d4a58ed7afc97bbe99e31ce44d7a6ff39c03ec2ccede519c0

Observation 5d987b17-618b-43bd-bf77-0d94176ecfcd · inbound

ERank: Fusing Supervised Fine-Tuning and Reinforcement Learning for Effective and Efficient Text Reranking cites this paper.

ERank: Fusing Supervised Fine-Tuning and Reinforcement Learning for Effective and Efficient Text Reranking REARANK: Reasoning Re-ranking Agent via Reinforcement Learning

Reference 42

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no resolver link, observed 2026-08-05T13:36:40.296461Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-05T13:36:40.296461Z digest=sha256:22a156e93b0c3924a0b927bc12c38d025de9a3e3433d8706506658c40b9a6e85

Observation b3de92ea-3f81-4977-b480-d990b3e990a0 · inbound

Rich-Media Re-Ranker: A User Satisfaction-Driven LLM Re-ranking Framework for Rich-Media Search cites this paper.

Rich-Media Re-Ranker: A User Satisfaction-Driven LLM Re-ranking Framework for Rich-Media Search REARANK: Reasoning Re-ranking Agent via Reinforcement Learning

Reference 40

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verified exact
arxiv_id, observed 2026-05-16T07:30:44.234572Z

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.

source=pdf_text observed=2026-05-16T07:30:28.303573Z digest=sha256:55d506d02fe9178c39b9a2c7b3e5488f69c65da5c9d676a8e81cfb56be592da4

Observation 28c9a35a-f2fc-452b-93f3-42fa969fb3b4 · inbound

MemSifter: Offloading LLM Memory Retrieval via Outcome-Driven Proxy Reasoning cites this paper.

MemSifter: Offloading LLM Memory Retrieval via Outcome-Driven Proxy Reasoning REARANK: Reasoning Re-ranking Agent via Reinforcement Learning

Reference 66

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no resolver link, observed 2026-08-02T19:23:18.186586Z

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source=pdf_text observed=2026-08-02T19:23:18.186586Z digest=sha256:f5c8652a817ebd2fe5dd033749aa888d62588a8518b297cb7e9aab921c2edbd7

Observation 9e5b552c-b100-400b-a8f4-79f2f8db55a4 · inbound

Optimizing RAG Rerankers with LLM Feedback via Reinforcement Learning cites this paper.

Optimizing RAG Rerankers with LLM Feedback via Reinforcement Learning REARANK: Reasoning Re-ranking Agent via Reinforcement Learning

Reference 53

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no resolver link, observed 2026-07-13T13:59:01.287449Z

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source=arxiv_source observed=2026-07-13T13:59:01.287449Z digest=sha256:ec86a5d75c3161baa48888ae0c1044c1b90bc5533e891b794d58fcccf6c99cd1