Paper Citation Record · LEDGER

AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning

As of 4 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 1 inbound Pith citation observation for arXiv:2604.17337.

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

pith.paper-citation-record.v1
2604.17337 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-10T06:45:08.361225Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-31T23:09:28.924670Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

39 of 39 outbound references displayed

verified exact0
verified fuzzy28
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch11

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e6093406-3360-4c05-af54-6bbc460f7ba7 · outbound

This paper cites Your group-relative advantage is biased.

AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning Your group-relative advantage is biased

Reference 1

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T06:46:37.197728Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation cee9313d-25ed-4a82-b043-4d56f1546e9a · outbound

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

AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning Advances in Neural Information Processing Systems , year=

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T15:25:18.578584Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 281027da-89a1-4300-97f3-8af4e42df995 · outbound

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

AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning Advances in Neural Information Processing Systems , year=

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T15:25:18.583872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation dbcbc64a-248e-48f9-b624-ea3ff03e6fea · outbound

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

AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning The Thirteenth International Conference on Learning Representations , year=

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T15:25:18.573553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 0190acbf-dee4-4daf-add2-62c9c74055d5 · outbound

This paper cites Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , year=.

AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , year=

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T15:25:18.570958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation ca5deebe-8370-4c7c-8e9c-5843fc99e55e · outbound

This paper cites Chao Wang, Hehe Fan, Huichen Yang, Zhengdong Hu, Sarvnaz Karimi, Lina Yao, and Yi Yang.

AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning Chao Wang, Hehe Fan, Huichen Yang, Zhengdong Hu, Sarvnaz Karimi, Lina Yao, and Yi Yang

Reference 6

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T06:46:37.189950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 91b7f513-878b-4103-994a-b5ed52a6bca6 · outbound

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

AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning The Fourteenth International Conference on Learning Representations , year=

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T15:25:18.575813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b98c20a1-9f6c-4a20-a2ef-09722f58f5e0 · outbound

This paper cites Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing , pages=.

AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing , pages=

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T15:25:18.581199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c6ffe90c-6404-48bc-b28a-23a0df846a68 · outbound

This paper cites Proceedings of the AAAI Conference on Artificial Intelligence , volume=.

AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning Proceedings of the AAAI Conference on Artificial Intelligence , volume=

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T15:25:18.589891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation dab2bbb0-4967-4d89-9765-77e49a0caf51 · outbound

This paper cites Workshop on Bridging Language, Agent, and World Models for Reasoning and Planning (LAW) , year=.

AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning Workshop on Bridging Language, Agent, and World Models for Reasoning and Planning (LAW) , year=

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T15:25:18.636211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d73acbdf-0160-4d26-ab3f-4a57dd13e061 · outbound

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

AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning Retrieval-Augmented Generation for Large Language Models: A Survey

Reference 11

Resolution: metadata mismatch; local_arxiv, observed 2026-05-10T06:46:37.192445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b15df252-904c-48ca-85ca-431b7a6a31c0 · outbound

This paper cites Proceedings of the 30th ACM SIGKDD conference on knowledge discovery and data mining , pages=.

AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning Proceedings of the 30th ACM SIGKDD conference on knowledge discovery and data mining , pages=

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T15:25:18.628822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e75174e1-6e47-47e7-98da-1dcdba09b7ea · outbound

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

AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 13

Resolution: metadata mismatch; local_arxiv, observed 2026-05-10T06:46:37.187218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f8b538df-64f1-4fcc-915a-2735c550ba9a · outbound

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

AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning Advances in Neural Information Processing Systems , year=

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T15:25:18.631246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 45c9c97a-2879-4066-9db9-ed45f4a5bc2e · outbound

This paper cites Qwen3 Technical Report.

AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning Qwen3 Technical Report

Reference 15

Resolution: metadata mismatch; local_arxiv, observed 2026-05-10T06:46:37.195049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 4a61006d-708a-4434-9960-99300f0f41e6 · outbound

This paper cites OpenAI o1 System Card.

AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning OpenAI o1 System Card

Reference 16

Resolution: metadata mismatch; local_arxiv, observed 2026-05-10T06:46:37.184826Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 22c0bbf1-7df2-4060-93cb-d8bbdf9c9bac · outbound

This paper cites Proceedings of the Second Conference on Language Modeling (COLM) , year=.

AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning Proceedings of the Second Conference on Language Modeling (COLM) , year=

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T15:25:18.633969Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a7259aa2-8112-4dd1-99ad-1dce0f11650e · outbound

This paper cites R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning.

AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning

Reference 18

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T18:37:17.866238Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d153f66b-6fd6-490e-b96a-8cc53001c914 · outbound

This paper cites Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing , pages=.

AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing , pages=

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T15:25:18.638816Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c52b4e2f-c84a-422b-a2f3-1637ad994beb · outbound

This paper cites Atom-Searcher: Enhancing Agentic Deep Research via Fine-Grained Atomic Thought Reward.

AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning Atom-Searcher: Enhancing Agentic Deep Research via Fine-Grained Atomic Thought Reward

Reference 20

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T06:46:37.202649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d4213e81-cda5-4b02-b71e-4f4821db44e2 · outbound

This paper cites R1-Searcher++: Incentivizing the Dynamic Knowledge Acquisition of LLMs via Reinforcement Learning.

AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning R1-Searcher++: Incentivizing the Dynamic Knowledge Acquisition of LLMs via Reinforcement Learning

Reference 21

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T06:46:37.205241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 9d2417be-8f4c-4ebd-bfc0-dcdda6ac59bb · outbound

This paper cites Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing , pages=.

AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing , pages=

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T15:25:18.618453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d8707d44-7d20-4f61-87f2-27ef9ab87054 · outbound

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

AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning The Fourteenth International Conference on Learning Representations , year=

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T15:25:18.623588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3b03737f-1c58-4bb4-b819-9595385b3465 · outbound

This paper cites Proceedings of the 61st annual meeting of the association for computational linguistics (ACL) , pages=.

AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning Proceedings of the 61st annual meeting of the association for computational linguistics (ACL) , pages=

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T15:25:18.612819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 57fecfd0-04c5-4758-9620-107c843dd2c8 · outbound

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

AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning The Eleventh International Conference on Learning Representations , year=

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T15:25:18.615270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 0c615517-c768-405b-9e59-3be4fc17db26 · outbound

This paper cites Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) , pages=.

AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) , pages=

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T15:25:18.626306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 858d86dc-16d8-4772-bc79-00cb463abf1f · outbound

This paper cites Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval , pages=.

AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval , pages=

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T15:25:18.620937Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 2ee8b7a0-e62e-4c74-9b4a-12178c255458 · outbound

This paper cites ToolRL: Reward is All Tool Learning Needs.

AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning ToolRL: Reward is All Tool Learning Needs

Reference 28

Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T00:26:48.594660Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation bf7b4c57-634f-4b31-9edd-2d8f1128e07a · outbound

This paper cites Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track , pages=.

AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track , pages=

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T15:25:18.641535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7097dbf3-0253-4e02-8dd0-06fc49e8863c · outbound

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

AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning The Fourteenth International Conference on Learning Representations , year=

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T15:25:18.601057Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c3cb4c6a-02e6-4944-909d-409788f3657a · outbound

This paper cites Transactions of the Association for Computational Linguistics , volume=.

AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning Transactions of the Association for Computational Linguistics , volume=

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T15:25:18.598461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 879774a5-d3b6-4705-89c0-ba060045384b · outbound

This paper cites Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=.

AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T15:25:18.593020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 8e05f851-44ce-4f01-ac9a-8976a1388261 · outbound

This paper cites Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL) , pages=.

AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL) , pages=

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T15:25:18.595794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 9fe08cee-44d8-42ed-a693-d10f47d04ea2 · outbound

This paper cites Proceedings of the 2018 conference on empirical methods in natural language processing , pages=.

AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning Proceedings of the 2018 conference on empirical methods in natural language processing , pages=

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T15:25:18.609879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation aee487f2-672d-49ff-8143-98663a0f2b7c · outbound

This paper cites Proceedings of the 28th International Conference on Computational Linguistics , pages=.

AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning Proceedings of the 28th International Conference on Computational Linguistics , pages=

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T15:25:18.603751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 8ce10910-8b16-406d-a473-1da8d23e5c5d · outbound

This paper cites Transactions of the Association for Computational Linguistics , volume=.

AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning Transactions of the Association for Computational Linguistics , volume=

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T15:25:18.606982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 8276bfe4-d868-4f0c-afc8-ecdefc903cd1 · outbound

This paper cites Findings of the Association for Computational Linguistics: EMNLP 2023 , pages=.

AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning Findings of the Association for Computational Linguistics: EMNLP 2023 , pages=

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T15:25:18.643928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation edbc6b45-9eb6-4879-8302-df06292656d7 · outbound

This paper cites Proceedings of the 2020 conference on empirical methods in natural language processing (EMNLP) , pages=.

AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning Proceedings of the 2020 conference on empirical methods in natural language processing (EMNLP) , pages=

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T15:25:18.586720Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation ed4dc2fe-d31f-42b6-b609-2179b75b931e · outbound

This paper cites Text Embeddings by Weakly-Supervised Contrastive Pre-training.

AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning Text Embeddings by Weakly-Supervised Contrastive Pre-training

Reference 39

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T04:54:04.551857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Pith citing papers

Observation 89e2855a-47e0-4ec9-b7e7-941c34bdad27 · inbound

MemChain: Learning Interpretable Memory Traces for Memory-Augmented LLM Agents cites this paper.

MemChain: Learning Interpretable Memory Traces for Memory-Augmented LLM Agents AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning

Reference 47

Resolution: unresolved; no resolver link, observed 2026-07-31T23:09:28.924670Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.