Paper Citation Record · LEDGER

DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 37 inbound Pith citation observations for arXiv:2503.00223.

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

pith.paper-citation-record.v1
2503.00223 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 37 of 37 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:02:43.163565Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
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malformed identifier0
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External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 92e40c64-c24d-435d-9768-d73dcd9df392 · inbound

Supervising the search process produces reliable and generalizable information-seeking agents cites this paper.

Supervising the search process produces reliable and generalizable information-seeking agents DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-23T02:22:25.425822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 63e574af-d708-4df1-9c00-db0f9766a112 · inbound

Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems cites this paper.

Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning

Reference 152

Resolution: verified exact; arxiv_id, observed 2026-05-22T21:42:10.633193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d51d3fbd-0a7f-4f24-8903-5d0eb1e50c3c · inbound

Generative AI Act II: Test Time Scaling Drives Cognition Engineering cites this paper.

Generative AI Act II: Test Time Scaling Drives Cognition Engineering DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning

Reference 140

Resolution: unresolved; no resolver link, observed 2026-08-16T12:02:43.163565Z

Source-reported events for the cited work

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Observation 1e834f5e-c856-4e03-bca5-c7395ddd959e · inbound

Synergizing RAG and Reasoning: A Systematic Review cites this paper.

Synergizing RAG and Reasoning: A Systematic Review DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-16T11:18:44.499446Z

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Observation f7548be0-6977-4d9b-8ed3-e540525ea30f · inbound

VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning cites this paper.

VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:50.943846Z

Source-reported events for the cited work

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Observation 1ad0381e-b8c5-4f9d-a42d-14fa07e5d01c · inbound

Training LLMs for EHR-Based Reasoning Tasks via Reinforcement Learning cites this paper.

Training LLMs for EHR-Based Reasoning Tasks via Reinforcement Learning DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T12:40:07.713578Z

Source-reported events for the cited work

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Observation 456875cc-aab0-41bf-88a4-46e23986080c · inbound

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning cites this paper.

Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T11:16:58.570880Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d650c154-9b6a-47ec-97db-02ba1e517737 · inbound

Not All Tokens Matter: Towards Efficient LLM Reasoning via Token Significance in Reinforcement Learning cites this paper.

Not All Tokens Matter: Towards Efficient LLM Reasoning via Token Significance in Reinforcement Learning DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-19T10:07:14.205834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation fbc2cd86-1f29-49da-81de-c5c269e04507 · inbound

TongSearch-QR: Reinforced Query Reasoning for Retrieval cites this paper.

TongSearch-QR: Reinforced Query Reasoning for Retrieval DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:19.358923Z

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Observation a362af34-c1ab-407d-a5c7-509a9f7d5b3e · inbound

Deep Research Agents: A Systematic Examination And Roadmap cites this paper.

Deep Research Agents: A Systematic Examination And Roadmap DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-06T23:26:56.686867Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 74867ca8-411d-4ef4-a9c4-a300bbb92cb6 · inbound

Harnessing the Power of Reinforcement Learning for Language-Model-Based Information Retriever via Query-Document Co-Augmentation cites this paper.

Harnessing the Power of Reinforcement Learning for Language-Model-Based Information Retriever via Query-Document Co-Augmentation DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-06T23:20:33.163275Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 26795ec2-f6b8-4c4a-aca3-55d9da057ccd · inbound

Beyond Independent Passages: Adaptive Passage Combination Retrieval for Retrieval Augmented Open-Domain Question Answering cites this paper.

Beyond Independent Passages: Adaptive Passage Combination Retrieval for Retrieval Augmented Open-Domain Question Answering DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T20:01:29.098275Z

Source-reported events for the cited work

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Observation 7070c892-62b0-4f54-98b4-5e5e37f5b15e · inbound

VerifyBench: A Systematic Benchmark for Evaluating Reasoning Verifiers Across Domains cites this paper.

VerifyBench: A Systematic Benchmark for Evaluating Reasoning Verifiers Across Domains DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T17:49:48.879997Z

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Unavailable: canonical work link unavailable.

Observation 7cf67020-c945-4a84-9d36-f214391e2de9 · inbound

The Landscape of Agentic Reinforcement Learning for LLMs: A Survey cites this paper.

The Landscape of Agentic Reinforcement Learning for LLMs: A Survey DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning

Reference 275

Resolution: verified exact; arxiv_id, observed 2026-05-18T19:21:46.705082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 48412180-b15a-4344-b498-059962a9d84f · inbound

Fishing for Answers: Exploring One-shot vs. Iterative Retrieval Strategies for Retrieval Augmented Generation cites this paper.

Fishing for Answers: Exploring One-shot vs. Iterative Retrieval Strategies for Retrieval Augmented Generation DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-15T16:30:36.849031Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d2e0ec60-d36f-407e-9bd4-7b6ae20e19bb · inbound

Domain-Aware RAG: MoL-Enhanced RL for Efficient Training and Scalable Retrieval cites this paper.

Domain-Aware RAG: MoL-Enhanced RL for Efficient Training and Scalable Retrieval DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T23:23:40.444325Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dee62877-ebc6-4646-bf44-2f95bb3a51bc · inbound

Revolutionizing Reinforcement Learning Framework for Diffusion Large Language Models cites this paper.

Revolutionizing Reinforcement Learning Framework for Diffusion Large Language Models DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-04T22:56:05.188630Z

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Observation a5189bcf-0271-46d0-9733-87846b9b63da · inbound

A Survey on Retrieval And Structuring Augmented Generation with Large Language Models cites this paper.

A Survey on Retrieval And Structuring Augmented Generation with Large Language Models DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-15T15:56:21.378508Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4cd9a041-d100-4fb8-81e6-9a0f83cb8368 · inbound

Rethinking On-policy Optimization for Query Augmentation cites this paper.

Rethinking On-policy Optimization for Query Augmentation DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-04T09:08:01.942254Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8101ce10-2b7b-4ad2-ad1d-c76b4a8d9052 · inbound

Agentic Reasoning for Large Language Models cites this paper.

Agentic Reasoning for Large Language Models DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning

Reference 61

Resolution: verified exact; arxiv_id, observed 2026-05-17T15:14:25.851932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5cd8013a-c711-43b9-8815-4a4fe2f0de6c · inbound

MoCo: A One-Stop Shop for Model Collaboration Research cites this paper.

MoCo: A One-Stop Shop for Model Collaboration Research DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-16T10:17:43.691021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 03f8cb0f-7b62-45c9-ae79-45eb3c5d24d0 · inbound

WikiSeeker: Rethinking the Role of Vision-Language Models in Knowledge-Based Visual Question Answering cites this paper.

WikiSeeker: Rethinking the Role of Vision-Language Models in Knowledge-Based Visual Question Answering DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-10T22:20:48.069237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 05338bfd-0bef-4c4f-9d79-39314030f58b · inbound

BRIDGE: Multimodal-to-Text Retrieval via Reinforcement-Learned Query Alignment cites this paper.

BRIDGE: Multimodal-to-Text Retrieval via Reinforcement-Learned Query Alignment DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-11T06:41:58.185271Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e46525f4-e212-48a2-b528-17297f55bcef · inbound

LLM-Oriented Information Retrieval: A Denoising-First Perspective cites this paper.

LLM-Oriented Information Retrieval: A Denoising-First Perspective DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning

Reference 81

Resolution: verified exact; arxiv_id, observed 2026-05-11T16:01:19.410968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 16da76d2-a5d4-48d5-8b8b-3ef158b9fec5 · inbound

LLM-Oriented Information Retrieval: A Denoising-First Perspective cites this paper.

LLM-Oriented Information Retrieval: A Denoising-First Perspective DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning

Reference 84

Resolution: verified exact; arxiv_id, observed 2026-05-21T00:19:16.635713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a7ec09a6-cadd-477a-9e6e-4a2a4d894c5d · inbound

When More Reformulations Hurt: Avoiding Drift using Ranker Feedback cites this paper.

When More Reformulations Hurt: Avoiding Drift using Ranker Feedback DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-09T19:35:39.582578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 94781733-9bd7-401c-b1a7-b464f600e30c · inbound

RICE-PO: Turning Retrieval Interactions into Credit Signals for Reasoning Agents cites this paper.

RICE-PO: Turning Retrieval Interactions into Credit Signals for Reasoning Agents DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-06-29T21:43:59.842264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 16c79743-9adf-4120-93e2-9c547587243c · inbound

RICE-PO: Turning Retrieval Interactions into Credit Signals for Reasoning Agents cites this paper.

RICE-PO: Turning Retrieval Interactions into Credit Signals for Reasoning Agents DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T05:01:24.898991Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7dc89a95-3121-49e1-9d73-4dd2d5ec0194 · inbound

Harness-1: Reinforcement Learning for Search Agents with State-Externalizing Harnesses cites this paper.

Harness-1: Reinforcement Learning for Search Agents with State-Externalizing Harnesses DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning

Reference 79

Resolution: verified exact; arxiv_id, observed 2026-07-01T23:26:22.121774Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 413b57ab-64db-4a4f-ba68-0b4b81f8cf15 · inbound

DuMate-DeepResearch: An Auditable Multi-Agent System with Recursive Search and Rubric-Grounded Reasoning cites this paper.

DuMate-DeepResearch: An Auditable Multi-Agent System with Recursive Search and Rubric-Grounded Reasoning DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-07-02T17:27:14.744673Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8de0e9af-6faf-41e1-a121-afbe97e9c5cf · inbound

Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application cites this paper.

Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning

Reference 293

Resolution: verified exact; arxiv_id, observed 2026-06-27T09:50:48.456275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 77cd4fe3-a529-4362-ae5e-4be8937997a1 · inbound

BashCoder-R1: Towards Robust and Explainable Bash Code Generation with Robustness-Aware Group Relative Policy Optimization cites this paper.

BashCoder-R1: Towards Robust and Explainable Bash Code Generation with Robustness-Aware Group Relative Policy Optimization DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning

Reference 19

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T17:05:50.881163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 36fbf8c0-1da7-4760-bb34-e4b8e2b4e1f5 · inbound

R$^2$-Searcher: Calibrating Retrieval and Reasoning Boundaries for Agentic Search cites this paper.

R$^2$-Searcher: Calibrating Retrieval and Reasoning Boundaries for Agentic Search DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-06-30T00:34:05.163437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2bdcbb89-4ab5-40b4-a29a-d9886aa44c2b · inbound

SLMs as Multi-Agent Routers: A Progressive SFT and Reinforcement Learning Approach cites this paper.

SLMs as Multi-Agent Routers: A Progressive SFT and Reinforcement Learning Approach DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T01:47:05.477346Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ac43929-65a7-4c0b-a76d-0c1212d43599 · inbound

Antares: Foundation Models for Agentic Vulnerability Localization cites this paper.

Antares: Foundation Models for Agentic Vulnerability Localization DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-04T07:50:50.618709Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 668a14fe-e0cd-440c-8854-cb92036753e2 · inbound

Contextual Information Policy Optimization for Search Agents cites this paper.

Contextual Information Policy Optimization for Search Agents DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-07T14:27:44.330205Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e9d89d0f-0a8a-4bc9-a019-463776c6bda5 · inbound

Contextual Information Policy Optimization for Search Agents cites this paper.

Contextual Information Policy Optimization for Search Agents DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-12T00:54:15.264777Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.