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Paper Citation Record · LEDGER

Open Data Synthesis For Deep Research

As of 14 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 10 inbound Pith citation observations for arXiv:2509.00375.

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

pith.paper-citation-record.v1
2509.00375 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T13:45:13.744163Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T15:12:55.803255Z

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

34 of 34 outbound references displayed

  • verified exact1
  • verified fuzzy3
  • unresolved29
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

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

Outbound references

Observation 57e90753-9c4b-49c9-abeb-aa7ce9abdfb8 · outbound

This paper cites Next, we conduct the second stage of reinforcement learning.

Open Data Synthesis For Deep Research Next, we conduct the second stage of reinforcement learning

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-05T13:45:13.744163Z digest=sha256:b208f72a59eb8a428dab06437aac92d97dbe56690228e9673a9eaf99af99e0c9

Observation 1c078b26-1e0b-4d38-a34b-834b59fdab0f · outbound

This paper cites Cognitive Kernel-Pro: A Framework for Deep Research Agents and Agent Foundation Models Training.

Open Data Synthesis For Deep Research Cognitive Kernel-Pro: A Framework for Deep Research Agents and Agent Foundation Models Training

Reference 6

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source=pdf_text observed=2026-08-05T13:45:11.590993Z digest=sha256:45a9e4249278399e33bb451ebbd24be6edc9118238bdfa2941d9bec3e9bf282f

Observation c35809ec-5754-4179-91b0-be83303a28b7 · outbound

This paper cites Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities.

Open Data Synthesis For Deep Research Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Reference 7

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source=pdf_text observed=2026-08-05T13:45:11.645449Z digest=sha256:e5a2102bff2f70d218b728666ac8f704fc49ecb6e7586dbc42c988610c3a4fca

Observation 6c7efccc-5c36-44c2-99e4-801f1d441dc5 · outbound

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

Open Data Synthesis For Deep Research DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 8

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source=pdf_text observed=2026-08-05T13:45:11.698405Z digest=sha256:f79063272f0a2f4c17f193e679ecd12f35b1944ffb9fcf8e7e1c0e21d88f2b3c

Observation ff94a419-327a-4215-91a4-d508473c1573 · outbound

This paper cites Deep Researcher with Test-Time Diffusion.

Open Data Synthesis For Deep Research Deep Researcher with Test-Time Diffusion

Reference 9

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source=pdf_text observed=2026-08-05T13:45:11.775915Z digest=sha256:b4e614f9b9183670864c11e59302b6621c8a1cd7624f500479c0d4c611012c86

Observation df384e68-eb04-4108-971d-c394512442a0 · outbound

This paper cites Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps.

Open Data Synthesis For Deep Research Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps

Reference 10

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source=pdf_text observed=2026-08-05T13:45:11.820398Z digest=sha256:d8f72c135cf7f8d396ece3827f5fe103f395b0c4600e4b61b948db5344b830e9

Observation 788e5e96-a1c4-4fad-9790-04349983b374 · outbound

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

Open Data Synthesis For Deep Research Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning

Reference 12

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source=pdf_text observed=2026-08-05T13:45:11.976736Z digest=sha256:c6f2de38df5775aca886de303e07766de37cda41551081b1755db5f907f8d10f

Observation 4fb63bbe-8308-4a46-9caf-1f2d2c592017 · outbound

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

Open Data Synthesis For Deep Research Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning

Reference 13

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source=pdf_text observed=2026-08-05T13:45:12.038009Z digest=sha256:ea4185b72e8446df0a0e842a69a0cd2d5204407398a26ba854f6a7421ae5d96f

Observation 82576d0c-c8fa-40f1-8c65-7d048df87609 · outbound

This paper cites 14 Technical report Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al.

Open Data Synthesis For Deep Research 14 Technical report Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al

Reference 15

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-05T13:45:12.192635Z digest=sha256:4413b7d1625f92954b6adcf4474181002fd875f3cf2cc02ce65247be24c13fab

Observation e0d55f66-e760-45ab-9ef5-242b6d0bcc9b · outbound

This paper cites Measuring and Narrowing the Compositionality Gap in Language Models.

Open Data Synthesis For Deep Research Measuring and Narrowing the Compositionality Gap in Language Models

Reference 16

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source=pdf_text observed=2026-08-05T13:45:12.298235Z digest=sha256:be083a8c6c72976f8512ea347dd7521602151f1ecbf1147e3ac667a2b3169c08

Observation 2e4b11ed-0567-4a90-ae0c-ef4857d241af · outbound

This paper cites Hawkbench: Investigating resilience of rag methods on stratified information-seeking tasks.

Open Data Synthesis For Deep Research Hawkbench: Investigating resilience of rag methods on stratified information-seeking tasks

Reference 18

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-05T13:45:12.418834Z digest=sha256:e67346ad5110b942a74f4ebaf5bd6752f50490d140b8adbb76042e1b18d3278d

Observation 1941d97a-8332-4671-a5b5-fa55cc4cb781 · outbound

This paper cites Alita: Generalist Agent Enabling Scalable Agentic Reasoning with Minimal Predefinition and Maximal Self-Evolution.

Open Data Synthesis For Deep Research Alita: Generalist Agent Enabling Scalable Agentic Reasoning with Minimal Predefinition and Maximal Self-Evolution

Reference 19

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source=pdf_text observed=2026-08-05T13:45:12.481722Z digest=sha256:a209b8c1be1a31946f98f0e12429c58f63086cb334d140e9168033d7d9dcb00a

Observation 96ae09e3-40bc-4870-8ea4-cd2326ff7d70 · outbound

This paper cites Pangu deepdiver: Adaptive search intensity scaling via open-web reinforcement learning.

Open Data Synthesis For Deep Research Pangu deepdiver: Adaptive search intensity scaling via open-web reinforcement learning

Reference 23

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source=pdf_text observed=2026-08-05T13:45:12.812195Z digest=sha256:5fec80485a57474a94d011474ca7e41241b2b794d7b7f539630375619b09b8d7

Observation 23b0208c-f8d2-48f6-aa1c-a6b1e0843da2 · outbound

This paper cites ZeroSearch: Incentivize the Search Capability of LLMs without Searching.

Open Data Synthesis For Deep Research ZeroSearch: Incentivize the Search Capability of LLMs without Searching

Reference 24

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source=pdf_text observed=2026-08-05T13:45:12.880393Z digest=sha256:7407d8bfc96fc6d1fcc293b650f8b0969d64140515b2fdd1a8c3751afb983631

Observation 52723a50-cf53-423d-b5c2-90fe0b142f5a · outbound

This paper cites Qwen2 Technical Report.

Open Data Synthesis For Deep Research Qwen2 Technical Report

Reference 25

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source=pdf_text observed=2026-08-05T13:45:12.966279Z digest=sha256:7230bcd6399d049e4e10e2f2df3a378e650abb798179d7536026a3913f1f2118

Observation bbfa09ef-40af-4dd6-a06a-e8cf811499b9 · outbound

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

Open Data Synthesis For Deep Research Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions

Reference 26

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source=pdf_text observed=2026-08-05T13:45:13.050432Z digest=sha256:c267a29a10067630cae01846ba87203b95809dcda313427932c4faf681485b84

Observation 488771f2-8483-4d15-b829-1b80f323ac68 · outbound

This paper cites BrowseComp: A Simple Yet Challenging Benchmark for Browsing Agents.

Open Data Synthesis For Deep Research BrowseComp: A Simple Yet Challenging Benchmark for Browsing Agents

Reference 27

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source=pdf_text observed=2026-08-05T13:45:13.108221Z digest=sha256:3889c7326d5cda2ef1a88a2169c2efb3fe87af063f1ebe2c1d69da77c9a61234

Observation 4acf394a-80e4-4910-87b1-0967ce9e5168 · outbound

This paper cites WebWalker: Benchmarking LLMs in Web Traversal.

Open Data Synthesis For Deep Research WebWalker: Benchmarking LLMs in Web Traversal

Reference 28

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source=pdf_text observed=2026-08-05T13:45:13.203875Z digest=sha256:227d88966a1fc8b4ca4657da5e0a7637276c57c6743ff5272b004142c4e28bff

Observation fa4559d7-d8b0-4e67-9844-968734406a40 · outbound

This paper cites Qwen3 Technical Report.

Open Data Synthesis For Deep Research Qwen3 Technical Report

Reference 29

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source=pdf_text observed=2026-08-05T13:45:13.288450Z digest=sha256:cb394433305d7b16022a3aa7e7a4fa653bda9c3a960c841ca50ecd4da1d06e9d

Observation edfbb634-fa44-407f-8a7c-f71cd0a7b2d2 · outbound

This paper cites HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering.

Open Data Synthesis For Deep Research HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering

Reference 30

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source=pdf_text observed=2026-08-05T13:45:13.378399Z digest=sha256:602bea54247b4b72982ee180d654c8a944991ce1ace5f0e05c4a1165f1768f11

Observation 085b71de-e681-4f06-9644-85c554ffeaa7 · outbound

This paper cites Agentic Information Retrieval.

Open Data Synthesis For Deep Research Agentic Information Retrieval

Reference 31

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source=pdf_text observed=2026-08-05T13:45:13.460438Z digest=sha256:ef0d400f10cf411f024a97610c7a54a2d112323a0131619b031692f0235f832c

Observation eb491e3e-a787-4dc0-86d4-ec8d280d851b · outbound

This paper cites AgentOrchestra: Orchestrating Multi-Agent Intelligence with the Tool-Environment-Agent(TEA) Protocol.

Open Data Synthesis For Deep Research AgentOrchestra: Orchestrating Multi-Agent Intelligence with the Tool-Environment-Agent(TEA) Protocol

Reference 32

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source=pdf_text observed=2026-08-05T13:45:13.545426Z digest=sha256:3708f73c4e78585f32ee7dfbd839e9f901a77291b0fd091ecfa9357b9367afd0

Observation d51a29c2-c421-448f-8281-6e5b4431d019 · outbound

This paper cites Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely.

Open Data Synthesis For Deep Research Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely

Reference 33

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source=pdf_text observed=2026-08-05T13:45:13.613918Z digest=sha256:dc51d125a51d204ca3651222956285a8791eb4960694fdc6c28e57d57f4e60c7

Observation bc5b8a63-854e-459d-9723-736403b9df16 · outbound

This paper cites shortcut.

Open Data Synthesis For Deep Research shortcut

Reference 34

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-05T13:45:13.674162Z digest=sha256:2127ffd5fa2a21767c0f81b2a369cb6921b683dd3798a614a42784cd7c5f48ea

Observation d1ca22b1-0123-4818-9a96-498baefa963c · outbound

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

Open Data Synthesis For Deep Research RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generation

Reference 1976

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source=pdf_text observed=2026-08-05T13:45:11.359396Z digest=sha256:25074c5d0c363723eff073d1eb54aa2b8cf51a371a587da67467c75c44bae699

Observation bb9447a8-5dab-4752-bc39-7f055dbbf040 · outbound

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

Open Data Synthesis For Deep Research High-Dimensional Continuous Control Using Generalized Advantage Estimation

Reference 2009

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source=pdf_text observed=2026-08-05T13:45:12.553416Z digest=sha256:134f5538758007c5464aef3c22c74e467234b30e221c2c92ea42ee21d4c7ba1b

Observation 51020e55-4f25-442b-abf3-11595a9198b5 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Open Data Synthesis For Deep Research Proximal Policy Optimization Algorithms

Reference 2015

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source=pdf_text observed=2026-08-05T13:45:12.651316Z digest=sha256:0611a40739e44f350434381530093f36f544722fc07f53a0054484f490fa8d19

Observation 990c622e-a4c8-44a6-9332-c6a3a8e7b6a9 · outbound

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

Open Data Synthesis For Deep Research DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 2017

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source=pdf_text observed=2026-08-05T13:45:12.737814Z digest=sha256:423672e2ca8ea9a616a75ab51f84561ec6b8f6087146efe936c4a37ed4bd62da

Observation b55c6770-84ad-4a04-b6ed-81b4a1a5c535 · outbound

This paper cites WebSailor: Navigating Super-human Reasoning for Web Agent.

Open Data Synthesis For Deep Research WebSailor: Navigating Super-human Reasoning for Web Agent

Reference 2019

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no resolver link, observed 2026-08-05T13:45:12.135736Z

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source=pdf_text observed=2026-08-05T13:45:12.135736Z digest=sha256:14a82c3775fef6f34a9c62a1358b55b3a971de0cf9a412604b773bfda9ff51ba

Observation 46c89d5a-ce45-45ba-b26a-afcbd3e4536c · outbound

This paper cites OpenAI o1 System Card.

Open Data Synthesis For Deep Research OpenAI o1 System Card

Reference 2020

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source=pdf_text observed=2026-08-05T13:45:11.868980Z digest=sha256:9673f3fc71a0a64d8426dc537c2f9bd867b286998b128976924aeb0d48cea0cc

Observation 60c71515-6770-4f91-bc24-87c5935698f1 · outbound

This paper cites Scent of knowledge: Optimizing search-enhanced reasoning with information foraging.

Open Data Synthesis For Deep Research Scent of knowledge: Optimizing search-enhanced reasoning with information foraging

Reference 2022

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source=pdf_text observed=2026-08-05T13:45:12.341801Z digest=sha256:b7045ecc18863fea15b068a8be8d79888100d8fb2814b4456f08fdb50334065d

Observation e851582e-a11c-4268-8ab1-04791c355845 · outbound

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

Open Data Synthesis For Deep Research ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning

Reference 2023

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source=pdf_text observed=2026-08-05T13:45:11.469664Z digest=sha256:ae421f83f8b88eb2480587f20807ac294a265266d52cb91a7d25744a89eed341

Observation cbd2aa57-6f18-4acc-b836-73395ee09a68 · outbound

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

Open Data Synthesis For Deep Research RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generation

Reference 2024

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source=pdf_text observed=2026-08-05T13:45:11.399188Z digest=sha256:8b99ea50446c817863512ff1bd600d667602970d0ab55f9d3ef508140b557a0c

Observation 010e1081-4ccf-42b1-8b51-87b92f2e4ab1 · outbound

This paper cites Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection.

Open Data Synthesis For Deep Research Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection

Reference 2025

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source=pdf_text observed=2026-08-05T13:45:11.313077Z digest=sha256:b2716b16c8bba492e0850149e7708300d7ea89b00ddb0f44af49d895bf4e29e3

Pith citing papers

Observation 902daa26-e857-4576-90cd-5784b9ca8960 · inbound

Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search cites this paper.

Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search Open Data Synthesis For Deep Research

Reference 33

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source=arxiv_source observed=2026-08-04T08:48:44.022931Z digest=sha256:089e8b5a066ad9d7ecbebc5f78619b2b95e91fd8d974e3a7685ad63b7c18afe7

Observation 2ca88b65-f366-489f-be5c-74f95098c5e1 · inbound

Scaling the Scaling Logic: Agentic Meta-Synthesis of Logic Reasoning cites this paper.

Scaling the Scaling Logic: Agentic Meta-Synthesis of Logic Reasoning Open Data Synthesis For Deep Research

Reference 6

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arxiv_id, observed 2026-05-16T12:12:51.101340Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-16T12:12:30.041005Z digest=sha256:c7624741ebe778a99d1578f61720177bfde48a4fc22e8b343c0e8120762b2e29

Observation 1c46dc45-be74-42f4-9ad9-bd745142935a · inbound

Learning to Retrieve from Agent Trajectories cites this paper.

Learning to Retrieve from Agent Trajectories Open Data Synthesis For Deep Research

Reference 18

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arxiv_id, observed 2026-05-14T01:48:36.098393Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-14T01:47:44.558384Z digest=sha256:644e3a2b3f333097ab7952920c551df83dd479e30959d12b6624f4293d8c0d00

Observation 8cb3d55a-893c-4926-8b76-e8568037aabb · inbound

POINTS-Seeker: An Open Recipe for Multimodal Search Agents with Visual Memory Management cites this paper.

POINTS-Seeker: An Open Recipe for Multimodal Search Agents with Visual Memory Management Open Data Synthesis For Deep Research

Reference 50

Resolution
metadata mismatch
arxiv_id, observed 2026-05-10T13:10:26.476983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-10T13:09:24.304696Z digest=sha256:7994bbd6d884351be3429c54a21dcfc73aa3ab2501ddf7b8a3aba6854e9f35b7

Observation 0bb23a1d-8b67-4794-8210-b72fbde161a6 · inbound

POINTS-Seeker: An Open Recipe for Multimodal Search Agents with Visual Memory Management cites this paper.

POINTS-Seeker: An Open Recipe for Multimodal Search Agents with Visual Memory Management Open Data Synthesis For Deep Research

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-02T16:18:25.106504Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T16:18:25.106504Z digest=sha256:b5457228d7c6d6b69148fa0e60f40218d775771bcb4219b101c395555238dedd

Observation 5649073c-62e3-48b1-af02-5c3a72e6ec48 · inbound

HyperEyes: Dual-Grained Efficiency-Aware Reinforcement Learning for Parallel Multimodal Search Agents cites this paper.

HyperEyes: Dual-Grained Efficiency-Aware Reinforcement Learning for Parallel Multimodal Search Agents Open Data Synthesis For Deep Research

Reference 40

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T01:45:52.317362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-11T01:28:36.266167Z digest=sha256:ac8ec25e6f393cda7d2812031e3c6cb598ce4d864f5cc72c773f5bc5d57fa3cb

Observation 0880bf4e-d718-41d5-a047-668cec95db32 · inbound

HyperEyes: Dual-Grained Efficiency-Aware Reinforcement Learning for Parallel Multimodal Search Agents cites this paper.

HyperEyes: Dual-Grained Efficiency-Aware Reinforcement Learning for Parallel Multimodal Search Agents Open Data Synthesis For Deep Research

Reference 40

Resolution
metadata mismatch
arxiv_id, observed 2026-05-12T03:21:19.026653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-12T03:18:01.006274Z digest=sha256:785836bd84ea5d7b942bbf8090f161f3a544ff6831112faff69789a25145d874

Observation 988ed784-0343-432a-8af5-6aacf7aa0904 · inbound

FORT-Searcher: Synthesizing Shortcut-Resistant Search Tasks for Training Deep Search Agents cites this paper.

FORT-Searcher: Synthesizing Shortcut-Resistant Search Tasks for Training Deep Search Agents Open Data Synthesis For Deep Research

Reference 24

Resolution
metadata mismatch
arxiv_id, observed 2026-07-03T10:27:56.444815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-06-27T10:01:45.332920Z digest=sha256:4e43f7b02e4d736bc2c77678a4369b035b448f1a0eaa18b709c6e36f95784eb1

Observation 5ed714ef-7fed-42ce-b0d1-a26e4501faa2 · 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 Open Data Synthesis For Deep Research

Reference 68

Resolution
metadata mismatch
arxiv_id, observed 2026-06-27T09:50:48.353965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-06-27T09:46:30.702256Z digest=sha256:1ebe2e8756f27f229b797cbd1416f262330fa7e6900f44d89ad863de3ec92e4f

Observation cd7621d0-a636-442c-a7f2-f090a868eeb4 · inbound

Fetch-then-Explore: Decoupling Selection from Extraction over a Persistent Workspace for Search Agents cites this paper.

Fetch-then-Explore: Decoupling Selection from Extraction over a Persistent Workspace for Search Agents Open Data Synthesis For Deep Research

Reference 114

Resolution
unresolved
no resolver link, observed 2026-08-04T15:12:55.803255Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T15:12:55.803255Z digest=sha256:bf428aedf7e4f7bd3fdb51bc96dcd4b0de772b2262675645077b5acaa556f125