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Paper Citation Record · LEDGER

Open Data Synthesis For Deep Research

As of 13 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 2026-08-13T06:32:02.005865+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 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T13:45:13.744163Z digest=sha256:4ce570c5a73d255f5f780e321d8afc9eebf45ef72adcde27ab98abe50c16acc5

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=sha256:21ddcab35092f58e76fdd7e25bee75c6b0bf7b6074260d9717d176db0ac13d8e

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=sha256:3b91d4b2d76e3021aaf5e49d5075e95895878163cf11b8c9d3e19e277dd02067

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=sha256:6dfdb36ec45f1af5c50ae5b4ad8000cab3e23b65c91609b8e10dcaffe498729d

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:cf35fa706ecca5e18da5ad1bba601177054d4c86a37560ef9ad83f13353e7e15

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=sha256:352faddbc737810f0f2ee94510d6752a9b2a25b9a8974f97c7f3e4ac013db0a4

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:a75b935f1292c56980bb4866d678b7ea2e5dda0cbad32106d18b8370c51fc41c

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=sha256:67c337201fa4eb19a915dde7a3b724c3d78179980e72c7d0565dd293c40bced7

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 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T13:45:12.192635Z digest=sha256:e2f71bddc62504b2e352edab0f80ab512d8c2d519f8dfb92ec898254bef614a2

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=sha256:739f2eb68080d2f7de2660022a282c68d82456ae4bc41c7125c4a62608051f50

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 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T13:45:12.418834Z digest=sha256:9e7d665c39e841daca0b2797b24d318b1e1a856b95d223c8857520f839ff035f

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:d35e0b0f2cab7197477623ddb33940c60a66233422ba27713cdf2c94019c9dc0

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=sha256:1699661947ec847101ffd27bc1024b7a040940fc496f0e6c9ce690244b6a538f

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:b8a2decd9c3b6aca2744bc3572aee58e8848c3353df078a208d8e14d9db20542

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:d2c90aca0b70f5bb7103f4b121115b49abe0e3787d53f6c53ddf5c6d48541e8d

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=sha256:7c3f8089a75fbe9f487e82d799e9ba74d21e9de8ddc2a161741fc71196d91dc2

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:a964b5fe9cc5869426b82be853020b081a3193368de41d704a46ee892ea0eaf8

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=sha256:50552c30943b7c2079ab74ef8df230ab46fa9dc886ae4343e38d8b152e854ee8

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:e104df046457e44441cb3aa98e41b66ff9d0c3ce95b3c5f30f1bbf89789c5df8

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:beaafefc1712d49329f8b1051324ac52e0147afba8b83ace0776d32115fcfebd

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=sha256:29162c6ae370e82449dd2cc50e7b6bc815f6800908749b2116c6debecb3b70e0

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:d857bfcf41e6a611d29544ce3b29f53ecacfc58269d183ad99391c96e95594e1

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=sha256:41b02642491f6632e0ca8476635e6b04dde540c4220f83873b4ef0c335d2283a

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 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T13:45:13.674162Z digest=sha256:1ecf78c51e200f8caf719bbb3379163626264c49f330c7f9fd8b82861b5c2a5c

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:e45b1d1db4675046fc31da2d653de9fbd13f0104a81f4d9f710a9506b4d397ac

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:d103b714fd60a5a514de355c9699e2d824229c43324968f67da90807804d2552

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:dec2d749be43a65a5866601f71e2cd9510448b7037d7e7c0bead586f98d54328

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=sha256:6f270f1a4b7e28ed9219819de43d84d3ee0a35dc583abdef1abbcb7d1a2d263b

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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source=pdf_text observed=2026-08-05T13:45:12.135736Z digest=sha256:a1cd4044b4fe98ffd3a28538e9eb450e2b352ea03b39f14e928cb87e12e72238

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:bd38d76191a4d1466efa6c10a5a52a80a8f06b87c9d450be47f4e3fd5738855e

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:f97da8f72004e474da8a2ddfe07dc9c71d1c5ddf5b7e69b552468be79c98823d

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:ae89064926adaad2d44ae926cd926a451a2f4aeb01dca683d9bc97a797279197

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=sha256:3ed6832b7cc7343c83a046a9409cd2f8a6509ea1cc9d48872c6afbee9750364e

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=sha256:32082f3e535661e20047e1abc4d795a2e605a68c76a1b60173b744fe0f8f0f62

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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no resolver link, observed 2026-08-04T08:48:44.022931Z

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source=arxiv_source observed=2026-08-04T08:48:44.022931Z digest=sha256:53ee3b4e6d227f7ebc2efb040d19718fb27fb27e256693bb9d64e3430737b456

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 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-16T12:12:30.041005Z digest=sha256:61af9662f363d9029a4f18d34f71a4b538141baba8ea73c89bc2dca85e508f9f

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 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-14T01:47:44.558384Z digest=sha256:6f19e41df20c0f51866c3d9aa6b7c3eb716cc29a36548e570078496652783734

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 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-10T13:09:24.304696Z digest=sha256:47fb95356530ecc06dbe89962db258986dfa0c929b2d6c77e9e7cbc757239b48

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=sha256:3f0fc35b643126a3e89576dc19e59de4ee7c2dbd27b955f4ef10f5e2fc8a2864

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 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-11T01:28:36.266167Z digest=sha256:eb0e7d2fa620b6ff0f502ae376632cc2cd0e0fc1813ef93312f46ae86d0533cb

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 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-12T03:18:01.006274Z digest=sha256:9e8572bf1eba1723423b3a1b16b94da277c67781d5a08bd8faa9619416a78b8f

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 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-06-27T10:01:45.332920Z digest=sha256:6c52a933f3614fc9bed26afedbead22066479726737ed2a763db1f12d6540fa0

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 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-06-27T09:46:30.702256Z digest=sha256:54ab94a59c39471eb7784fb3f4ce9c156275363a6d64b3de952889d27f8e8aa2

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=sha256:864581d91165c8a70d76e76003f9114809b95f52c89f651b5383260d53fb2939