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Paper Citation Record · LEDGER

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation

As of 18 August 2026, this Paper Citation Record lists 100 of 106 outbound references and 9 inbound Pith citation observations for arXiv:2505.15872.

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

pith.paper-citation-record.v1
2505.15872 v2

Coverage vector

measured 100 of 106 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:19:23.537787Z

measured 109 of 109 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:22:23.654894Z

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

100 of 106 outbound references displayed

  • verified exact2
  • verified fuzzy16
  • unresolved81
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

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

Outbound references

Observation 267f1866-ee30-47f4-a9e4-7479e897a770 · outbound

This paper cites Claude 3.7 sonnet, 2025.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Claude 3.7 sonnet, 2025

Reference 1

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source=pdf_text observed=2026-08-07T15:19:13.947951Z digest=sha256:3232c0b456ae41ab2fc7481dee9abd6c2ac664e8222f6f902f32919c8c6fae22

Observation fd6d2c75-f7b2-4483-be7b-083fc2594580 · outbound

This paper cites Benchmarking large language mod- els in retrieval-augmented generation.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Benchmarking large language mod- els in retrieval-augmented generation

Reference 2

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source=pdf_text observed=2026-08-07T15:19:13.999769Z digest=sha256:e297ab5cfadb494c6722240b38c4cc0c90998f5d34149e7e56b349d2c36190a2

Observation 9868d956-170f-43ee-aab0-8d09d0c2db47 · outbound

This paper cites Deepseek-v3-0324, 2025.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Deepseek-v3-0324, 2025

Reference 3

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source=pdf_text observed=2026-08-07T15:19:14.035896Z digest=sha256:28d3dac72a09b15ae3ebeaa1369418584acc02f74e938ccc68c9c3ffd1078871

Observation b43578b4-2135-4856-8a50-d55246173fc4 · outbound

This paper cites A survey on rag meeting llms: Towards retrieval-augmented large language models.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation A survey on rag meeting llms: Towards retrieval-augmented large language models

Reference 4

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source=pdf_text observed=2026-08-07T15:19:14.093682Z digest=sha256:b3d5adc1a2d2ac4cc61a70a75b6add6ba2e5463ce181e4c451f0e0356b18949b

Observation 8f6676c9-7389-4d85-9668-2546d9f77d3a · outbound

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

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Retrieval-Augmented Generation for Large Language Models: A Survey

Reference 5

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source=pdf_text observed=2026-08-07T15:19:14.164968Z digest=sha256:359e01ece11bb61f34e1c9783eb68ce30b048a00e1477418a89cb7ea49f8cabc

Observation 4706f898-6e7a-4eae-8857-73483b5eb354 · outbound

This paper cites gemini-2.0-flash, 2025.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation gemini-2.0-flash, 2025

Reference 6

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source=pdf_text observed=2026-08-07T15:19:14.206620Z digest=sha256:f057732590b3e22bfe0fe4c81bd5ea6859e09523776d0fe74ed4e5178be576c2

Observation 101d6a70-f59a-4c91-bb86-e8fd9a0519e8 · outbound

This paper cites gemini-2.5-flash-preview, 2025.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation gemini-2.5-flash-preview, 2025

Reference 7

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source=pdf_text observed=2026-08-07T15:19:14.314908Z digest=sha256:850dc33f979b9e648af5724cdf72c4910c37dd2d0daa62132acbfc9e6798dd07

Observation b5b00800-f280-426a-a63b-c4f55a0cbfed · outbound

This paper cites gemini-2.5-pro-preview, 2025.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation gemini-2.5-pro-preview, 2025

Reference 8

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source=pdf_text observed=2026-08-07T15:19:14.425559Z digest=sha256:ad9c17808450ab8a126101ab7c531de2d539555c7649675e4cc54d0092715b0d

Observation f51360fe-390f-4c51-bde7-be8caf5d5fa8 · outbound

This paper cites Gemini deep research, 2025.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Gemini deep research, 2025

Reference 9

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source=pdf_text observed=2026-08-07T15:19:14.536700Z digest=sha256:b53bb9569cadc32d67dedd565b0c1811cb7ce7f38d3054df08f5a3181cf18f1f

Observation 5db5fc67-f636-4e5b-9eb4-955bb2a5706b · outbound

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

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 10

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source=pdf_text observed=2026-08-07T15:19:14.644607Z digest=sha256:c3ad9be404d647cc0a3b5db3658e3652a5bc6731ca1799e10832071e44cc6e38

Observation 1d16f51a-4c9a-4af9-86ab-eb3ea3078778 · outbound

This paper cites A survey on large language models: Applications, challenges, limitations, and practical usage.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation A survey on large language models: Applications, challenges, limitations, and practical usage

Reference 11

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source=pdf_text observed=2026-08-07T15:19:14.758836Z digest=sha256:bb91da05d13ebcd11d32b71292f823d49d579530f884bcd9c402d62fafd4f332

Observation 35e1c6ae-f86d-4ab0-add9-ce9c632b1960 · outbound

This paper cites Rethinking with Retrieval: Faithful Large Language Model Inference.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Rethinking with Retrieval: Faithful Large Language Model Inference

Reference 12

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source=pdf_text observed=2026-08-07T15:19:14.858856Z digest=sha256:f3d223c61a590b19acbbe87478078ef03bba25be0a7c3c1d1f0edd8b86ff23a2

Observation 0eee85ad-94c3-4cb4-99d2-2ce6b8a33120 · outbound

This paper cites MINTQA: A Multi-Hop Question Answering Benchmark for Evaluating LLMs on New and Tail Knowledge.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation MINTQA: A Multi-Hop Question Answering Benchmark for Evaluating LLMs on New and Tail Knowledge

Reference 13

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source=pdf_text observed=2026-08-07T15:19:14.992316Z digest=sha256:3f944d936d1288bddb08d031d8870a6ffc2559199942950e3e5cfa0d445a1bd9

Observation 678d7220-987e-4052-a1ee-be7aa0215040 · outbound

This paper cites PaSa: An LLM Agent for Comprehensive Academic Paper Search.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation PaSa: An LLM Agent for Comprehensive Academic Paper Search

Reference 14

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source=pdf_text observed=2026-08-07T15:19:15.118158Z digest=sha256:69990539b53cd21adf13930e7b4b82503cf70a1776438f2cbf14e3b84c96ed83

Observation b9d38361-37fb-49af-8833-aa69ee60a63a · outbound

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

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps

Reference 15

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source=pdf_text observed=2026-08-07T15:19:15.233813Z digest=sha256:df6110de49c33f5f88f2e955c5d210468ee1e770d6078bf494128fcbac9e94ae

Observation 5c461fc7-2b5c-45fe-a606-09e0ab635195 · outbound

This paper cites A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions

Reference 16

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source=pdf_text observed=2026-08-07T15:19:15.354087Z digest=sha256:3f564596e84b8d7187dbce281cbf1e4309b176d457cf5e8116a9e28605bd93af

Observation cfe4ff5d-e9c3-4acc-8fc3-ce40224ded2a · outbound

This paper cites GPT-4o System Card.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation GPT-4o System Card

Reference 17

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source=pdf_text observed=2026-08-07T15:19:15.478998Z digest=sha256:469ddca61ce147f30979afb1081c6f015057a459e3cab66d7e6990b896e6078a

Observation 2fd2e740-e35e-4528-8d5c-ea2ae7b7c190 · outbound

This paper cites TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension

Reference 18

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source=pdf_text observed=2026-08-07T15:19:15.636063Z digest=sha256:30b6cb109d1e9bc559b6397d45db985f04f8ed34c5966b4517b0a4638668bb09

Observation b0df4548-cec2-4f30-86d7-8b1bf083ba21 · outbound

This paper cites Chatgpt for good? on opportunities and challenges of large language models for education.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Chatgpt for good? on opportunities and challenges of large language models for education

Reference 19

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source=pdf_text observed=2026-08-07T15:19:15.768872Z digest=sha256:0189f30f76542b638ffd1fa86ae972affb3f96360283d24d38d168a91ccf8468

Observation a3e894fe-5af4-4630-bc8c-20a4770454f2 · outbound

This paper cites Natural questions: a benchmark for question answering research.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Natural questions: a benchmark for question answering research

Reference 20

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source=pdf_text observed=2026-08-07T15:19:15.936377Z digest=sha256:33f5debd7ae94b4f8b9af49c336940fc14dd632694e2ff9a60b642e2fb28dcae

Observation 103bf430-99cb-48df-b65c-3a1ac146e397 · outbound

This paper cites Agent-g: An agentic framework for graph retrieval augmented generation.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Agent-g: An agentic framework for graph retrieval augmented generation

Reference 21

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source=pdf_text observed=2026-08-07T15:19:15.993701Z digest=sha256:ce912b9bd87b687cfaa37e88cdcf4551dedea54583b4f2bd9c7685fad4b5f7ed

Observation 994cbbaa-1198-4e57-8847-ca0b0835e653 · outbound

This paper cites Retrieval-augmented generation for knowledge-intensive nlp tasks.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Retrieval-augmented generation for knowledge-intensive nlp tasks

Reference 22

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source=pdf_text observed=2026-08-07T15:19:16.066079Z digest=sha256:12918251fd320432b775bfc842066a9e7c5b8dd55cd6a9a1b8b0407a40638ba9

Observation 74593749-1e68-4435-a723-03b62d41b193 · outbound

This paper cites Clickprompt: Ctr models are strong prompt generators for adapting language models to ctr prediction.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Clickprompt: Ctr models are strong prompt generators for adapting language models to ctr prediction

Reference 23

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source=pdf_text observed=2026-08-07T15:19:16.182100Z digest=sha256:73b6b4f3e9045165894f7640d23bea42488b2e060b91943e94c3a42808ff86ef

Observation b6f7c60e-e3a4-4176-be2c-68248cf2d8bf · outbound

This paper cites Rella: Retrieval-enhanced large language models for lifelong sequential behavior comprehension in recommendation.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Rella: Retrieval-enhanced large language models for lifelong sequential behavior comprehension in recommendation

Reference 24

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source=pdf_text observed=2026-08-07T15:19:16.271297Z digest=sha256:9ac9aa9ae3148e343c0e184001e87a626984dcac884617c5dfe2d4a51c234c54

Observation f079e44d-f224-476d-bbb3-b374ca3055c7 · outbound

This paper cites How can recommender systems benefit from large language models: A survey.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation How can recommender systems benefit from large language models: A survey

Reference 25

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source=pdf_text observed=2026-08-07T15:19:16.368612Z digest=sha256:7eabb7fc47dfed9783fc99fc4846e3698e3d1fed99faa830470403cc25196cdb

Observation 582c4d37-436a-43cd-9da9-988fcbe77aab · outbound

This paper cites DeepSeek-V3 Technical Report.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation DeepSeek-V3 Technical Report

Reference 26

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source=pdf_text observed=2026-08-07T15:19:16.432667Z digest=sha256:e3936418b26381a252260a8c3fc4d820341a4e77a3b47e4a0133bec656e9641c

Observation 7c6eb347-2f37-4537-9335-642bbaf7b7dc · outbound

This paper cites CRUD-RAG: A Comprehensive Chinese Benchmark for Retrieval-Augmented Generation of Large Language Models.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation CRUD-RAG: A Comprehensive Chinese Benchmark for Retrieval-Augmented Generation of Large Language Models

Reference 27

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source=pdf_text observed=2026-08-07T15:19:16.517440Z digest=sha256:d7d2849545702b7403a7a28a535e14d39c67ae95bda6924698c1ff4c244728ef

Observation 5fd43ff2-07e0-4741-aad1-b07e843949d6 · outbound

This paper cites Knowledge injection to counter large language model (llm) hallucination.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Knowledge injection to counter large language model (llm) hallucination

Reference 28

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source=pdf_text observed=2026-08-07T15:19:16.547907Z digest=sha256:80b18ed6eeb284588f388edda02c0bd74c1f290c019ba9ef8197b73cdd46663b

Observation 0dbf52a1-7bc4-49bd-a30d-87caa729498c · outbound

This paper cites Llama 4 maverick, 2025.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Llama 4 maverick, 2025

Reference 29

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source=pdf_text observed=2026-08-07T15:19:16.624346Z digest=sha256:4f430f753ae42591c91f1cb1ad80f064edd2dfbd3c65b922019558f0330a1008

Observation 491c3951-d3cb-43c8-8067-774427415883 · outbound

This paper cites Ms marco: A human-generated machine reading comprehension dataset.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Ms marco: A human-generated machine reading comprehension dataset

Reference 30

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source=pdf_text observed=2026-08-07T15:19:16.684229Z digest=sha256:e6c950180a7bb0a0b407f3a80a27491065861bdf19534cab84419809ccf4b22f

Observation e0a6509e-404d-467e-be36-d1148bfd7bec · outbound

This paper cites Gpt-4o mini, 2025.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Gpt-4o mini, 2025

Reference 31

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source=pdf_text observed=2026-08-07T15:19:16.717551Z digest=sha256:1c9bacf3a62dfe6de5dd51dd15213079cf6cde96c8e6ab8b43be2ee8db592408

Observation c289c90b-5968-471e-ac0e-4c2f1a1b16f5 · outbound

This paper cites o3-mini, 2025.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation o3-mini, 2025

Reference 32

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source=pdf_text observed=2026-08-07T15:19:16.824918Z digest=sha256:f56a7f38ba2348579d20c9d391576f953e28f6d3834a7f3cb9318e8982afb5e9

Observation effa06d9-9fdd-46b4-88bf-887ff43aa2e1 · outbound

This paper cites Introducing deep research, 2025.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Introducing deep research, 2025

Reference 33

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source=pdf_text observed=2026-08-07T15:19:16.974719Z digest=sha256:f3db2888ea83115587928bdcf2e2dc3d8a0f9bfea1f5125bd8cfe1f0c6b82580

Observation 079932c0-a680-4903-8181-1290229def75 · outbound

This paper cites KwaiAgents: Generalized Information-seeking Agent System with Large Language Models.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation KwaiAgents: Generalized Information-seeking Agent System with Large Language Models

Reference 34

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source=pdf_text observed=2026-08-07T15:19:17.076854Z digest=sha256:42765bd7c7e0b1c979188b6f5c02fd7b1ed7b5f54b1ef1dfb20bacb8d842c8e6

Observation 1675159f-a359-4928-b17f-45b930d2eaeb · outbound

This paper cites LLM Evaluators Recognize and Favor Their Own Generations.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation LLM Evaluators Recognize and Favor Their Own Generations

Reference 35

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source=pdf_text observed=2026-08-07T15:19:17.178061Z digest=sha256:06f674096dd2e2513aacd06ad53bab9315cd52aabe1f86bc0a441dc40c2f4b0c

Observation a987e43d-d025-4bf4-ad30-9a7e03c63825 · outbound

This paper cites Ragnarök: A reusable rag framework and baselines for trec 2024 retrieval-augmented generation track.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Ragnarök: A reusable rag framework and baselines for trec 2024 retrieval-augmented generation track

Reference 36

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source=pdf_text observed=2026-08-07T15:19:17.319679Z digest=sha256:bdd52296f6982a4079c0493980eff02caacc1e59e251951ee5fc00ac2128adef

Observation a33c8004-94fd-435b-93d1-8f73b207f353 · outbound

This paper cites Agentic Retrieval-Augmented Generation for Time Series Analysis.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Agentic Retrieval-Augmented Generation for Time Series Analysis

Reference 37

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source=pdf_text observed=2026-08-07T15:19:17.428332Z digest=sha256:101fa025e6e355fdd7575939b9f95288a29d7cf0ee07397b24e035a06a8b4a5e

Observation d9cf9d45-6cbf-4147-8300-74fa0c85c8e5 · outbound

This paper cites Evaluating retrieval quality in retrieval-augmented gen- eration.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Evaluating retrieval quality in retrieval-augmented gen- eration

Reference 38

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source=pdf_text observed=2026-08-07T15:19:17.591234Z digest=sha256:18f374101c2fc01f27e78650dc4bb7a4dad592b459fd3b4ca0d326297ea0ce4b

Observation bfa664fc-b668-4447-8417-5877e53b794c · outbound

This paper cites CollEX -- A Multimodal Agentic RAG System Enabling Interactive Exploration of Scientific Collections.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation CollEX -- A Multimodal Agentic RAG System Enabling Interactive Exploration of Scientific Collections

Reference 39

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verified exact
local_arxiv, observed 2026-08-07T15:19:24.764456Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:19:17.704289Z digest=sha256:6371d8c50b0577c1ab027a8d35b092fc5e7d21749a129111c1bb73278c5d3e50

Observation 5295d18e-ca0b-44de-904f-f43c57f26969 · outbound

This paper cites In ChatGPT We Trust? Measuring and Characterizing the Reliability of ChatGPT.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation In ChatGPT We Trust? Measuring and Characterizing the Reliability of ChatGPT

Reference 40

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T15:19:17.865016Z digest=sha256:c1416f77875c1e7c32a840b3364bd250935034cd8a1714af9270c0d2ceb6532b

Observation ef530cfd-0f44-4ddc-90d4-96ea8f2aa83c · outbound

This paper cites Autogpt: Build, deploy, and run ai agents, 2025.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Autogpt: Build, deploy, and run ai agents, 2025

Reference 41

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T15:19:17.969329Z digest=sha256:78a633a4d8da7308b3821202ef8ca0bb5b291a54a88d8158ad391ccc32e8545d

Observation 57d79fee-85c1-4050-a218-5dfd31eb6d68 · outbound

This paper cites Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG

Reference 42

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T15:19:18.089092Z digest=sha256:b78719a439410a1fb47d3892220ddb10f5d9cf2999f7d1367af2fa6d91db4837

Observation 750f9791-c30a-489f-a82e-c579d1406a7a · outbound

This paper cites MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop Queries.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop Queries

Reference 43

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:19:18.170694Z digest=sha256:636ba6dc9bb9576c7234c923323310c1387b2d279af0de9dacbe757af153f41c

Observation 0cab2332-c1f8-4873-a11e-68b07907008e · outbound

This paper cites Introducing perplexity deep research, 2025.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Introducing perplexity deep research, 2025

Reference 44

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verified fuzzy
raw_fallback, observed 2026-08-07T15:19:31.056135Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:19:18.298213Z digest=sha256:97f7978b8fcec55c9fda1bf1b6037ef75be0c1f3d68d7ebe3e2e1a1efd8c43d1

Observation 00477aa1-964a-439e-9655-275347e4135b · outbound

This paper cites Qwen3 technical report, 2025.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Qwen3 technical report, 2025

Reference 45

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verified fuzzy
raw_fallback, observed 2026-08-07T15:19:30.979602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:19:18.425635Z digest=sha256:e81a4d55386d71beb635d0fb68587aa697a8b3a9865f6208a1691062d19b11e8

Observation b2c50445-44a1-4665-b13e-00f41ec56e4f · outbound

This paper cites Large language models in medicine.Nature medicine, 29(8):1930–1940, 2023.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Large language models in medicine.Nature medicine, 29(8):1930–1940, 2023

Reference 46

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T15:19:18.557239Z digest=sha256:70b4f7f8c6d4db2ac55768c3505083c4f61933639c0d8d98ce74ea7f8d0c7019

Observation f6af8607-9b95-40fb-8e67-0a44ec10c3e0 · outbound

This paper cites Musique: Multihop questions via single-hop question composition.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Musique: Multihop questions via single-hop question composition

Reference 47

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no resolver link, observed 2026-08-07T15:19:18.674661Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:19:18.674661Z digest=sha256:a7dedcea402f47c67a5f4457f5b215a26b07097abe9702c4ec24423a12a447e6

Observation bbbbe329-a461-41ea-a917-3a64ed83bf5a · outbound

This paper cites FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation

Reference 48

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no resolver link, observed 2026-08-07T15:19:18.807656Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:19:18.807656Z digest=sha256:2f7137b661cf8aca6dc08276941badb646577986c9bad685ac934759a0643956

Observation c9309b0f-2cf6-4248-910d-9818c3f76ad3 · outbound

This paper cites A survey on large language model based autonomous agents.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation A survey on large language model based autonomous agents

Reference 49

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T15:19:18.969967Z digest=sha256:df2b43f7eaf630e4ee06caff6595798d3dfd8732dc059515dcdde96ac66b3b15

Observation 9cbbabbb-20b8-4100-ba79-c5f56b859dc6 · outbound

This paper cites Emergent Abilities of Large Language Models.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Emergent Abilities of Large Language Models

Reference 50

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no resolver link, observed 2026-08-07T15:19:19.059875Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:19:19.059875Z digest=sha256:c90526f1c93484f5e9e8e12ce882427730eae93ac3d109a3ad28d7107f59177a

Observation 40e101cd-6d9b-4b0c-b069-2f46f3b24295 · outbound

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

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation BrowseComp: A Simple Yet Challenging Benchmark for Browsing Agents

Reference 51

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no resolver link, observed 2026-08-07T15:19:19.113302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:19:19.113302Z digest=sha256:14688aa778caa8c6e784ef6ddc222e45467b0c93b794a2d2a9922ce552cecadf

Observation 25861ee1-edf5-48db-bb22-57131a23e744 · outbound

This paper cites Retrieval-Augmented Generation for Natural Language Processing: A Survey.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Retrieval-Augmented Generation for Natural Language Processing: A Survey

Reference 52

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no resolver link, observed 2026-08-07T15:19:19.170878Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:19:19.170878Z digest=sha256:ceb80338849fed7e2a38b35fb85bcce6c73244f93c10b69442b0f9f4842b652b

Observation e09b9809-0ee9-4147-930f-08d344793a34 · outbound

This paper cites Towards open-world recommendation with knowledge augmentation from large language models.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Towards open-world recommendation with knowledge augmentation from large language models

Reference 53

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no resolver link, observed 2026-08-07T15:19:19.245250Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:19:19.245250Z digest=sha256:1793c4e1ff4dde62a1ed40b1b98efca07ae86bb045d57ee3d712d963c7ecb17e

Observation dce9cb97-04e8-45ab-a346-42310fc04ef5 · outbound

This paper cites Memocrs: Memory-enhanced sequential conversational recommender systems with large language models.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Memocrs: Memory-enhanced sequential conversational recommender systems with large language models

Reference 54

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no resolver link, observed 2026-08-07T15:19:19.312886Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T15:19:19.312886Z digest=sha256:1ea21d4e6e315963cc51c3f0bc9e3d69649c383518774650f755325a7d223c0f

Observation d2326f22-5198-4046-bafc-e7f8c1597f2b · outbound

This paper cites Efficiency Unleashed: Inference Acceleration for LLM-based Recommender Systems with Speculative Decoding.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Efficiency Unleashed: Inference Acceleration for LLM-based Recommender Systems with Speculative Decoding

Reference 55

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no resolver link, observed 2026-08-07T15:19:19.371243Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T15:19:19.371243Z digest=sha256:533311bf61bb5afec596c9bf1cd7649b6e7f285e6bc4344e128ce35fa6c6fac4

Observation 50ee4f9c-641c-451a-9ad9-6dfeda0bfdd3 · outbound

This paper cites Bursting Filter Bubble: Enhancing Serendipity Recommendations with Aligned Large Language Models.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Bursting Filter Bubble: Enhancing Serendipity Recommendations with Aligned Large Language Models

Reference 56

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no resolver link, observed 2026-08-07T15:19:19.432011Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:19:19.432011Z digest=sha256:110711f9adc42d410df5ef12926f98c12e0ca8e80d9551f4c7125771dd489852

Observation d4c12770-c843-43b4-aa6a-f04672e3c37e · outbound

This paper cites A Critical Evaluation of Evaluations for Long-form Question Answering.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation A Critical Evaluation of Evaluations for Long-form Question Answering

Reference 57

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no resolver link, observed 2026-08-07T15:19:19.493812Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T15:19:19.493812Z digest=sha256:48b61ab4badee25496960148a4010d08aceedbd7134a0c60d1d2951261ae9688

Observation 499a9a3c-1645-4800-8d80-a9e2ae47d583 · outbound

This paper cites Crag-comprehensive rag benchmark.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Crag-comprehensive rag benchmark

Reference 58

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no resolver link, observed 2026-08-07T15:19:19.565887Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:19:19.565887Z digest=sha256:bc93bacf72402d8c673c84498b9cad14b67487905081fef2e52a7a0f5f38b412

Observation ce27a73b-58e9-4190-9d9d-e7567d4b30c4 · outbound

This paper cites A Survey of AI Agent Protocols.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation A Survey of AI Agent Protocols

Reference 59

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no resolver link, observed 2026-08-07T15:19:19.630391Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:19:19.630391Z digest=sha256:cead9df7790e826386f4819d98087864d6b5f3cd9b5ad2d2b5bb25c48e7c4e68

Observation cada1bec-29ab-4d54-b7d7-5c92c4e519a6 · outbound

This paper cites Babyagi, 2025.https://github.com/yoheinakajima/babyagi, Accessed on 2025-05-06.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Babyagi, 2025.https://github.com/yoheinakajima/babyagi, Accessed on 2025-05-06

Reference 60

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raw_fallback, observed 2026-08-07T15:19:30.796053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:19:19.693477Z digest=sha256:1106837ed793579958f3ae816f17ad6913c5071eda6efe19733883fceb2287bb

Observation 690db4a3-5f38-45cd-a054-3304ac7de735 · outbound

This paper cites Agentic Information Retrieval.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Agentic Information Retrieval

Reference 61

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no resolver link, observed 2026-08-07T15:19:19.756781Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:19:19.756781Z digest=sha256:620f3a3d4fc3a723726aa883be41c8369598039c4bc28041bc5b7ea028a11a79

Observation fa7e5241-9967-4f2d-9a81-bf276e91f10a · outbound

This paper cites Retrieval-Augmented Generation for AI-Generated Content: A Survey.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Retrieval-Augmented Generation for AI-Generated Content: A Survey

Reference 62

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no resolver link, observed 2026-08-07T15:19:19.811003Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:19:19.811003Z digest=sha256:f8becebf96f1b9adf2644add13cfbfc317f81e6bd3b82ab2310f451e2badb160

Observation 343225e3-40e4-429a-9efd-9c875c4c839c · outbound

This paper cites A Survey of Large Language Models.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation A Survey of Large Language Models

Reference 63

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no resolver link, observed 2026-08-07T15:19:19.867289Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:19:19.867289Z digest=sha256:b8bf40f86b1410c6257942a841fe15ebe1905a9d66a70261927bfc16ad24ec48

Observation 33e6708e-c0e2-4061-af62-cb58750096a9 · outbound

This paper cites BrowseComp-ZH: Benchmarking Web Browsing Ability of Large Language Models in Chinese.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation BrowseComp-ZH: Benchmarking Web Browsing Ability of Large Language Models in Chinese

Reference 64

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no resolver link, observed 2026-08-07T15:19:19.934907Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:19:19.934907Z digest=sha256:db37e499facf8c452734c2a29c506fcef30f31c7d3aedeef2991bf46ba236d4f

Observation 55f5349f-f63b-4fea-8f88-990f623ba0b2 · outbound

This paper cites an unresolved cited work.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Unresolved cited work

Reference 67

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unresolved
raw_fallback, observed 2026-08-07T15:19:30.390161Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:19:20.136892Z digest=sha256:172064145bca7daad27fceafbc7223e7f727e0c5335d84e98375fb8e97cd2428

Observation 6538c969-60d4-475d-b631-99635af5ebf5 · outbound

This paper cites task_name.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation task_name

Reference 72

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:19:29.874877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:19:20.410372Z digest=sha256:2615146b5d157eedbe7ec730a3b9e9dce00a8b4976a3f1674c2e49f8cc3da99b

Observation b97fb32c-6848-4c9a-805f-23075e8cc68a · outbound

This paper cites an unresolved cited work.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Unresolved cited work

Reference 73

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unresolved
raw_fallback, observed 2026-08-07T15:19:29.764725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:19:20.447949Z digest=sha256:32622074c694f6accef651bd67ac3a1bc1dfec74613420b3b41cf63ec852c369

Observation a259c30f-e5f7-44ad-88a3-34491b833484 · outbound

This paper cites an unresolved cited work.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Unresolved cited work

Reference 74

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unresolved
raw_fallback, observed 2026-08-07T15:19:29.616185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:19:20.501635Z digest=sha256:6baf2d6bd7d1185e2dd3ad78573d285f79aa7abcff41b7d675f3b041c35188f5

Observation 4d6bfccb-63c1-4401-ac91-690ef5b142a4 · outbound

This paper cites If the question has multiple sub-questions, the relevant webpages of each sub-question must be included.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation If the question has multiple sub-questions, the relevant webpages of each sub-question must be included

Reference 75

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:19:29.486800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:19:20.565291Z digest=sha256:21e8c33379566ce8b27b1b3fb5a9881ff10c43c8d61d9a410bf50488ef26a91b

Observation 7d28e0f6-88b8-4ec8-95ba-755c103a1ecf · outbound

This paper cites If it is more than {max_webpage_num}, select {max_webpage_num} of the most important ones.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation If it is more than {max_webpage_num}, select {max_webpage_num} of the most important ones

Reference 76

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:19:29.375333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:19:20.636361Z digest=sha256:b7af2e091898d23379e27f6dc73534fbdb5ade80f19f92c004fd4dc5c4a0dc0f

Observation 661593f3-c32e-485d-b4d2-67b3599b3494 · outbound

This paper cites url": "The webpage's URL.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation url": "The webpage's URL

Reference 77

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:19:29.259442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:19:20.696792Z digest=sha256:6ad701de5b94bc041b5c6a76a960ed92ada21df5edb35330064034ea8e1a943f

Observation b668ac36-ca70-4523-a2f8-ac9452eee593 · outbound

This paper cites an unresolved cited work.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Unresolved cited work

Reference 78

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unresolved
raw_fallback, observed 2026-08-07T15:19:29.138347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:19:20.766715Z digest=sha256:87bb6b5333e84cadddc239800d090a3bc48efdf38b1589cfd1f1bd4bf5d2b425

Observation c6b5d546-fc6a-4023-b250-c8f8d56e10d0 · outbound

This paper cites an unresolved cited work.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Unresolved cited work

Reference 79

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:19:29.020987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:19:20.833307Z digest=sha256:b7a9d13eb31e9bd1b49a9a6ec49a3102c787e06390da3ec268a6bd93bcf48dc6

Observation 96395ced-66a3-4a2a-9665-ad874417c927 · outbound

This paper cites advantage languages.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation advantage languages

Reference 80

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:19:28.915943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:19:20.903671Z digest=sha256:6919eb7dc9789fe0180f4245b9f944177eb5d8a11c283ea14e91edfc811f05c7

Observation a6b8fe08-1776-46f5-81be-6d22dfd2d4b2 · outbound

This paper cites an unresolved cited work.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Unresolved cited work

Reference 81

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:19:28.783645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:19:20.974635Z digest=sha256:7da01e125bf40027156ac01b99a9234f19e91641706025f6afda177027be99e9

Observation f6091001-1e5c-4e9b-8595-3118200d8db7 · outbound

This paper cites Who is the current president of the USA?.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Who is the current president of the USA?

Reference 82

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:19:28.674794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:19:21.021882Z digest=sha256:6727fb5740bb65a509dc58370bd3d4b3c11d390367301aabe80cba9166a6f76e

Observation a1a8b4e0-c7fb-4bb5-a506-a1de2d848c08 · outbound

This paper cites This question contains a false premise:.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation This question contains a false premise:

Reference 83

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:19:28.564714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:19:21.083089Z digest=sha256:1f0894c3cc51cbb8e96b7b5045bb1667af43e59d337d280d1a42c4085e77211a

Observation ac7be271-c1fb-43aa-92f5-3c04bf17c235 · outbound

This paper cites an unresolved cited work.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Unresolved cited work

Reference 84

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:19:28.456963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:19:21.159894Z digest=sha256:590e92c8664bb7694794f9d55636586366eca8d188abeba5cf7db757adef5493

Observation a57235cf-9215-49e3-b677-91c81ed25bcd · outbound

This paper cites an unresolved cited work.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Unresolved cited work

Reference 85

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:19:28.368169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:19:21.226249Z digest=sha256:7d5ce5880d76aed6c0819141497708902d27594f5f184a03edc4b203a0023199

Observation 020365ce-c09e-479b-80dd-657d98575c25 · outbound

This paper cites an unresolved cited work.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Unresolved cited work

Reference 86

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:19:28.280173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:19:21.300657Z digest=sha256:d6c169e037114e54c8d47ddbfdb2186e1d7fc66bb4a6036cc83decf5e0cc6cd8

Observation 79cc6289-6f33-4779-ac0d-9cf932ca483e · outbound

This paper cites a president who is a comedian.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation a president who is a comedian

Reference 87

Resolution
verified exact
raw_fallback, observed 2026-08-07T15:19:24.356196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:19:21.437628Z digest=sha256:4fbe07003ae7b09ba4a94ce993348f3a6993df011a992fb0f9d24f895e53911d

Observation 8e1e3ed3-2d61-49ed-a81d-3b6ab4feabc6 · outbound

This paper cites an unresolved cited work.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Unresolved cited work

Reference 88

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:19:28.124265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:19:21.602108Z digest=sha256:569ea9d1ff2355b51e12563c0b272e7486a3efa7bbba2b6802a898da99f95c76

Observation 99ecdb49-f994-47d5-ac70-4821bec4322d · outbound

This paper cites an unresolved cited work.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Unresolved cited work

Reference 89

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:19:27.944082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:19:21.725635Z digest=sha256:335a6808445bbe5711e4cd72a0709bcea6ef3d98353a980d29b55b2157bf74eb

Observation 211cd592-5ea7-4ecd-8932-6fc6b7d17a62 · outbound

This paper cites an unresolved cited work.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Unresolved cited work

Reference 90

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:19:27.829802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:19:21.848072Z digest=sha256:46c116e6ef564fd8d983c459d0c3df1911dc3dfba2b39b83dd4d0c99ce065b98

Observation eb848811-7be8-45c3-82bb-d2a2694a2a78 · outbound

This paper cites If the candidate answer says that the question has a wrong premise„ such as person/event may not exist, it is a wrong answer.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation If the candidate answer says that the question has a wrong premise„ such as person/event may not exist, it is a wrong answer

Reference 91

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:19:27.641294Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:19:21.976370Z digest=sha256:014d4c70f91b1f0927bb3cc20782d6c78ef3cfd4b97c64c4f99f4131e0586534

Observation e10ccb42-9ab0-4096-a8e7-9916f9c1000d · outbound

This paper cites This question contains a false premise:.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation This question contains a false premise:

Reference 92

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:19:27.426565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:19:22.102150Z digest=sha256:7948ebaefaf03a4aa9e088b99fd60c32a16348e3b9e46f0d31f9877d36341977

Observation f295c3e6-d53d-4e1e-9b2f-f300ced42dda · outbound

This paper cites If the candidate answer does not point out or correct this false premise, it is incorrect.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation If the candidate answer does not point out or correct this false premise, it is incorrect

Reference 93

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:19:27.179647Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:19:22.234924Z digest=sha256:dc8df95285bf4b2c664dccaba843e37fb5447b2b2506185bc57b670102163668

Observation c2f1d933-2184-4079-a7ad-f1065d673467 · outbound

This paper cites an unresolved cited work.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Unresolved cited work

Reference 94

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:19:26.982175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:19:22.372627Z digest=sha256:ae832bfc520aab80b2a87311b7b150b4da1d6d5026b5842ca7e2d02b83c4d2ad

Observation ba3f5bbd-0acd-4c84-b193-9f8ccb70f510 · outbound

This paper cites an unresolved cited work.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Unresolved cited work

Reference 95

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:19:26.875005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:19:22.449679Z digest=sha256:bcea6a909dfa574670f0733f834ecebb48612555c9c1c395e8bf74e69460d193

Observation 108963b9-8d56-4161-89f0-6261ddabd3be · outbound

This paper cites an unresolved cited work.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Unresolved cited work

Reference 96

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:19:26.705143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:19:22.539108Z digest=sha256:51c55d9571195394580d93cc6f774fb8f28ffb048a3dabd2559e33faab797615

Observation 42d2be57-db8c-485c-b675-934bbb484ca1 · outbound

This paper cites an unresolved cited work.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Unresolved cited work

Reference 97

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:19:26.568136Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:19:22.630382Z digest=sha256:2275e1d8d9314928dc6fe1b8fd05d20f520440ae3600b273acc275945ba91f2e

Observation 2ed94714-a8f0-42b8-adf8-b4b0dd49cffb · outbound

This paper cites If the 25 candidate answer proposes a wrong premise or cannot determine whether the person/event exists, it is a wrong answer.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation If the 25 candidate answer proposes a wrong premise or cannot determine whether the person/event exists, it is a wrong answer

Reference 98

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:19:26.456032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:19:22.717294Z digest=sha256:7dbb25d35464ace8097fac87deb494d94fb9be943dc4551057f3efa75d77b7d1

Observation 304ff2c7-69db-4528-97bc-a58b2dc662f3 · outbound

This paper cites If the candidate answer does not answer the question correctly but proposes the need to further query relevant information, it is a wrong answer.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation If the candidate answer does not answer the question correctly but proposes the need to further query relevant information, it is a wrong answer

Reference 99

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:19:26.317244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:19:22.836487Z digest=sha256:234afb0e0d4797c74c495f8954ebd60c528c39a8342208a4689f63e1ccfcc3ac

Observation 1fd61b0c-1828-44eb-9fd9-2bd293948a37 · outbound

This paper cites an unresolved cited work.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Unresolved cited work

Reference 100

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:19:26.150261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:19:22.905154Z digest=sha256:ecd0af40ae93fdb352b556ab81b4ae35c01c6e77e499be9c1bebb0c336797b60

Observation 3d1d024e-82ef-450a-aee9-0be83f84d833 · outbound

This paper cites an unresolved cited work.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Unresolved cited work

Reference 101

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:19:25.934347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:19:22.993023Z digest=sha256:2a4dbe4bf8fd302f23617bf7d6bf45c6b5b1dbf70b895b897a6ad2089077695a

Observation 93c86005-2d1f-4e9a-8f1e-e7c2b5d1e171 · outbound

This paper cites an unresolved cited work.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Unresolved cited work

Reference 102

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:19:25.787588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:19:23.095041Z digest=sha256:fda6b45da7e8735e55041c341caae2b7aa89d467e034126cf73d43292de8c543

Observation 3af93312-3691-47c6-8cd3-01619c8a202f · outbound

This paper cites Each query also consumes about 24k input tokens and produces roughly 4k output tokens.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Each query also consumes about 24k input tokens and produces roughly 4k output tokens

Reference 103

Resolution
malformed identifier
raw_fallback, observed 2026-08-07T15:19:25.662352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:19:23.210174Z digest=sha256:cedd58fbf736056343c3b50c8346dbf58a7e6df1bdd28d5a89ddbeea8d1bc736

Observation bc9b289b-d626-4106-a137-99a79facaba1 · outbound

This paper cites an unresolved cited work.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Unresolved cited work

Reference 104

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:19:30.661659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:19:23.327446Z digest=sha256:2cc391598d8f1af99002136f3bc530ebf82a7cce076e4cbcf99cb1ae2d8a9676

Observation 716aec19-b0a2-4e86-a58d-60cccd8ef996 · outbound

This paper cites an unresolved cited work.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Unresolved cited work

Reference 105

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:19:30.530379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:19:23.458577Z digest=sha256:622a65136daec5d3a60d19f45c07fb02d218a9401feabf28f194563ae537a437

Observation 07cf1b17-89b6-452a-8cb9-865d3de5e633 · outbound

This paper cites 31 Table 17: Retrieval interference under different languages, measured by %.

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation 31 Table 17: Retrieval interference under different languages, measured by %

Reference 106

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:19:25.483022Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:19:23.537787Z digest=sha256:4deedb3a381e14204aac5bd33612cfc87762407cbb1bc7016ae72f8933e31b25

Pith citing papers

Observation d36943e7-71c7-4fe0-a2b7-a58138345389 · inbound

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

A Vision for Geo-Temporal Deep Research Systems: Towards Comprehensive, Transparent, and Reproducible Geo-Temporal Information Synthesis InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation

Reference 56

Resolution
unresolved
no resolver link, observed 2026-08-07T00:22:23.654894Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:22:23.654894Z digest=sha256:6da207b8bd25a166e7822ed00dbf547b429f2e6d9150a1a336ef19d2ab900e73

Observation 633dbf49-04a8-4d26-b05b-cfb5832a819f · inbound

Search-Time Data Contamination cites this paper.

Search-Time Data Contamination InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-05T21:08:06.259236Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T21:08:06.259236Z digest=sha256:3d11fcbd3cad3dde914d58335957cb588cb213f8c7e1086834f5bcf9bc976594

Observation a08b2747-bd47-44da-b08d-8e6acec2a86e · inbound

LocalSearchBench: Benchmarking Agentic Search in Real-World Local Life Services cites this paper.

LocalSearchBench: Benchmarking Agentic Search in Real-World Local Life Services InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-03T18:00:21.476899Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T18:00:21.476899Z digest=sha256:9e5e180e83908c929c372be2561c720cdd94cd3fd6321c933c8a650b24716c3e

Observation 566409b6-47be-4d1c-97ed-9ee14306cbdd · inbound

Toward Agentic RAG for Ukrainian cites this paper.

Toward Agentic RAG for Ukrainian InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-05-10T11:25:18.441619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-10T11:24:17.860095Z digest=sha256:e9cf391b398fa57a0821ab4e08c06b536e592f1cb5372bc71c23dd0002eba926

Observation f02dca05-5bcf-4999-b883-b95929da1eb5 · inbound

Modular Representation Compression: Adapting LLMs for Efficient and Effective Recommendations cites this paper.

Modular Representation Compression: Adapting LLMs for Efficient and Effective Recommendations InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation

Reference 64

Resolution
verified exact
arxiv_id, observed 2026-05-11T12:06:04.691242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-10T04:09:10.125285Z digest=sha256:c8ca704bc61071d692e19d45635cefd2544893d005934ada5cd68f19e73e09f6

Observation a7bfa4f0-c122-4597-adb6-7271404b1426 · inbound

AutoResearchBench: Benchmarking AI Agents on Complex Scientific Literature Discovery cites this paper.

AutoResearchBench: Benchmarking AI Agents on Complex Scientific Literature Discovery InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation

Reference 48

Resolution
verified exact
arxiv_id, observed 2026-05-11T23:36:33.156215Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-07T16:38:40.938722Z digest=sha256:2bb8cc64c3ae5a4f9e2a82a6620e3dba7ed8657fafc7e2be1a4931de96e8c573

Observation c2a4ebbc-21a1-4410-a417-49b5807141a7 · inbound

Toward Generalist Autonomous Research via Hypothesis-Tree Refinement cites this paper.

Toward Generalist Autonomous Research via Hypothesis-Tree Refinement InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation

Reference 9

Resolution
verified exact
arxiv_id, observed 2026-06-27T09:40:47.104895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-06-27T09:34:41.800309Z digest=sha256:aa40ee3ab413e8d63cf6400ef6ecb916c4a2ea23272a00de5121629e3e2d7c59

Observation 71eb74e4-2072-4771-a37e-92333b5d68f4 · 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 InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation

Reference 67

Resolution
verified exact
arxiv_id, observed 2026-06-27T09:50:48.362454Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-06-27T09:46:30.702256Z digest=sha256:ca547c347a8932f7d7656704054b2fb3f2616c589590ae089b82e4d155909a20

Observation 008b51d7-21a8-4314-9bd4-d76f5f4991b3 · 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 InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation

Reference 116

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