Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T15:19:23.537787Z
Paper Citation Record · LEDGER
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.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T15:19:23.537787Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T00:22:23.654894Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z
100 of 106 outbound references displayed
External citation measurements
0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Observation 267f1866-ee30-47f4-a9e4-7479e897a770 · outbound
InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Claude 3.7 sonnet, 2025
Reference 1
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Observation fd6d2c75-f7b2-4483-be7b-083fc2594580 · outbound
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Observation 9868d956-170f-43ee-aab0-8d09d0c2db47 · outbound
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Observation b43578b4-2135-4856-8a50-d55246173fc4 · outbound
InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation A survey on rag meeting llms: Towards retrieval-augmented large language models
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Observation 8f6676c9-7389-4d85-9668-2546d9f77d3a · outbound
InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Retrieval-Augmented Generation for Large Language Models: A Survey
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Observation 4706f898-6e7a-4eae-8857-73483b5eb354 · outbound
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Observation 101d6a70-f59a-4c91-bb86-e8fd9a0519e8 · outbound
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Observation b5b00800-f280-426a-a63b-c4f55a0cbfed · outbound
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Observation 5db5fc67-f636-4e5b-9eb4-955bb2a5706b · outbound
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Observation 1d16f51a-4c9a-4af9-86ab-eb3ea3078778 · outbound
InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation A survey on large language models: Applications, challenges, limitations, and practical usage
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Observation 35e1c6ae-f86d-4ab0-add9-ce9c632b1960 · outbound
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Observation 0eee85ad-94c3-4cb4-99d2-2ce6b8a33120 · outbound
InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation MINTQA: A Multi-Hop Question Answering Benchmark for Evaluating LLMs on New and Tail Knowledge
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InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation PaSa: An LLM Agent for Comprehensive Academic Paper Search
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Observation b9d38361-37fb-49af-8833-aa69ee60a63a · outbound
InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps
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Observation 5c461fc7-2b5c-45fe-a606-09e0ab635195 · outbound
InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions
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Observation cfe4ff5d-e9c3-4acc-8fc3-ce40224ded2a · outbound
InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation GPT-4o System Card
Reference 17
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Observation 2fd2e740-e35e-4528-8d5c-ea2ae7b7c190 · outbound
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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Observation b0df4548-cec2-4f30-86d7-8b1bf083ba21 · outbound
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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Observation a3e894fe-5af4-4630-bc8c-20a4770454f2 · outbound
InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Natural questions: a benchmark for question answering research
Reference 20
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Observation 103bf430-99cb-48df-b65c-3a1ac146e397 · outbound
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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Observation 994cbbaa-1198-4e57-8847-ca0b0835e653 · outbound
InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Retrieval-augmented generation for knowledge-intensive nlp tasks
Reference 22
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Observation 74593749-1e68-4435-a723-03b62d41b193 · outbound
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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Observation b6f7c60e-e3a4-4176-be2c-68248cf2d8bf · outbound
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Observation f079e44d-f224-476d-bbb3-b374ca3055c7 · outbound
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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Observation 582c4d37-436a-43cd-9da9-988fcbe77aab · outbound
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Observation 7c6eb347-2f37-4537-9335-642bbaf7b7dc · outbound
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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Observation 5fd43ff2-07e0-4741-aad1-b07e843949d6 · outbound
InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Knowledge injection to counter large language model (llm) hallucination
Reference 28
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Observation 0dbf52a1-7bc4-49bd-a30d-87caa729498c · outbound
InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Llama 4 maverick, 2025
Reference 29
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Observation 491c3951-d3cb-43c8-8067-774427415883 · outbound
InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Ms marco: A human-generated machine reading comprehension dataset
Reference 30
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Observation e0a6509e-404d-467e-be36-d1148bfd7bec · outbound
InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Gpt-4o mini, 2025
Reference 31
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Observation c289c90b-5968-471e-ac0e-4c2f1a1b16f5 · outbound
InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation o3-mini, 2025
Reference 32
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Observation effa06d9-9fdd-46b4-88bf-887ff43aa2e1 · outbound
InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Introducing deep research, 2025
Reference 33
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Observation 079932c0-a680-4903-8181-1290229def75 · outbound
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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Observation 1675159f-a359-4928-b17f-45b930d2eaeb · outbound
InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation LLM Evaluators Recognize and Favor Their Own Generations
Reference 35
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Observation a987e43d-d025-4bf4-ad30-9a7e03c63825 · outbound
InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Ragnarök: A reusable rag framework and baselines for trec 2024 retrieval-augmented generation track
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Observation a33c8004-94fd-435b-93d1-8f73b207f353 · outbound
InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Agentic Retrieval-Augmented Generation for Time Series Analysis
Reference 37
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Observation d9cf9d45-6cbf-4147-8300-74fa0c85c8e5 · outbound
InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Evaluating retrieval quality in retrieval-augmented gen- eration
Reference 38
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Observation bfa664fc-b668-4447-8417-5877e53b794c · outbound
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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Observation 5295d18e-ca0b-44de-904f-f43c57f26969 · outbound
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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Observation ef530cfd-0f44-4ddc-90d4-96ea8f2aa83c · outbound
InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Autogpt: Build, deploy, and run ai agents, 2025
Reference 41
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Observation 57d79fee-85c1-4050-a218-5dfd31eb6d68 · outbound
InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG
Reference 42
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Observation 750f9791-c30a-489f-a82e-c579d1406a7a · outbound
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Reference 43
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Observation 0cab2332-c1f8-4873-a11e-68b07907008e · outbound
InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Introducing perplexity deep research, 2025
Reference 44
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Reference 45
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Observation b2c50445-44a1-4665-b13e-00f41ec56e4f · outbound
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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InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Musique: Multihop questions via single-hop question composition
Reference 47
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InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation
Reference 48
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Observation c9309b0f-2cf6-4248-910d-9818c3f76ad3 · outbound
InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation A survey on large language model based autonomous agents
Reference 49
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InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Emergent Abilities of Large Language Models
Reference 50
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Observation 40e101cd-6d9b-4b0c-b069-2f46f3b24295 · outbound
InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation BrowseComp: A Simple Yet Challenging Benchmark for Browsing Agents
Reference 51
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InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Retrieval-Augmented Generation for Natural Language Processing: A Survey
Reference 52
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Reference 53
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Reference 54
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Reference 55
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Observation 50ee4f9c-641c-451a-9ad9-6dfeda0bfdd3 · outbound
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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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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Observation 499a9a3c-1645-4800-8d80-a9e2ae47d583 · outbound
InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Crag-comprehensive rag benchmark
Reference 58
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InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation A Survey of AI Agent Protocols
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Reference 60
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InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Agentic Information Retrieval
Reference 61
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InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation A Survey of Large Language Models
Reference 63
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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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Reference 67
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InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation task_name
Reference 72
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Reference 73
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Reference 74
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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
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Reference 76
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Reference 77
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Reference 78
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Observation c6b5d546-fc6a-4023-b250-c8f8d56e10d0 · outbound
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Reference 79
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Observation 96395ced-66a3-4a2a-9665-ad874417c927 · outbound
InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation advantage languages
Reference 80
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Observation a6b8fe08-1776-46f5-81be-6d22dfd2d4b2 · outbound
InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Unresolved cited work
Reference 81
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Observation f6091001-1e5c-4e9b-8595-3118200d8db7 · outbound
InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Who is the current president of the USA?
Reference 82
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Observation a1a8b4e0-c7fb-4bb5-a506-a1de2d848c08 · outbound
InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation This question contains a false premise:
Reference 83
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Observation ac7be271-c1fb-43aa-92f5-3c04bf17c235 · outbound
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Reference 84
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Observation a57235cf-9215-49e3-b677-91c81ed25bcd · outbound
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Reference 85
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Observation 020365ce-c09e-479b-80dd-657d98575c25 · outbound
InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Unresolved cited work
Reference 86
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Observation 79cc6289-6f33-4779-ac0d-9cf932ca483e · outbound
InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation a president who is a comedian
Reference 87
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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
Source-reported events for the cited work
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Reference 92
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Source-reported events for the cited work
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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
Source-reported events for the cited work
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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
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Reference 100
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Reference 101
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Reference 102
Source-reported events for the cited work
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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
Source-reported events for the cited work
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Reference 104
Source-reported events for the cited work
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InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Unresolved cited work
Reference 105
Source-reported events for the cited work
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Reference 106
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Reference 64
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Reference 48
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Reference 67
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Reference 116
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