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Paper Citation Record · LEDGER

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering

As of 21 August 2026, this Paper Citation Record lists 100 of 111 outbound references and 10 inbound Pith citation observations for arXiv:2505.16582.

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

pith.paper-citation-record.v1
2505.16582 v2

Coverage vector

measured 100 of 111 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:01:57.448378Z

measured 110 of 110 standing notices

One-hop event checks from named stored sources.

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

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T15:07:59.132190Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T12:53:26.512979Z

Reference resolution

100 of 111 outbound references displayed

  • verified exact1
  • verified fuzzy6
  • unresolved93
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 847a1c54-3737-4fa3-a66e-b5de5a9feefe · outbound

This paper cites Open Deep Search: Democratizing Search with Open-source Reasoning Agents.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Open Deep Search: Democratizing Search with Open-source Reasoning Agents

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:01:47.195572Z digest=sha256:7433f719155e8eeebe765228bd240d491ddd2ec9091cb32ba7ec7e2c394f84f4

Observation 8ba48282-e552-4bfb-a28e-365a4b41ba1b · outbound

This paper cites Self-rag: Learning to retrieve, generate, and critique through self-reflection.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Self-rag: Learning to retrieve, generate, and critique through self-reflection

Reference 2

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T15:01:47.242590Z digest=sha256:335eb7c17b88c601697f0ce539d28491003bdab0573ee35a24eec74eecd836d7

Observation fcb008d6-51fa-4e1f-a13f-b035257be31b · outbound

This paper cites Scaling instruction-finetuned language models.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Scaling instruction-finetuned language models

Reference 3

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source=pdf_text observed=2026-08-07T15:01:47.307736Z digest=sha256:f002f060e84329afb8a640c24ea0bb07dbc2e58143af0833a66b61fec8fc6cbc

Observation ff85960c-cd99-4f2f-b1a4-b209315cfd3f · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Training Verifiers to Solve Math Word Problems

Reference 4

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:01:47.367232Z digest=sha256:6d9ea6c8d65bb569ea038e1a719838742fb75342a4e9548e48a942c7f32473f3

Observation b6f8af41-810c-4400-93f2-b0721fb8dd03 · outbound

This paper cites Process Reinforcement through Implicit Rewards.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Process Reinforcement through Implicit Rewards

Reference 5

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source=pdf_text observed=2026-08-07T15:01:47.477105Z digest=sha256:288db7864fa7681318c929afcd3f160570fa153395fe93674198ffc448136b63

Observation 81c929cc-f245-4362-a33c-4ae4bb8766fd · outbound

This paper cites Plan-and-Act: Improving Planning of Agents for Long-Horizon Tasks.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Plan-and-Act: Improving Planning of Agents for Long-Horizon Tasks

Reference 6

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source=pdf_text observed=2026-08-07T15:01:47.550560Z digest=sha256:00fc0f3f2b783d5139d03909fc97e3b31d6a11aef669b6792e82099905a0c874

Observation 611c1b96-836d-44db-b049-50ca3d94d462 · outbound

This paper cites Trustgeogen: Scalable and formal-verified data engine for trustworthy multi-modal geometric problem solving.arXiv preprint arXiv:2504.15780, 2025.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Trustgeogen: Scalable and formal-verified data engine for trustworthy multi-modal geometric problem solving.arXiv preprint arXiv:2504.15780, 2025

Reference 7

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source=pdf_text observed=2026-08-07T15:01:47.648803Z digest=sha256:1f080b702068994f0027c48aea0548fbfbadff49227051fa866953f45432af42

Observation fcd707f8-e676-4686-9bb4-d6a9124266a5 · outbound

This paper cites Gemini deep research, 12 2024.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Gemini deep research, 12 2024

Reference 8

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T15:01:47.729516Z digest=sha256:61c0cf555fe7cff8b35d73b41403b08b7afe302d1c0e3c9083a9d27aa9fd413f

Observation 3e22bf9b-4a03-4e83-b702-465daa1c6931 · outbound

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

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 9

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source=pdf_text observed=2026-08-07T15:01:47.778287Z digest=sha256:112fc029b7a53ef27c1c979cf6a0b35a02ae2b6c6238014f1479dbfd6a36e1b9

Observation 215e9b38-a1b0-4bc7-9897-9dd826d031a4 · outbound

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

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps

Reference 10

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source=pdf_text observed=2026-08-07T15:01:47.840206Z digest=sha256:1f66e8089488e09bc1cbdda12bf9c401bf8c7258f0429ae47df1eedb7a63765c

Observation 091d1d25-1cfe-45d9-bb4b-b1a114b9610a · outbound

This paper cites MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework

Reference 11

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source=pdf_text observed=2026-08-07T15:01:47.901985Z digest=sha256:5344396ca8a7adb93136422ed42104f5ec194225c48898a2dca40ff1d976bf50

Observation 402cca18-7e9a-4141-919a-75d7735f7b4d · outbound

This paper cites Tree-Planner: Efficient Close-loop Task Planning with Large Language Models.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Tree-Planner: Efficient Close-loop Task Planning with Large Language Models

Reference 12

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source=pdf_text observed=2026-08-07T15:01:47.987757Z digest=sha256:df5a3280c515c87feace13df05329e11240df1a35ffe479efb82f4b6c0a2c32a

Observation 30975c1a-0dd9-4284-9292-78b8f6e5bfbb · outbound

This paper cites A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions.ACM Transactions on Information Systems, 43(2):1– 55, 2025.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions.ACM Transactions on Information Systems, 43(2):1– 55, 2025

Reference 13

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source=pdf_text observed=2026-08-07T15:01:48.094720Z digest=sha256:6809c4530000620521330770e14852b343731d049aa72ed2f8fc268e480562fd

Observation ee9623c0-c286-452b-b653-c596b2ab5b07 · outbound

This paper cites GPT-4o System Card.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering GPT-4o System Card

Reference 14

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source=pdf_text observed=2026-08-07T15:01:48.219960Z digest=sha256:b9b07f78993068edd166e6c8316aeca69dd7603644274db1b2819ce8a78d2172

Observation 778d78e5-c897-4141-8747-e552d25c98e1 · outbound

This paper cites Forward-Backward Reasoning in Large Language Models for Mathematical Verification.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Forward-Backward Reasoning in Large Language Models for Mathematical Verification

Reference 15

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source=pdf_text observed=2026-08-07T15:01:48.297471Z digest=sha256:a03474723f66b1d67f1253a12a69a730a903478676927ea1c3560fadd811b9d6

Observation cc0ce0bd-8652-41a5-a22b-9522ea2343f6 · outbound

This paper cites SWE-bench: Can Language Models Resolve Real-World GitHub Issues?.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering SWE-bench: Can Language Models Resolve Real-World GitHub Issues?

Reference 16

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source=pdf_text observed=2026-08-07T15:01:48.380961Z digest=sha256:8d6af19d42e764714714177f47b3bb4b06b344f6e577ebc9cd5c08b077de1e8f

Observation 43f068dc-332d-473c-8bfc-37d6d4caaa07 · outbound

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

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning

Reference 17

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source=pdf_text observed=2026-08-07T15:01:48.462784Z digest=sha256:3ccf4f5170ab6b110c4247d0f92b8f4143f6cc11c9261a0b3197c7676896331c

Observation 0e74f23f-ece1-4f61-8bbf-a13b8a87a01a · outbound

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

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension

Reference 18

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source=pdf_text observed=2026-08-07T15:01:48.529406Z digest=sha256:c86a62e444d24936dd32af88134b74e208680963b658a44ce5b795ab293eb09f

Observation 9c5cb148-d9b7-434d-a74f-7f6fe8c99e51 · outbound

This paper cites Reinforcement learning: A survey.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Reinforcement learning: A survey

Reference 19

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source=pdf_text observed=2026-08-07T15:01:48.624021Z digest=sha256:c955aff66e30fa99fbc171c88b06465e7365adfee21f804a9fca7978a72a2509

Observation 1721a036-de9d-4a52-b495-ccef6511a74d · outbound

This paper cites Dense passage retrieval for open-domain question answering.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Dense passage retrieval for open-domain question answering

Reference 20

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source=pdf_text observed=2026-08-07T15:01:48.710088Z digest=sha256:dcfc661dc1977e12cf133774008c5d3aeb57f46c92b295211df3f8cf8eee944f

Observation c813b869-2e47-46b7-aae0-eb56fdaa6954 · outbound

This paper cites UniKnow: A Unified Framework for Reliable Language Model Behavior across Parametric and External Knowledge.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering UniKnow: A Unified Framework for Reliable Language Model Behavior across Parametric and External Knowledge

Reference 21

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local_arxiv, observed 2026-08-07T15:01:59.854455Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T15:01:48.786022Z digest=sha256:b5ff684c4eefcfbecf6952ca7f54f3d7a52ce54bb61625e491c3b8ba2de90687

Observation 594f7765-5216-41ab-b530-fe1519d8bec8 · outbound

This paper cites The hungarian method for the assignment problem.Naval research logistics quarterly, 2(1-2):83–97, 1955.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering The hungarian method for the assignment problem.Naval research logistics quarterly, 2(1-2):83–97, 1955

Reference 22

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source=pdf_text observed=2026-08-07T15:01:48.869272Z digest=sha256:497dbc86e2e2823237f50379b5fbb2ab0a1a50b6a2eba10711b8ea12792d64cd

Observation 034bbc83-e669-465f-9e23-4def2e38040a · outbound

This paper cites Natural questions: a benchmark for question answering research.Transactions of the Association for Computational Linguistics, 7:453–466, 2019.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Natural questions: a benchmark for question answering research.Transactions of the Association for Computational Linguistics, 7:453–466, 2019

Reference 23

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source=pdf_text observed=2026-08-07T15:01:48.945339Z digest=sha256:b177572b3c238cbbb26e144452429fcb0ae80f901c75300ee3325af52bdfcbc3

Observation 63316094-82aa-4f62-af96-c6683c9dc7df · outbound

This paper cites Retrieval-augmented generation for knowledge- intensive nlp tasks.Advances in neural information processing systems, 33:9459–9474, 2020.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Retrieval-augmented generation for knowledge- intensive nlp tasks.Advances in neural information processing systems, 33:9459–9474, 2020

Reference 24

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source=pdf_text observed=2026-08-07T15:01:49.045923Z digest=sha256:cd6e06712b83a21ec04426771a89f5c5fcb29028dc160997c51e1e8735a7ecf1

Observation 5b2c346c-50d5-4e55-9ec6-ef2be37bfa1b · outbound

This paper cites Camel: Commu- nicative agents for" mind" exploration of large language model society.Advances in Neural Information Processing Systems, 36:51991–52008, 2023.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Camel: Commu- nicative agents for" mind" exploration of large language model society.Advances in Neural Information Processing Systems, 36:51991–52008, 2023

Reference 25

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source=pdf_text observed=2026-08-07T15:01:49.127651Z digest=sha256:836870834997cce564c146b186a7ad2a460d012e3eeecce7f0ec044b72bd4a16

Observation 14a1550f-da7e-4db8-9338-afef52d85c5c · outbound

This paper cites CodeI/O: Condensing Reasoning Patterns via Code Input-Output Prediction.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering CodeI/O: Condensing Reasoning Patterns via Code Input-Output Prediction

Reference 26

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source=pdf_text observed=2026-08-07T15:01:49.182311Z digest=sha256:1d934837d8e475c6713b9e1573ea2f20b47583f65fe7fb0e2e2b1d744558e9c9

Observation b62c9527-997b-4ad2-bf7e-6e0659191ad2 · outbound

This paper cites Search-o1: Agentic Search-Enhanced Large Reasoning Models.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Search-o1: Agentic Search-Enhanced Large Reasoning Models

Reference 27

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source=pdf_text observed=2026-08-07T15:01:49.281298Z digest=sha256:6988e9411c2178d4c60acb6a73ae4cdba9b7f20c02e1a6429df8e7789844f8c9

Observation 690493bc-5bc7-488a-b952-f95873eb618e · outbound

This paper cites Openmanus: An open-source framework for building general ai agents.https://github.com/mannaandpoem/OpenManus, 2025.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Openmanus: An open-source framework for building general ai agents.https://github.com/mannaandpoem/OpenManus, 2025

Reference 28

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source=pdf_text observed=2026-08-07T15:01:49.363237Z digest=sha256:b044e55bc7debb21a305df8d9316be2b6871d5586586b75c045e0746ada6f742

Observation 1996600a-f76b-45c9-9de2-996dbd426c05 · outbound

This paper cites DeepSeek-V3 Technical Report.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering DeepSeek-V3 Technical Report

Reference 29

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source=pdf_text observed=2026-08-07T15:01:49.481404Z digest=sha256:96070d74a78a55a4b773e08472192e0150756d95772be4d45111c8488db09dae

Observation 78f6a0db-2198-4338-9e9f-76dd49a2269c · outbound

This paper cites Inference- time scaling for generalist reward modeling.arXiv preprint arXiv:2504.02495, 2025.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Inference- time scaling for generalist reward modeling.arXiv preprint arXiv:2504.02495, 2025

Reference 30

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source=pdf_text observed=2026-08-07T15:01:49.582252Z digest=sha256:6d1cb369a728b5717800e92c23f0fa93a9d887753609f1be983b4d70316a8088

Observation a72772db-d49b-47f5-8a58-b276f70d5e69 · outbound

This paper cites Large Language Model Agent: A Survey on Methodology, Applications and Challenges.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Large Language Model Agent: A Survey on Methodology, Applications and Challenges

Reference 31

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source=pdf_text observed=2026-08-07T15:01:49.684485Z digest=sha256:fdb8aa5af40ea33eebfef899f45b50f177d2c5058189d34cb6674a7cb2bcdbca

Observation 42ab3d9d-addf-4c42-9d29-53c4ac9612d7 · outbound

This paper cites When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories

Reference 32

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source=pdf_text observed=2026-08-07T15:01:49.810326Z digest=sha256:3dd935cc774277510875fdc209aad1eccd7a0b73def56f20ed26722c03e9a038

Observation 644dd034-b6b8-4e11-98b2-777d9925284b · outbound

This paper cites Gaia: a benchmark for general ai assistants.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Gaia: a benchmark for general ai assistants

Reference 33

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source=pdf_text observed=2026-08-07T15:01:49.933428Z digest=sha256:501494cb3774917b78096b3ba7201a602246ee6e696759583cbe18724bec5e9b

Observation 8f228cc7-3e22-4f73-b311-848da86558d1 · outbound

This paper cites Deep research system card.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Deep research system card

Reference 34

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source=pdf_text observed=2026-08-07T15:01:50.046157Z digest=sha256:10d0ce7487e81c1e457ff620815a4bb3719c68ff37faba776229200aa82ae0dd

Observation 4dcb5c48-c56d-4d57-9016-a181337a6cd2 · outbound

This paper cites Memgpt: Towards llms as operating systems.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Memgpt: Towards llms as operating systems

Reference 35

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source=pdf_text observed=2026-08-07T15:01:50.131402Z digest=sha256:431fd766b60f6d389f6f8b3ecab0d1aae06f55d2b17507dacf21091d5bef3961

Observation e39f4fee-a022-4cf9-9a31-14ded47c1835 · outbound

This paper cites Advancing Reasoning in Large Language Models: Promising Methods and Approaches.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Advancing Reasoning in Large Language Models: Promising Methods and Approaches

Reference 36

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Observation a6cbe681-2e65-48f0-b8c9-6a6e3271f3d4 · outbound

This paper cites Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback

Reference 37

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source=pdf_text observed=2026-08-07T15:01:50.298572Z digest=sha256:8c68a8ea33466a267f6b16718ffeab0f6571da606a6fff44b202b0a40ba500ea

Observation bc5c113b-88cc-4f32-be09-c995dd129c73 · outbound

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

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Measuring and Narrowing the Compositionality Gap in Language Models

Reference 38

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source=pdf_text observed=2026-08-07T15:01:50.399671Z digest=sha256:28f0cf478858d3b3b72f491f49f590baa8afb5fc210b512f2594e2a18cd51df1

Observation ce5848bb-bb7a-4e77-9104-29c7483ecce3 · outbound

This paper cites Making Language Models Better Tool Learners with Execution Feedback.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Making Language Models Better Tool Learners with Execution Feedback

Reference 39

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source=pdf_text observed=2026-08-07T15:01:50.527055Z digest=sha256:ebdcfb3fa7f829593c18b718cf367afa1e803efbbd2998fed6b1cb0147fb9268

Observation 19ff8281-4890-4dae-b72b-568dc585149d · outbound

This paper cites ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs

Reference 40

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source=pdf_text observed=2026-08-07T15:01:50.657302Z digest=sha256:6f515c09176ae93d56fbde05121d17ddeec822e6155ed4c0be9f544daa652f53

Observation bf577efc-4b7e-4bfe-b9c9-39ac2b2939e3 · outbound

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

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 41

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source=pdf_text observed=2026-08-07T15:01:50.767884Z digest=sha256:1c9d8c9f41abefdc5fc9f436ae94e5d320841daa4624d4e62a0ca4f5490afb2b

Observation 8e3cd2d2-d47c-486e-8c7b-6f55f1e00139 · outbound

This paper cites R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning

Reference 42

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source=pdf_text observed=2026-08-07T15:01:50.849688Z digest=sha256:905a302ded76d3fa8581d9be522f612379ba443c7e6c58902c61cfcde501f1f2

Observation 2ca20240-bad4-4ae2-836d-25b4c2f87e5a · outbound

This paper cites Reinforcement learning.Journal of Cognitive Neuroscience, 11(1):126–134, 1999.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Reinforcement learning.Journal of Cognitive Neuroscience, 11(1):126–134, 1999

Reference 43

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source=pdf_text observed=2026-08-07T15:01:50.920286Z digest=sha256:7e4fb1033f23b0bc9420e0d9afc8eab57eabe72ecb69c0969e31fa66e7eb369d

Observation 793f0e73-9947-453d-a3db-c08a1840bce5 · outbound

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

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions

Reference 44

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source=pdf_text observed=2026-08-07T15:01:51.029031Z digest=sha256:992ddce7875fe9bcdd6a859966c29e0a6bfef2a4aba3d29cbd58783463d3576b

Observation b07cbbaa-7ea3-4238-a693-d612dc3b5cf9 · outbound

This paper cites Musique: Multihop questions via single-hop question composition.Transactions of the Association for Computational Linguistics, 10:539–554, 2022.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Musique: Multihop questions via single-hop question composition.Transactions of the Association for Computational Linguistics, 10:539–554, 2022

Reference 45

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source=pdf_text observed=2026-08-07T15:01:51.104525Z digest=sha256:ff7682a46f4ca120d2dd7e7cf56ddf9f024df2c59cf28c754df224c6ae0a51b5

Observation 0ad1b616-83f9-481b-81fd-ca21f09f70e0 · outbound

This paper cites an unresolved cited work.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Unresolved cited work

Reference 46

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source=pdf_text observed=2026-08-07T15:01:51.175924Z digest=sha256:b9d19284e3f1b9086814310575208cf5ebaa61c12b2aa36d3f2c3838e880fdb9

Observation bd95d930-e731-475a-bfb7-c993d4cd4372 · outbound

This paper cites Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models

Reference 47

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source=pdf_text observed=2026-08-07T15:01:51.271476Z digest=sha256:6d20800c51b88ad8f847fcc2fc52ad674aaf5acb162ead96057891fa3029d531

Observation 02ed0be0-a863-4c51-95ca-6be3163ba623 · outbound

This paper cites Text Embeddings by Weakly-Supervised Contrastive Pre-training.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Text Embeddings by Weakly-Supervised Contrastive Pre-training

Reference 48

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source=pdf_text observed=2026-08-07T15:01:51.378790Z digest=sha256:1eb839508c7a2158498680c5693f8302e440e1bbd4ca25fe2a31cd0c87790a06

Observation 814c6b55-cf7d-46bb-abbb-401c702343b2 · outbound

This paper cites Minilm: Deep self-attention distillation for task-agnostic compression of pre-trained transformers.Advances in neural information processing systems, 33:5776–5788, 2020.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Minilm: Deep self-attention distillation for task-agnostic compression of pre-trained transformers.Advances in neural information processing systems, 33:5776–5788, 2020

Reference 49

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source=pdf_text observed=2026-08-07T15:01:51.518537Z digest=sha256:e28b96feaf2c3a260b0bb2bfee482b02f86b06f12a91cf0248f1757727bf306b

Observation cb219b6d-eefa-4626-9778-7d0db03f3079 · outbound

This paper cites Self-Consistency Improves Chain of Thought Reasoning in Language Models.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 50

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source=pdf_text observed=2026-08-07T15:01:51.615429Z digest=sha256:8aa1eeeb6935ebe6f3de9261967ed93fc4ef8aadf838839253bdca8a4ca2d998

Observation 8fa0c1b0-5eed-48b7-a3a9-731c882e894f · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models.Advances in neural information processing systems, 35:24824–24837, 2022.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Chain-of-thought prompting elicits reasoning in large language models.Advances in neural information processing systems, 35:24824–24837, 2022

Reference 51

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source=pdf_text observed=2026-08-07T15:01:51.707303Z digest=sha256:368e2eb7586d7c732d3036c8c81a443729b1f42635737ebfd97cad1ffcb187a9

Observation b1af11d8-dcda-4a62-919a-e7f7233e29b7 · outbound

This paper cites Avatar: Optimizing llm agents for tool usage via contrastive reasoning.Advances in Neural Information Processing Systems, 37:25981–26010, 2024.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Avatar: Optimizing llm agents for tool usage via contrastive reasoning.Advances in Neural Information Processing Systems, 37:25981–26010, 2024

Reference 52

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source=pdf_text observed=2026-08-07T15:01:51.779425Z digest=sha256:c3280708f44e2e2d47c68c5293309147fb8a3af9bf2570c818a71dd1df4480f6

Observation 4067dd1a-013e-44d5-8404-6e922c0743b0 · outbound

This paper cites Omnithink: Expanding knowledge boundaries in machine writing through thinking.arXiv preprint arXiv:2501.09751, 2025.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Omnithink: Expanding knowledge boundaries in machine writing through thinking.arXiv preprint arXiv:2501.09751, 2025

Reference 53

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source=pdf_text observed=2026-08-07T15:01:51.868860Z digest=sha256:15bc600159c9c8a8ac0fcca4b165c48a6b19a77cde01327d9a43feaea16eb821

Observation 4207c2aa-10d7-4cc2-b57f-24ee7365f39b · outbound

This paper cites DeepSeek-Prover: Advancing Theorem Proving in LLMs through Large-Scale Synthetic Data.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering DeepSeek-Prover: Advancing Theorem Proving in LLMs through Large-Scale Synthetic Data

Reference 54

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source=pdf_text observed=2026-08-07T15:01:51.945284Z digest=sha256:85ae996c3be03c8578f0f6b527337ba5984e8981ab6dcc0d72994abf42926afb

Observation f442aaf3-56ec-4faa-a2c4-b336f64ab9ee · outbound

This paper cites Qwen2.5 Technical Report.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Qwen2.5 Technical Report

Reference 55

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source=pdf_text observed=2026-08-07T15:01:52.044882Z digest=sha256:f71ef07f839d1b18fc34c43bf19deef74cdceddbb116333876ee1c4a00d31d15

Observation 2a864e78-38ee-450c-a45f-a93d33d58a88 · outbound

This paper cites Gpt4tools: Teaching large language model to use tools via self-instruction.Advances in Neural Information Processing Systems, 36:71995–72007, 2023.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Gpt4tools: Teaching large language model to use tools via self-instruction.Advances in Neural Information Processing Systems, 36:71995–72007, 2023

Reference 56

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source=pdf_text observed=2026-08-07T15:01:52.132950Z digest=sha256:33da2c00818dbdd0ea830ff2de072134ee1ee840493346e2d19358db610c0eec

Observation 72d31ea5-a9ca-49a2-85ef-cd0c72e8b173 · outbound

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

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering

Reference 57

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source=pdf_text observed=2026-08-07T15:01:52.183621Z digest=sha256:430c5a3f952b6aa77658a921f289ca89e9099d86723c803e63fd2b4525da41a0

Observation f10a572c-bac7-48fd-9f68-d105f0286360 · outbound

This paper cites Tree of thoughts: Deliberate problem solving with large language models.Advances in neural information processing systems, 36:11809–11822, 2023.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Tree of thoughts: Deliberate problem solving with large language models.Advances in neural information processing systems, 36:11809–11822, 2023

Reference 58

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Observation 03b5cb72-3240-42fc-a5c3-9c625885ab88 · outbound

This paper cites Re- act: Synergizing reasoning and acting in language models.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Re- act: Synergizing reasoning and acting in language models

Reference 59

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source=pdf_text observed=2026-08-07T15:01:52.356651Z digest=sha256:4141f0300ede7503885ef8caa65a974ad8f82d6838ba20b663ef72711d33b871

Observation 76f415d1-231e-4d8b-bd28-b0812425b0ae · outbound

This paper cites Cognitive Mirage: A Review of Hallucinations in Large Language Models.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Cognitive Mirage: A Review of Hallucinations in Large Language Models

Reference 60

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source=pdf_text observed=2026-08-07T15:01:52.456120Z digest=sha256:4e16184f6e81ef4971b4721dceb591ebb6f3fcafb75a86bc430985e293d5ce92

Observation 653d0af0-7953-4fa3-a53b-b4e415f2de83 · outbound

This paper cites Physics of language models: Part 2.1, grade- school math and the hidden reasoning process.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Physics of language models: Part 2.1, grade- school math and the hidden reasoning process

Reference 61

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source=pdf_text observed=2026-08-07T15:01:52.554083Z digest=sha256:d8705de18e5c63c8e10a42337ba17215ad549f1e7e501fe2298a1d73b2b95b09

Observation 6d5116c2-3996-4605-802c-33106f2444d1 · outbound

This paper cites DAPO: An Open-Source LLM Reinforcement Learning System at Scale.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Reference 62

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source=pdf_text observed=2026-08-07T15:01:52.623494Z digest=sha256:e1b18997cd2748a04f87f55ca318f753f8d0885eb2e9b62ee57005ea9b600f8f

Observation 9f508db2-4861-4a7e-b441-d4aec3024129 · outbound

This paper cites Rankrag: Unifying context ranking with retrieval-augmented generation in llms.Advances in Neural Information Processing Systems, 37:121156–121184, 2024.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Rankrag: Unifying context ranking with retrieval-augmented generation in llms.Advances in Neural Information Processing Systems, 37:121156–121184, 2024

Reference 63

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source=pdf_text observed=2026-08-07T15:01:52.715969Z digest=sha256:88c0b7c8dd8e9274f6b0d721f0f189a19a3173b1680478134b3c261c1ee90367

Observation e9f68267-02ed-43b9-b69c-8527e21d69ae · outbound

This paper cites EASYTOOL: Enhancing LLM-based Agents with Concise Tool Instruction.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering EASYTOOL: Enhancing LLM-based Agents with Concise Tool Instruction

Reference 64

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source=pdf_text observed=2026-08-07T15:01:52.820435Z digest=sha256:35ad2511c962fcea19ed68991e4af2292f16d0d351339410152af391d1842f1e

Observation 94d5e418-60fe-411f-8901-5df2a9f77ef0 · outbound

This paper cites Inference Scaling for Long-Context Retrieval Augmented Generation.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Inference Scaling for Long-Context Retrieval Augmented Generation

Reference 65

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source=pdf_text observed=2026-08-07T15:01:52.929152Z digest=sha256:16e4de66ce9bf82bd8d24af7f3e4bb3c52b437efeb265031eb4dfd4eae0a7f08

Observation 657dc109-a0cd-412b-860d-200919d6f8d1 · outbound

This paper cites ReST-MCTS*: LLM Self-Training via Process Reward Guided Tree Search.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering ReST-MCTS*: LLM Self-Training via Process Reward Guided Tree Search

Reference 66

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source=pdf_text observed=2026-08-07T15:01:53.041020Z digest=sha256:0284ecf52c31a24366462fe072f239f411ad80b00aa3293b4760fb8bcff46e3d

Observation 0166a8af-3a9e-4bf1-b492-0413054aa0ef · outbound

This paper cites Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models

Reference 67

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source=pdf_text observed=2026-08-07T15:01:53.124339Z digest=sha256:c02518c40959771b70babe6fe5a2e71bd3e9b67b694758a2244e9565389b53cd

Observation f2b8f1dd-75bf-4dd2-a92a-dd8dc3af16ca · outbound

This paper cites Towards lifelong learning of large language models: A survey.ACM Computing Surveys, 57(8):1–35, 2025.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Towards lifelong learning of large language models: A survey.ACM Computing Surveys, 57(8):1–35, 2025

Reference 68

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source=pdf_text observed=2026-08-07T15:01:53.225400Z digest=sha256:660c890c92d699ef34321f5271cc0aefb3d21dfa348a1bb52ebdfdfd63ee848c

Observation 9d50a212-4afb-4547-a6e6-0f9653e8a99a · outbound

This paper cites DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environments.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environments

Reference 69

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no resolver link, observed 2026-08-07T15:01:53.300843Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:01:53.300843Z digest=sha256:80eb0b205bf3939dbbeb8adfef93fe7a0d7c0d912a560e6633a585155635d52e

Observation 3c9b6478-de05-4a90-9dcf-f395c8b39ed5 · outbound

This paper cites Least-to-Most Prompting Enables Complex Reasoning in Large Language Models.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Least-to-Most Prompting Enables Complex Reasoning in Large Language Models

Reference 70

Resolution
unresolved
no resolver link, observed 2026-08-07T15:01:53.399064Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:01:53.399064Z digest=sha256:cfdd7af987aa716f0eb4eb43f3f441a9aa52ffe7622c6b9a3d2258aa7aedabcf

Observation 3b763416-6090-47a8-a48f-108cead2487d · outbound

This paper cites WebArena: A Realistic Web Environment for Building Autonomous Agents.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering WebArena: A Realistic Web Environment for Building Autonomous Agents

Reference 71

Resolution
unresolved
no resolver link, observed 2026-08-07T15:01:53.518376Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:01:53.518376Z digest=sha256:63985640254260d61bc4562da2b10a53b2c90085afcab74f6fd9a8560101eeb1

Observation 7adba043-e770-4594-a863-467cceb02a30 · outbound

This paper cites an unresolved cited work.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Unresolved cited work

Reference 72

Resolution
unresolved
no resolver link, observed 2026-08-07T15:01:53.618260Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:01:53.618260Z digest=sha256:16b841f98ef349d1c75a01dbf43180e4d84490edb6593c03cc3499e058409aa6

Observation f3b28dd0-07b0-47b5-b515-efb74e773622 · outbound

This paper cites an unresolved cited work.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Unresolved cited work

Reference 73

Resolution
unresolved
no resolver link, observed 2026-08-07T15:01:53.721300Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:01:53.721300Z digest=sha256:58d9710599c0c89d6ffa1be0d9288a6e5427ebc42d9d62dc194c55010f488a5f

Observation 4e1e5bc4-a34c-42b4-8d5c-ba06df308c3f · outbound

This paper cites an unresolved cited work.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Unresolved cited work

Reference 74

Resolution
unresolved
no resolver link, observed 2026-08-07T15:01:53.835103Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:01:53.835103Z digest=sha256:d18eff932b19a93e84021f787969a00a7deb50c63064da48d39d4cb093673c5d

Observation f27925fd-ce24-4e7f-8360-088776a72f6d · outbound

This paper cites Key finding 1: Green energy policies have led to a 15% increase in employment across renewable energy sectors in 2023.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Key finding 1: Green energy policies have led to a 15% increase in employment across renewable energy sectors in 2023

Reference 75

Resolution
unresolved
no resolver link, observed 2026-08-07T15:01:53.964131Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:01:53.964131Z digest=sha256:3668d6810a3e18d2058e4d3d075b3ac1adcf2b174a7c8ec078044478c5eb3d78

Observation 1b7069ce-8a9a-4bb9-ab59-f374f65550cd · outbound

This paper cites an unresolved cited work.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Unresolved cited work

Reference 76

Resolution
unresolved
no resolver link, observed 2026-08-07T15:01:54.082523Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:01:54.082523Z digest=sha256:ef5e57761a0965291fff15f3f5022735e19b9c8ae83d84feebf162ba07ff39fd

Observation d8b81ca9-4dbe-45e1-85f5-c3db13079f32 · outbound

This paper cites an unresolved cited work.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Unresolved cited work

Reference 77

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:02:07.471877Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T15:01:54.204923Z digest=sha256:2dae7241b786a6bc59b00682f7b734a972b04d27f3cb63a10f15ba7de46e9f54

Observation 2e0006db-737b-4fea-bf1e-0a93d165bcc5 · outbound

This paper cites an unresolved cited work.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Unresolved cited work

Reference 78

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:02:07.316944Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T15:01:54.368152Z digest=sha256:4f9a91a158d2ffd4bf38d753000abb624aed78575dea042a3e5506262912e3dc

Observation c2284111-b60f-4aa1-8e1e-12575ec27185 · outbound

This paper cites an unresolved cited work.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Unresolved cited work

Reference 79

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:02:07.118479Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T15:01:54.503352Z digest=sha256:e23004eee90ded7c94357e6829d73a5d6adeec627831cb5676340f7a504a1cc0

Observation 0729ce25-d2cd-48ad-bebf-0c505e88d7ff · outbound

This paper cites an unresolved cited work.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Unresolved cited work

Reference 80

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:02:06.902036Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T15:01:54.675577Z digest=sha256:c45b665ba0d1cda362870b70de419f1f708689714ddb00c3eef27fb712da7c0f

Observation 925d70dc-97ac-4af9-bb18-3091e69940c4 · outbound

This paper cites an unresolved cited work.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Unresolved cited work

Reference 81

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:02:06.799178Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T15:01:54.816652Z digest=sha256:0d1897bf929288f40158bdbeb13cb1b50e4785b51fd83ebe10bef4bd5a5fd782

Observation ce2a1ca1-8f55-4ccb-9599-1efefc80cfe1 · outbound

This paper cites an unresolved cited work.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Unresolved cited work

Reference 82

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:02:06.565501Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T15:01:54.973379Z digest=sha256:7d2d6ea83448c95ee946a7b8207b8432d01b2025f9f967970229b02baca3bcc7

Observation 40c6bf94-0574-4c51-a19f-7b5a86cb7c4c · outbound

This paper cites Return a maximum of three distinct learnings.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Return a maximum of three distinct learnings

Reference 83

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:02:06.368255Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T15:01:55.160156Z digest=sha256:d89028caca15eaefe83369d38d188231dc545e79b4a6673db4ed518e5f48bee0

Observation 1de8bf65-7cdb-44ab-bd1a-c33abc31e5b6 · outbound

This paper cites an unresolved cited work.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Unresolved cited work

Reference 84

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:02:06.175434Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T15:01:55.297085Z digest=sha256:a45a7e6f1da883240eee46276af3c654ca5c32ebbf70ba243ca8d7b316fe6ce6

Observation 317336d3-f42e-4078-849b-d3f4508a829e · outbound

This paper cites an unresolved cited work.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Unresolved cited work

Reference 85

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:02:05.990506Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T15:01:55.480295Z digest=sha256:d0f3816d034a9855ba5cd56bb8af7207faf4ddf48436e6bb38b2df7c4146eaad

Observation aefdc583-c524-4f4d-97ae-fdac0e8c431a · outbound

This paper cites input finding 1.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering input finding 1

Reference 86

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:02:05.832913Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T15:01:55.614968Z digest=sha256:354f0fa2f8fefaf67c81c0692428a98f15f5cfae7388a0e63bb9d54b4c99b5c0

Observation 19b54e8b-4aa0-4aca-9a38-d7ecf1890a12 · outbound

This paper cites an unresolved cited work.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Unresolved cited work

Reference 87

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:02:05.570165Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T15:01:55.751651Z digest=sha256:d2b859edd8df6dec431bb125aa86cd306919562418b93b793b1e7fc4bf49f009

Observation 69911469-f8d7-415e-b91c-04470c30f860 · outbound

This paper cites an unresolved cited work.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Unresolved cited work

Reference 88

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:02:05.354227Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T15:01:55.928812Z digest=sha256:5904a21ab0f45a272af17ef803237650a36dad2ac4395f44066aa051fa632fd6

Observation c010c893-8c3b-4d69-99d7-952fd0239669 · outbound

This paper cites an unresolved cited work.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Unresolved cited work

Reference 89

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:02:05.128734Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T15:01:56.057466Z digest=sha256:5ee120a762d7d34ffdbc35de790104386623f3d7881c8627067d545f4bb26e07

Observation 8a59f225-e6f4-49a4-98f2-300a79271451 · outbound

This paper cites Do not include any explanatory text outside the JSON array.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Do not include any explanatory text outside the JSON array

Reference 90

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:02:04.968210Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T15:01:56.170592Z digest=sha256:c41b48c3c7f5921743b282d7e7c5bcbad9a02833d12bea4718694dfc1975418e

Observation cd5dc44a-6b66-4fcd-802b-762177016cdd · outbound

This paper cites an unresolved cited work.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Unresolved cited work

Reference 91

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:02:04.813465Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T15:01:56.335207Z digest=sha256:e8c4f19184cf86182b049826bcd0860cbad8b5216a81ff179852e06fb2b71a0b

Observation 7fa4c344-7704-443f-a38a-08a97526595c · outbound

This paper cites an unresolved cited work.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Unresolved cited work

Reference 92

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:02:04.606680Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T15:01:56.513208Z digest=sha256:0ccdbceba004cb8734eb01ca834e690c0845a0aa4bcfd5b95006afe6f69403ff

Observation 10f4ab2d-bffe-433f-a56e-bfbfd0409b53 · outbound

This paper cites O 2-Searcher is equipped with search capabilities to effectively gather and sift through pertinent online information, distilling key findings.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering O 2-Searcher is equipped with search capabilities to effectively gather and sift through pertinent online information, distilling key findings

Reference 93

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:02:04.446765Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T15:01:56.624751Z digest=sha256:f7465e3b92e28755acae2e6c76ad28a9aa6ce2c3bc6a6b64ee26861a2aacf0ef

Observation 38a70488-ece6-476a-9640-42227b9db57c · outbound

This paper cites an unresolved cited work.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Unresolved cited work

Reference 94

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:02:04.286353Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T15:01:56.690183Z digest=sha256:a749f9e18d8e8da4122c50efd4b1ab86e08a984f301cb74502a93e3b8746d3ca

Observation 938845f3-0672-43b6-b522-69cc58159b3b · outbound

This paper cites an unresolved cited work.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Unresolved cited work

Reference 95

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:02:04.163549Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T15:01:56.790817Z digest=sha256:46042ae9950f17bbcb5ec288bde1d1a66804cfe06e0e51a88be9c1eb051ffc4c

Observation 9922ce81-b2b9-4650-a531-23562b18b94d · outbound

This paper cites Here are distilled key findings relevent to user’s query:<contents>CONTENTS</contents>.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Here are distilled key findings relevent to user’s query:<contents>CONTENTS</contents>

Reference 96

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:02:03.890076Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T15:01:56.929816Z digest=sha256:2538faa5b387dcad545afcdb15e4150008be1ec3a52b79e4c62b120bd488ff25

Observation 3fb8bc33-b64f-4b10-8b20-4243cbd2d007 · outbound

This paper cites Introduction.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Introduction

Reference 97

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:02:03.664232Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T15:01:57.055868Z digest=sha256:af39cb6241e6184dc5c9a61fe55276655300b42b23fd6023b716644e30cb5c69

Observation ea4128f7-9e5e-4172-90ce-6bb53afedabd · outbound

This paper cites an unresolved cited work.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Unresolved cited work

Reference 98

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:02:03.449336Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T15:01:57.185951Z digest=sha256:6381dc7e779f8037cdc489e0077d8b24d8d1fc4d254b8d7c63b48546d622076b

Observation 121eb02b-580b-450b-bcfe-4ca03cad9c6c · outbound

This paper cites an unresolved cited work.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Unresolved cited work

Reference 99

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:02:03.270150Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T15:01:57.345893Z digest=sha256:7f207e572a198e0cd6927fc16368767e71dcea1f95040f4415b7aebedbccabf5

Observation ae0c1e73-cadc-465d-a6d5-14d7731887dc · outbound

This paper cites an unresolved cited work.

O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Unresolved cited work

Reference 100

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:02:03.139766Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T15:01:57.448378Z digest=sha256:a5934cca2813743962c54ad405fde8cb46b8590ce34f6730075ac51ed084a7bc

Pith citing papers

Observation db71740e-3dcd-4e2e-9a94-e4126188259d · inbound

ParallelSearch: Train your LLMs to Decompose Query and Search Sub-queries in Parallel with Reinforcement Learning cites this paper.

ParallelSearch: Train your LLMs to Decompose Query and Search Sub-queries in Parallel with Reinforcement Learning O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-05T21:12:11.380262Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T21:12:11.380262Z digest=sha256:838a4e0daab1a876b77a0424ac2ccc1370805947cf0fb46550988b6f62333686

Observation 95a35b92-324c-41c0-afe5-153aa7987f46 · inbound

Erase to Improve: Erasable Reinforcement Learning for Search-Augmented LLMs cites this paper.

Erase to Improve: Erasable Reinforcement Learning for Search-Augmented LLMs O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering

Reference 44

Resolution
verified exact
arxiv_id, observed 2026-05-18T11:11:18.194028Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-18T11:06:20.058342Z digest=sha256:f1420458b04adaa4b8dad43cae2c8cbda926a5d505616d501c35b15e1dab9552

Observation 4a4d953d-6c70-46e2-bf01-d7543508163a · inbound

EvolveR: Self-Evolving LLM Agents through an Experience-Driven Lifecycle cites this paper.

EvolveR: Self-Evolving LLM Agents through an Experience-Driven Lifecycle O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering

Reference 24

Resolution
verified exact
arxiv_id, observed 2026-05-18T06:20:58.379213Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-18T06:19:44.360734Z digest=sha256:37bedd8742ca8110ca619e318e7c7ff7506b6e098cef465da4705d796b8d1d2f

Observation c6fa54ac-2972-4ee1-9caa-0ab9c54c5381 · inbound

EvolveR: Self-Evolving LLM Agents through an Experience-Driven Lifecycle cites this paper.

EvolveR: Self-Evolving LLM Agents through an Experience-Driven Lifecycle O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering

Reference 24

Resolution
verified exact
arxiv_id, observed 2026-05-21T20:50:36.521411Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-21T20:50:06.642976Z digest=sha256:6d61c536a6da1f180d1473677f7dc4285aa42c7ceb753af4daf3aa8a174fbc1e

Observation 38e62f24-3abf-4f9f-87f5-f16567de907c · inbound

The Agent's First Day: Benchmarking Learning, Exploration, and Scheduling in the Workplace Scenarios cites this paper.

The Agent's First Day: Benchmarking Learning, Exploration, and Scheduling in the Workplace Scenarios O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-03T10:55:09.411210Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T10:55:09.411210Z digest=sha256:9055b1799955fd7a8e50af8b650a95d776a9bc089e8b9907d5dc1cfb85877e4b

Observation 7b446caa-dc00-470e-b490-ce6343ff6535 · inbound

Planner-Centric Reinforcement Learning for Deep Research with Structure-Aware Reward cites this paper.

Planner-Centric Reinforcement Learning for Deep Research with Structure-Aware Reward O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering

Reference 17

Resolution
verified exact
arxiv_id, observed 2026-06-28T22:32:44.034005Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-06-28T22:30:00.735630Z digest=sha256:0acfadb616645395dd70e3278d7550c3609dfc2e2940d2a0a8df9d8b240c3579

Observation baf03880-f8ab-4fe3-9637-5e937418abcc · inbound

DocArena: Turning Raw Documents into Controllable Training Environments for Document Search Agents cites this paper.

DocArena: Turning Raw Documents into Controllable Training Environments for Document Search Agents O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering

Reference 35

Resolution
metadata mismatch
arxiv_id, observed 2026-06-29T12:53:26.514372Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-06-29T12:50:16.625077Z digest=sha256:92aeb3ec2756d4d1d681a135eae1c73b2f3f420d925daa45c0d8575590e4a3f9

Observation 09a51526-e474-4168-9506-abd0a408f3fd · inbound

MemHarness: Memory Is Reconstructed, Not Replayed cites this paper.

MemHarness: Memory Is Reconstructed, Not Replayed O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering

Reference 4

Resolution
unresolved
no resolver link, observed 2026-07-31T12:39:14.014060Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-31T12:39:14.014060Z digest=sha256:f9ecc16366850efbbd4b24e57f00dee75b72e16dfb2434e0a3c4f37e6085058b

Observation 9e35b822-9552-4fc3-9095-7a7f92e91990 · inbound

SearchMaster: Grounded and Regulated Self-Play for Search Agents cites this paper.

SearchMaster: Grounded and Regulated Self-Play for Search Agents O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-15T15:07:59.132190Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T15:07:59.132190Z digest=sha256:612704ea11942ec0a6fd3e55279df4f3f1dc783133b366b34a6c941c04b44f51

Observation 141553b0-a2d5-49f9-9141-ef0e292b767e · 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 O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering

Reference 151

Resolution
unresolved
no resolver link, observed 2026-08-04T15:12:55.912187Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T15:12:55.912187Z digest=sha256:01b9d1f93adf44af4e454178df6f6956c709dac5bc942d80d421e60c822bf2d3