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Paper Citation Record · LEDGER

Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search

As of 19 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 6 inbound Pith citation observations for arXiv:2510.18939.

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

pith.paper-citation-record.v1
2510.18939 v2

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T08:48:44.754612Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T02:07:43.248172Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T16:17:08.733194Z

Reference resolution

40 of 40 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved39
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2fed54d5-6e6a-42de-a523-19ce9900b4b4 · outbound

This paper cites Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models.

Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models

Reference 1

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T08:48:40.047420Z digest=sha256:20929a4bc2c8190e100fed22f14fd2985afe39ef6eb85a43925cc82e09aabc03

Observation eef1e18d-7975-4846-a20c-e2f1ba762bf2 · outbound

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

Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning

Reference 2

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source=arxiv_source observed=2026-08-04T08:48:40.148524Z digest=sha256:d28f9fce3c4e6f4cd7f74db7f73050c61ba7354f57cf62dd45495292c6e5e427

Observation 04a67dca-f160-4b10-ba4d-b64439e5dc83 · outbound

This paper cites DeepResearch Bench: A Comprehensive Benchmark for Deep Research Agents.

Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search DeepResearch Bench: A Comprehensive Benchmark for Deep Research Agents

Reference 3

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source=arxiv_source observed=2026-08-04T08:48:40.295134Z digest=sha256:7791c56ef663555bce33bcacb05f7f79e8fd1cb0dd72c6a6cfc25df79d630d07

Observation 371c1350-aa90-4f0e-8658-75cabc40daee · outbound

This paper cites gpt-researcher , July 2023.

Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search gpt-researcher , July 2023

Reference 4

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source=arxiv_source observed=2026-08-04T08:48:40.440962Z digest=sha256:b8412d1d22b96bda3ecd9f45917942dcca9c92f0892d0420b5fc71a49d3a86cb

Observation b7dcd724-0ca3-43ad-bf58-cad6142d3a05 · outbound

This paper cites Enabling large language models to generate text with citations.

Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search Enabling large language models to generate text with citations

Reference 5

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source=arxiv_source observed=2026-08-04T08:48:40.583499Z digest=sha256:0028c6f18fe4f34a9868e35813431ed4a1fbc923b7f6a03a1e4d4a8ea9e61b96

Observation 6ace1904-2836-423c-a057-6f17831da184 · outbound

This paper cites Gemini deep research — your personal research assistant, September 2025.

Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search Gemini deep research — your personal research assistant, September 2025

Reference 6

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-04T08:48:40.695565Z digest=sha256:62796210ea10194e71ca37c1bfb844ef7662a2371cc9f1c09eb5be9744a042f3

Observation f67cbf6c-619c-4640-b201-8aec9bccc120 · outbound

This paper cites Atlas: few-shot learning with retrieval augmented language models.

Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search Atlas: few-shot learning with retrieval augmented language models

Reference 7

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source=arxiv_source observed=2026-08-04T08:48:40.797754Z digest=sha256:68876b8074452320f117c7f438c2609ee7fa7fbb061ce34e27217d08d00b4e96

Observation 785a30ee-2b24-48a5-af96-a455692b9848 · outbound

This paper cites An Empirical Study on Reinforcement Learning for Reasoning-Search Interleaved LLM Agents.

Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search An Empirical Study on Reinforcement Learning for Reasoning-Search Interleaved LLM Agents

Reference 8

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source=arxiv_source observed=2026-08-04T08:48:40.941733Z digest=sha256:2707368fccad37bd529cb75033640cb13d7694c1493d9e22adcb09579a477332

Observation 65719429-2043-47a2-8b09-86fa8d687856 · outbound

This paper cites Search-r1: Training LLM s to reason and leverage search engines with reinforcement learning.

Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search Search-r1: Training LLM s to reason and leverage search engines with reinforcement learning

Reference 9

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source=arxiv_source observed=2026-08-04T08:48:41.086533Z digest=sha256:f31e0a9a396c38b61831bcc487f53a060129ae3ad70c5749688abf4ca3038cee

Observation c2eef895-aa1f-4617-bfff-25bb505e8a86 · outbound

This paper cites T rivia QA : A large scale distantly supervised challenge dataset for reading comprehension.

Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search T rivia QA : A large scale distantly supervised challenge dataset for reading comprehension

Reference 10

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source=arxiv_source observed=2026-08-04T08:48:41.209069Z digest=sha256:2c6b906c6c3042d574c0cfe50f9486dee745c456cb8346e2e052d4c2652f2b16

Observation d50dde22-6808-43e0-86a4-4d1f36ea3bec · outbound

This paper cites WiCE: Real-World Entailment for Claims in Wikipedia.

Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search WiCE: Real-World Entailment for Claims in Wikipedia

Reference 11

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source=arxiv_source observed=2026-08-04T08:48:41.324961Z digest=sha256:831fa3794cb90535df4408701da937911e2a596ee9dad420ad05a92c4cae3aed

Observation 9a01aef6-952a-410c-be42-47723190317c · outbound

This paper cites Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov.

Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov

Reference 12

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source=arxiv_source observed=2026-08-04T08:48:41.423753Z digest=sha256:721627cd15de07778519cd03578167946e44e38ff9a64e7c301989546428a1c7

Observation 51336c07-5652-4246-966b-49266096af35 · outbound

This paper cites u ttler, Mike Lewis, Wen-tau Yih, Tim Rockt\.

Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search u ttler, Mike Lewis, Wen-tau Yih, Tim Rockt\

Reference 13

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source=arxiv_source observed=2026-08-04T08:48:41.572815Z digest=sha256:3db677de944f46b9dbf68d424bf0a883939a041db9fa846736de9d78d54423fe

Observation 23489cbe-5327-4627-b7e4-c1acdb7c21e6 · outbound

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

Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search WebSailor: Navigating Super-human Reasoning for Web Agent

Reference 14

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source=arxiv_source observed=2026-08-04T08:48:41.651005Z digest=sha256:8c2e312ea877fe7692ccab3dbc9d451365ee0cd4fc163a1981bc2e5f3fb0ab8e

Observation b1298222-eaa7-401f-a71e-e7837e9323cb · outbound

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

Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search Search-o1: Agentic Search-Enhanced Large Reasoning Models

Reference 15

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source=arxiv_source observed=2026-08-04T08:48:41.750721Z digest=sha256:6757f8832e08b7420e871ea3974840b1c9b065d56b145e095824f8cfc2e4ee38

Observation 28ba5ee9-b330-4f1b-821d-513dc3c36596 · outbound

This paper cites WebThinker: Empowering Large Reasoning Models with Deep Research Capability.

Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search WebThinker: Empowering Large Reasoning Models with Deep Research Capability

Reference 16

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source=arxiv_source observed=2026-08-04T08:48:41.881562Z digest=sha256:be36da6c3b32efe1ad746c59927bffdeae62c7e0a6d12656f44cb46d2c4cc6dd

Observation d7e92b7d-3697-4d93-9b3e-5474dbeedd6e · outbound

This paper cites SFR-DeepResearch: Towards Effective Reinforcement Learning for Autonomously Reasoning Single Agents.

Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search SFR-DeepResearch: Towards Effective Reinforcement Learning for Autonomously Reasoning Single Agents

Reference 17

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source=arxiv_source observed=2026-08-04T08:48:42.025933Z digest=sha256:b00a2b8f96973d70be65a3686d47e6f03a90cb3b81ef5a6d6296b55232385153

Observation e6ec41b4-455a-4f23-a9ea-374de8da0e04 · outbound

This paper cites Introducing deep research, February 2025.

Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search Introducing deep research, February 2025

Reference 18

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source=arxiv_source observed=2026-08-04T08:48:42.132538Z digest=sha256:f50da4b9b1a2cc8ce2eabebf05b046d02b6c9964ae387677fc58904a934ab856

Observation 481fa4da-cdde-4393-9327-71386fab43d3 · outbound

This paper cites KILT : a benchmark for knowledge intensive language tasks.

Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search KILT : a benchmark for knowledge intensive language tasks

Reference 19

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source=arxiv_source observed=2026-08-04T08:48:42.252055Z digest=sha256:5fb3798fa7e71411cb4adf8e5c296995f25b6034584d690b7ba50a080707d25a

Observation 255871b4-6af2-4c5b-a0eb-596639a9efbe · outbound

This paper cites Humanity's Last Exam.

Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search Humanity's Last Exam

Reference 20

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source=arxiv_source observed=2026-08-04T08:48:42.374979Z digest=sha256:caa753851f4ea1331d8a2205e0a88c4e985653299f205f46d0ed278733359498

Observation 0abeef4f-bad7-4e50-8762-2223247f6b88 · outbound

This paper cites Webresearcher: Unleashing unbounded reasoning capability in long-horizon agents, 2025.

Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search Webresearcher: Unleashing unbounded reasoning capability in long-horizon agents, 2025

Reference 21

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source=arxiv_source observed=2026-08-04T08:48:42.518136Z digest=sha256:f1fa72b6033b902f80460d633f048708b47ddc89a0da1095e5c27f2a3e3ca95b

Observation c9f46303-9efb-43a2-ac75-f870bc30e799 · outbound

This paper cites Measuring Attribution in Natural Language Generation Models.

Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search Measuring Attribution in Natural Language Generation Models

Reference 22

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source=arxiv_source observed=2026-08-04T08:48:42.643771Z digest=sha256:283c384589738e9efafc86251353bcfb9ba2e28197ded4c684ddf342ee19615a

Observation 4bd4faa8-481d-48b3-92e6-73e1fc28b681 · outbound

This paper cites The probabilistic relevance framework: Bm25 and beyond.

Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search The probabilistic relevance framework: Bm25 and beyond

Reference 23

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source=arxiv_source observed=2026-08-04T08:48:42.819519Z digest=sha256:8691880db0a80e8a3678a9b311635679135956d64d68bdad50a1e9b485e477e3

Observation e29ce087-131b-4684-b83f-5bba075b43aa · outbound

This paper cites Open-source DeepResearch -- Freeing our search agents, January 2025.

Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search Open-source DeepResearch -- Freeing our search agents, January 2025

Reference 24

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source=arxiv_source observed=2026-08-04T08:48:42.940305Z digest=sha256:1897d27dbe82fe523bbbce1096aa61bb5dbe975e97d34cdf68c561450ca79f98

Observation a51cc6d8-d1a4-429c-9106-c954e5042fb9 · outbound

This paper cites REPLUG : Retrieval-augmented black-box language models.

Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search REPLUG : Retrieval-augmented black-box language models

Reference 25

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source=arxiv_source observed=2026-08-04T08:48:43.070059Z digest=sha256:84096ce31c1c9a3173d08dd2316b5d219434141584ca3befe6829f9b774013f7

Observation 22126f1b-c684-45c6-a0c2-05c8d0bc2ad3 · outbound

This paper cites Simpledeepsearcher: Deep information seeking via web-powered reasoning trajectory synthesis, 2025.

Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search Simpledeepsearcher: Deep information seeking via web-powered reasoning trajectory synthesis, 2025

Reference 26

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source=arxiv_source observed=2026-08-04T08:48:43.219177Z digest=sha256:c3677511673a5f75175e9eb9657a6098b61cf981618d9b808bafc12296aa7ac1

Observation c33103ca-983b-4a90-92a7-d7bede1a5605 · outbound

This paper cites WebShaper: Agentically Data Synthesizing via Information-Seeking Formalization.

Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search WebShaper: Agentically Data Synthesizing via Information-Seeking Formalization

Reference 27

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source=arxiv_source observed=2026-08-04T08:48:43.350399Z digest=sha256:9c29f1001677b6b118be22e2a3bde2cb732791dcbf9159c945bfa2e3bbe6630a

Observation 42073e56-f2f1-489f-bdb7-53e1b5f76b3f · outbound

This paper cites Executable code actions elicit better llm agents.

Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search Executable code actions elicit better llm agents

Reference 28

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source=arxiv_source observed=2026-08-04T08:48:43.429850Z digest=sha256:91435a8d72200a8edd99e2cc11b85baaa0b12f6abc5c775353e1fdb772369c91

Observation f859f58a-86bb-439c-b203-cd87289ef507 · outbound

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

Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search BrowseComp: A Simple Yet Challenging Benchmark for Browsing Agents

Reference 29

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source=arxiv_source observed=2026-08-04T08:48:43.544718Z digest=sha256:e84e52f6e1ff7c94f4a4a6314dd5a8c7147d66d8273c7c31d1b877c4fdce88b2

Observation a8124256-3110-4499-bc2b-dd1627f33e4c · outbound

This paper cites W eb W alker: Benchmarking LLM s in web traversal.

Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search W eb W alker: Benchmarking LLM s in web traversal

Reference 30

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no resolver link, observed 2026-08-04T08:48:43.688386Z

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source=arxiv_source observed=2026-08-04T08:48:43.688386Z digest=sha256:2fa6c6af4d6a156b75fee781fcd7022dbaaf7b46fc3cb4c72c8d277d7d448121

Observation 0c6c584b-5830-4217-8897-cc943e2d46c0 · outbound

This paper cites Resum: Unlocking long-horizon search intelligence via context summarization, 2025 b.

Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search Resum: Unlocking long-horizon search intelligence via context summarization, 2025 b

Reference 31

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source=arxiv_source observed=2026-08-04T08:48:43.823739Z digest=sha256:18faeb5ba6c93ec66522fc9f0c380ff32b906ed0ee9ba24d3bd57bdd381dc7be

Observation 3dffd7e9-c1e0-49a9-8a5e-6aa6529b9d68 · outbound

This paper cites Grok 3 beta — the age of reasoning agents, February 2025.

Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search Grok 3 beta — the age of reasoning agents, February 2025

Reference 32

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source=arxiv_source observed=2026-08-04T08:48:43.920875Z digest=sha256:662b7a9f7c78c6b679a75e2570d6fdfd91cb56b6103f6a1921f1bfd1e51cb29a

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

This paper cites Open Data Synthesis For Deep Research.

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

Reference 33

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source=arxiv_source observed=2026-08-04T08:48:44.022931Z digest=sha256:61ea9bdd5900a17847a8e10f7b988a3e712d01366f8fb7d5a465dd2f0333ee44

Observation 40c8f4e1-27bd-4c0b-bb6a-331515d06fce · outbound

This paper cites ReAct: Synergizing Reasoning and Acting in Language Models.

Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search ReAct: Synergizing Reasoning and Acting in Language Models

Reference 34

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source=arxiv_source observed=2026-08-04T08:48:44.134713Z digest=sha256:429d3acaf64c4d9cbfa5c2dffbb6323500440d757c73fe57a84417ce7c5eafbc

Observation bfff2785-59b6-4d45-8fdd-5827d7223dce · outbound

This paper cites Helmet: How to evaluate long-context language models effectively and thoroughly.

Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search Helmet: How to evaluate long-context language models effectively and thoroughly

Reference 35

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source=arxiv_source observed=2026-08-04T08:48:44.221714Z digest=sha256:bf3eb24e9af6b6059a21586a14deb888069e6919a6003d08b7d30deb184b950c

Observation cb78d5e0-b660-4caa-b446-3b6896f794db · outbound

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

Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environments

Reference 36

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source=arxiv_source observed=2026-08-04T08:48:44.343371Z digest=sha256:71681b6602105bc810c9407132d7c97a9f99102caec0fc9d8a63e883fb001800

Observation eff58286-6f42-4ab3-8966-87fa3d8511bc · outbound

This paper cites write newline.

Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search write newline

Reference 37

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no resolver link, observed 2026-08-04T08:48:44.452167Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T08:48:44.452167Z digest=sha256:f7f230b6bcc03b72e32c665d037055aa42c8023a0aed6101257bce83c39866ec

Observation af8d029f-fa03-4cac-a1ea-c960024764d9 · outbound

This paper cites @esa (Ref.

Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search @esa (Ref

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-04T08:48:44.558202Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T08:48:44.558202Z digest=sha256:b377e03a31e2c6dcc9cf10c2589d4860689e56266b74ecc6d55938c8c483be19

Observation a07488ff-7af1-4257-a0d2-fadfb9d6ff08 · outbound

This paper cites an unresolved cited work.

Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search Unresolved cited work

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-04T08:48:44.655859Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T08:48:44.655859Z digest=sha256:bc75215cbc6e9140cc71c3062edd95b4aad4f7eccbac3ba991001341e3e73085

Observation f0b18037-c21a-480f-bfd4-f72b168ab37e · outbound

This paper cites yes" or.

Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search yes" or

Reference 40

Resolution
malformed identifier
no resolver link, observed 2026-08-04T08:48:44.754612Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T08:48:44.754612Z digest=sha256:0c0e6417cb74e2a25e0a7d09f909f36e8b33f5acb96f66b3a4ae28671250dedb

Pith citing papers

Observation a897a367-e288-4bd5-a5a8-227d4013d101 · inbound

On Training Large Language Models for Long-Horizon Tasks: An Empirical Study of Horizon Length cites this paper.

On Training Large Language Models for Long-Horizon Tasks: An Empirical Study of Horizon Length Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search

Reference 32

Resolution
metadata mismatch
arxiv_id, observed 2026-07-15T01:20:48.939128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-05-08T18:13:25.735085Z digest=sha256:00d08626854c182bfdf4ac93083ed0ec195de4cb2edc915cc5d860633a8d2927

Observation f3e368c8-d99a-44de-bbad-4562672385f3 · inbound

The Context Gathering Decision Process: A POMDP Framework for Agentic Search cites this paper.

The Context Gathering Decision Process: A POMDP Framework for Agentic Search Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search

Reference 43

Resolution
verified exact
arxiv_id, observed 2026-07-15T01:20:48.939128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-11T01:11:10.959395Z digest=sha256:1fe0f34655236f4fd6401bfbe73cce966cdab8b4aad507d020d8b50c387eef3e

Observation 36bb1f0f-8cda-4c08-88af-df86b168a2a4 · inbound

SlimSearcher: Training Efficiency-Aware Web Agents via Adaptive Reward Gating cites this paper.

SlimSearcher: Training Efficiency-Aware Web Agents via Adaptive Reward Gating Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search

Reference 57

Resolution
metadata mismatch
arxiv_id, observed 2026-07-15T01:20:48.939128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-06-27T22:54:44.329613Z digest=sha256:6af5d9ae2c28f2e0162bafbe3633d25016d8f1d0382249736f8c005fb5a61f9a

Observation 551d871b-bbc1-4b3b-ba19-a103a0c77616 · inbound

LLM Agents Are Latent Context Managers: Eliciting Self-Managed Context via State Proprioception cites this paper.

LLM Agents Are Latent Context Managers: Eliciting Self-Managed Context via State Proprioception Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search

Reference 10

Resolution
metadata mismatch
arxiv_id, observed 2026-07-15T01:20:48.939128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-06-30T06:27:19.044879Z digest=sha256:082e3a85e479a269f8f8fc6320a500bcd636fb6457e55d4d4cc3bb23c156a948

Observation 6fd5439e-a56b-4eb1-86cb-6fdbc8a2b584 · inbound

LLM Agents Are Latent Context Managers: Eliciting Self-Managed Context via State Proprioception cites this paper.

LLM Agents Are Latent Context Managers: Eliciting Self-Managed Context via State Proprioception Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-02T09:38:44.788879Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T09:38:44.788879Z digest=sha256:cfc951e33c3e372dfc384052567859649c48bbe5660e28674f6986e88aa07f26

Observation 0080a496-f748-4bf8-a651-634892344d7a · inbound

LLM Agents Are Latent Context Managers: Eliciting Self-Managed Context via State Proprioception cites this paper.

LLM Agents Are Latent Context Managers: Eliciting Self-Managed Context via State Proprioception Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-03T02:07:43.248172Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T02:07:43.248172Z digest=sha256:e7a56f09ccf940db7438e4120031f2e18409876842066b72de53aa19e45529ea