Paper Citation Record · LEDGER

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering

As of 24 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 1 inbound Pith citation observation for arXiv:2608.00974.

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

pith.paper-citation-record.v1
2608.00974 v1

Coverage vector

measured 55 of 55 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T15:18:32.208109Z

measured 56 of 56 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T15:18:31.074500Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-15T15:18:32.771997Z

Reference resolution

55 of 55 outbound references displayed

verified exact2
verified fuzzy24
unresolved29
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation abfb8242-d544-4f9a-a52c-2a44665327fa · outbound

This paper cites Traditionally, users were required to manually compile information by reviewing a list of results generated by search engines.

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering Traditionally, users were required to manually compile information by reviewing a list of results generated by search engines

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:18:34.175156Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation de076a78-438b-44aa-96bc-5730d87e4bbe · outbound

This paper cites an unresolved cited work.

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-15T15:18:34.164267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 29a41df9-53c5-4385-873d-7424a5a48704 · outbound

This paper cites an unresolved cited work.

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-15T15:18:34.151227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e5e75d26-bc5c-4f8a-9faa-c9d35927332a · outbound

This paper cites Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering.

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-08-15T15:18:32.777667Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 35e08189-ca59-4bed-a912-d38878f827d5 · outbound

This paper cites Question answering tasks can be categorized into two sub-classes: general question answering.

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering Question answering tasks can be categorized into two sub-classes: general question answering

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:18:34.008752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 4b7c67c9-5333-4908-bea1-ab0793e54bbb · outbound

This paper cites an unresolved cited work.

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-15T15:18:33.790738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3d7a92a3-f95e-4d14-b38d-54c3dbcd5022 · outbound

This paper cites an unresolved cited work.

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-15T15:18:33.737503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ddd0352f-d329-43a4-b7b9-36f33940714e · outbound

This paper cites Other notable works include HiPRAG [16], R1-Searcher [17],β-GRPO [18] and ZeroSearch [19].

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering Other notable works include HiPRAG [16], R1-Searcher [17],β-GRPO [18] and ZeroSearch [19]

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:18:33.919163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 6a1c4d74-df08-4865-a403-ac8aeb21d9e6 · outbound

This paper cites It breaks the query into subqueries and uses the search engine to find relevant information for each subquery.

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering It breaks the query into subqueries and uses the search engine to find relevant information for each subquery

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:18:33.907173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7d6973ba-ed4a-4946-ac31-ec3aa29151b2 · outbound

This paper cites To address open- domain question answering, several approaches have been pro- posed such as IRCoT [12], ReAct [13], CoRAG [14] and Deep- RAG [15].

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering To address open- domain question answering, several approaches have been pro- posed such as IRCoT [12], ReAct [13], CoRAG [14] and Deep- RAG [15]

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:18:33.930887Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1696f787-0fe3-4c5b-988b-ae3fc01db7b9 · outbound

This paper cites an unresolved cited work.

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering Unresolved cited work

Reference 11

Resolution: verified exact; raw_fallback, observed 2026-08-15T15:18:32.758757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 40ad0145-03b6-4269-9faa-0516eabb44ce · outbound

This paper cites Our method demonstrates strong performance across all individual QA tasks, achieving the highest or second-highest accuracy in each case.

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering Our method demonstrates strong performance across all individual QA tasks, achieving the highest or second-highest accuracy in each case

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:18:33.870887Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 03875023-44e1-4e33-af1c-251fe8a45083 · outbound

This paper cites React: Synergizing reasoning and acting in lan- guage models,.

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering React: Synergizing reasoning and acting in lan- guage models,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T15:18:31.581363Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c11b59ba-1d81-4d33-83e8-abb0fd423356 · outbound

This paper cites Chain-of-retrieval augmented generation,.

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering Chain-of-retrieval augmented generation,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T15:18:31.623203Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 46bf08c9-f5e2-469b-b9b6-19e7626daece · outbound

This paper cites an unresolved cited work.

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering Unresolved cited work

Reference 15

Resolution: unresolved; raw_fallback, observed 2026-08-15T15:18:33.724648Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d007d617-e279-4ed0-9ad8-1b0608e16c9f · outbound

This paper cites Query rewriting in retrieval-augmented large language models,.

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering Query rewriting in retrieval-augmented large language models,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T15:18:31.309438Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 160a102a-80a5-4a36-a6eb-86f9b9db8d78 · outbound

This paper cites Query rewriting in retrieval-augmented large language models,.

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering Query rewriting in retrieval-augmented large language models,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:18:33.712796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 18b766a7-bc67-49a9-9ac7-21e6981ac4c1 · outbound

This paper cites Enhancing conversational search: Large language model-aided informative query rewriting,.

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering Enhancing conversational search: Large language model-aided informative query rewriting,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:18:33.700143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 6ac71a1f-fca4-4272-af94-1937b5e9fef3 · outbound

This paper cites Genrewrite: Query rewriting via large language models,.

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering Genrewrite: Query rewriting via large language models,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-15T15:18:31.323570Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dd87d576-bfab-42d0-a70d-9580f44ac2bd · outbound

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

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering A survey on rag meeting llms: Towards retrieval-augmented large language models,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T15:18:31.327152Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 05cd11da-10c0-4d31-ae22-75a9a2cc334b · outbound

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

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering Retrieval-Augmented Generation for Large Language Models: A Survey

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T15:18:31.331716Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 112393b6-b894-47fb-ae4e-b62dd0a8a321 · outbound

This paper cites Search arena: Analyzing search-augmented llms,.

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering Search arena: Analyzing search-augmented llms,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T15:18:31.336689Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c3ffc81d-c851-4ad8-8b13-7e26a2168176 · outbound

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

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering Search-r1: Training LLMs to reason and leverage search engines with reinforcement learning,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:18:33.632556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 4819a6ec-52c2-420c-9762-b1bce67d0638 · outbound

This paper cites Reading wikipedia to answer open-domain questions,.

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering Reading wikipedia to answer open-domain questions,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:18:33.481575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1ee7b268-a5fb-4283-a796-6ff001cf18c7 · outbound

This paper cites Natural questions: a benchmark for question answering re- search,.

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering Natural questions: a benchmark for question answering re- search,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-15T15:18:31.349376Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f472628-da4b-4499-befe-2e8fb26b9526 · outbound

This paper cites Hotpotqa: A dataset for diverse, explain- able multi-hop question answering,.

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering Hotpotqa: A dataset for diverse, explain- able multi-hop question answering,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-15T15:18:31.353640Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df5e6053-c1b0-4794-b8c6-ff5a01ee43fd · outbound

This paper cites Interleaving retrieval with chain-of-thought reasoning for knowledge-intensive multi-step questions,.

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering Interleaving retrieval with chain-of-thought reasoning for knowledge-intensive multi-step questions,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-15T15:18:31.472074Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b0874470-1770-4bf5-ba64-321d3c9d2a42 · outbound

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

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering Retrieval-augmented generation for knowledge- intensive nlp tasks,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:18:33.011137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e0e71ff3-a091-41ba-9d37-36d2691e7b79 · outbound

This paper cites Interleaving retrieval with chain-of-thought reasoning for knowledge-intensive multi-step questions,.

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering Interleaving retrieval with chain-of-thought reasoning for knowledge-intensive multi-step questions,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:18:32.825913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1de2e671-8044-4ef8-a932-c0bc50991fb7 · outbound

This paper cites DeepRAG: Thinking to Retrieve Step by Step for Large Language Models.

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering DeepRAG: Thinking to Retrieve Step by Step for Large Language Models

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-15T15:18:31.628021Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2b4c0ad-6d8a-410f-b8b5-dd8641b17c6e · outbound

This paper cites HiPRAG: Hierarchical Process Rewards for Efficient Agentic Retrieval Augmented Generation.

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering HiPRAG: Hierarchical Process Rewards for Efficient Agentic Retrieval Augmented Generation

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-15T15:18:31.632553Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af918295-ef49-446b-953d-48360de8b94c · outbound

This paper cites These methods improve the LLM’s performance through fine-tuning on QA task data or the LLM’s own responses that lead to correct answers.

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering These methods improve the LLM’s performance through fine-tuning on QA task data or the LLM’s own responses that lead to correct answers

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:18:33.894969Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation aa1686a9-ff08-4237-b1b1-3c0a981f2ac3 · outbound

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

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-15T15:18:31.637676Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf6cd40c-80da-4dd9-a751-b00b966b5429 · outbound

This paper cites Search wisely: Mitigating sub-optimal agentic searches by reducing uncertainty,.

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering Search wisely: Mitigating sub-optimal agentic searches by reducing uncertainty,

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-15T15:18:31.642685Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3dba222f-c9ec-4d87-9a30-9c278a199448 · outbound

This paper cites ZeroSearch: Incentivize the Search Capability of LLMs without Searching.

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering ZeroSearch: Incentivize the Search Capability of LLMs without Searching

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-15T15:18:31.646292Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 62327d5c-db5a-452e-8324-66d03c1d95e4 · outbound

This paper cites The knowledge corpus used for the search engine retrieval mechanism consists of the 2018 Wikipedia documents.

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering The knowledge corpus used for the search engine retrieval mechanism consists of the 2018 Wikipedia documents

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:18:33.883043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 0e13173c-8d35-423e-aab4-6c41356a548f · outbound

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

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering Natural questions: A benchmark for question answering research,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:18:33.435281Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3267bd66-f71b-4226-a4f5-3fc4853d2ce1 · outbound

This paper cites TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension,.

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:18:33.184986Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2a95049e-6879-4785-8aac-127fb2c5538a · outbound

This paper cites When not to trust language models: Investigating effectiveness of parametric and non-parametric memories,.

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering When not to trust language models: Investigating effectiveness of parametric and non-parametric memories,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:18:33.107698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 70f0c9e8-c4aa-4afb-8003-b8a40c92a2fe · outbound

This paper cites HotpotQA: A dataset for diverse, explainable multi-hop question answering,.

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering HotpotQA: A dataset for diverse, explainable multi-hop question answering,

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-15T15:18:31.813916Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6c665b2d-db3c-4500-b4bd-62ed583e52c0 · outbound

This paper cites Constructing a multi-hop QA dataset for comprehensive evaluation of reasoning steps,.

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering Constructing a multi-hop QA dataset for comprehensive evaluation of reasoning steps,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:18:33.089527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 65c088f5-8e9e-4507-b238-ee2b955a3aed · outbound

This paper cites MuSiQue: Multihop questions via single-hop question com- position,.

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering MuSiQue: Multihop questions via single-hop question com- position,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:18:33.077119Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 0442af27-d72d-49fe-977c-a29746a10c32 · outbound

This paper cites Measuring and narrowing the compositionality gap in language models,.

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering Measuring and narrowing the compositionality gap in language models,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:18:33.065426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 4b4adcaf-a38d-4609-8d6f-51ab13f6176a · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models,.

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering Chain-of-thought prompting elicits reasoning in large language models,

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-15T15:18:31.935561Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 01d072cc-dd0c-409a-956e-ed964a6458ad · outbound

This paper cites Search-o1: Agentic search-enhanced large reasoning models,.

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering Search-o1: Agentic search-enhanced large reasoning models,

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-15T15:18:31.948645Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 02344f6c-ba49-47f5-bde9-bd1321c10509 · outbound

This paper cites Scaling instruction-finetuned language models,.

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering Scaling instruction-finetuned language models,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:18:32.811413Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation fadc9261-a8ba-4f87-9cc8-35dcbc56aa96 · outbound

This paper cites Deepseek-r1 incentivizes reasoning in llms through reinforcement learning,.

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering Deepseek-r1 incentivizes reasoning in llms through reinforcement learning,

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-15T15:18:32.074972Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4f842e2-4e55-43fd-a6fb-b257f8a5dcde · outbound

This paper cites Scaling Relationship on Learning Mathematical Reasoning with Large Language Models.

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering Scaling Relationship on Learning Mathematical Reasoning with Large Language Models

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-15T15:18:32.188086Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5dd41c45-ee91-46c8-803f-b6c98dd88402 · outbound

This paper cites Qwen2.5 Technical Report.

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering Qwen2.5 Technical Report

Reference 51

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Observation be5a3e0d-8783-4aa1-8b24-18eb77d0c8a7 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering Proximal Policy Optimization Algorithms

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-15T15:18:32.196979Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c8a0fdf-a986-46a2-ba1c-0176c3b4bd36 · outbound

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

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering Dense passage retrieval for open-domain question answering,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:18:32.790964Z

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Observation 7306ab8a-30a1-4e58-8ee6-136a931d7b97 · outbound

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

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering Text Embeddings by Weakly-Supervised Contrastive Pre-training

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-15T15:18:32.203639Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e227c02-0c45-46cc-94a0-4d88219369c3 · outbound

This paper cites Available: https://aclanthology.org/P17-1147/.

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering Available: https://aclanthology.org/P17-1147/

Reference 1611

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:18:33.119696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f1c923f4-1801-4eba-a8c6-970258a3abd7 · outbound

This paper cites Available: https://aclanthology.org/Q19-1026/.

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering Available: https://aclanthology.org/Q19-1026/

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:18:33.422235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1e09a285-1a47-411c-9463-8b252cfaf97c · outbound

This paper cites Available: https://doi.org/10.48550/arXiv.2212.

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering Available: https://doi.org/10.48550/arXiv.2212

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-15T15:18:32.208109Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation e5e75d26-bc5c-4f8a-9faa-c9d35927332a · inbound