Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Grounding by Trying: LLMs with Reinforcement Learning-Enhanced Retrieval
As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2410.23214.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T23:26:56.659188Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-01T00:55:12.120217Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation d1116e0c-96ad-44d0-b721-58b0f7237495 · inbound
Supervising the search process produces reliable and generalizable information-seeking agents cites this paper.
Supervising the search process produces reliable and generalizable information-seeking agents Grounding by Trying: LLMs with Reinforcement Learning-Enhanced Retrieval
Reference 22
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T02:22:25.332332Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 923bf86b-f26c-41eb-a7ff-d45de05d90b5 · inbound
Deep Research Agents: A Systematic Examination And Roadmap cites this paper.
Deep Research Agents: A Systematic Examination And Roadmap Grounding by Trying: LLMs with Reinforcement Learning-Enhanced Retrieval
Reference 42
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T23:26:56.659188Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5487c94a-887e-4904-a508-d404b5b6fd49 · inbound
Harnessing the Power of Reinforcement Learning for Language-Model-Based Information Retriever via Query-Document Co-Augmentation cites this paper.
Harnessing the Power of Reinforcement Learning for Language-Model-Based Information Retriever via Query-Document Co-Augmentation Grounding by Trying: LLMs with Reinforcement Learning-Enhanced Retrieval
Reference 2020
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T23:20:32.987960Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c468b4b9-3e7a-4abe-a988-a13c8704446a · inbound
Foundational Design Principles and Patterns for Building Robust and Adaptive GenAI-Native Systems cites this paper.
Foundational Design Principles and Patterns for Building Robust and Adaptive GenAI-Native Systems Grounding by Trying: LLMs with Reinforcement Learning-Enhanced Retrieval
Reference 24
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T22:11:52.598296Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation aa0ae670-76ed-47e1-9c13-6d8bbf177cd0 · inbound
When Adaptive Rewards Hurt: Causal Probing and the Switching-Stability Dilemma in LLM-Guided LEO Satellite Scheduling cites this paper.
When Adaptive Rewards Hurt: Causal Probing and the Switching-Stability Dilemma in LLM-Guided LEO Satellite Scheduling Grounding by Trying: LLMs with Reinforcement Learning-Enhanced Retrieval
Reference 22
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T13:05:01.450957Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation da40d7a6-0b9a-425a-bb82-a3c5c5face30 · inbound
$S^3$-R1: Learning to Retrieve and Answer Step-by-Step with Synthetic Data cites this paper.
$S^3$-R1: Learning to Retrieve and Answer Step-by-Step with Synthetic Data Grounding by Trying: LLMs with Reinforcement Learning-Enhanced Retrieval
Reference 9
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T16:46:06.821269Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation f67d7d85-af3b-4baa-a4b4-57ec6559e215 · inbound
$S^3$-R1: Learning to Retrieve and Answer Step-by-Step with Synthetic Data cites this paper.
$S^3$-R1: Learning to Retrieve and Answer Step-by-Step with Synthetic Data Grounding by Trying: LLMs with Reinforcement Learning-Enhanced Retrieval
Reference 9
- Resolution
- verified exact
- arxiv_id, observed 2026-07-01T00:55:12.122183Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.