Paper Citation Record · LEDGER

How Much Can RAG Help the Reasoning of LLM?

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2410.02338.

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

pith.paper-citation-record.v1
2410.02338 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:21:51.700097Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

1
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 620cfbf7-8a45-4eee-af99-677a67704333 · inbound

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

Search-o1: Agentic Search-Enhanced Large Reasoning Models How Much Can RAG Help the Reasoning of LLM?

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-13T17:36:27.564269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0ba83370-ab54-4fcb-93ff-40c743543e19 · inbound

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

WebThinker: Empowering Large Reasoning Models with Deep Research Capability How Much Can RAG Help the Reasoning of LLM?

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-16T19:14:25.353941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4f52b676-c5f4-4405-be4d-6affaf9de1f8 · inbound

Leaps Beyond the Seen: Reinforced Reasoning Augmented Generation for Clinical Notes cites this paper.

Leaps Beyond the Seen: Reinforced Reasoning Augmented Generation for Clinical Notes How Much Can RAG Help the Reasoning of LLM?

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-07T11:21:51.700097Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b913be1-8284-46c4-859f-e4e1a9452d8a · inbound

EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora cites this paper.

EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora How Much Can RAG Help the Reasoning of LLM?

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T22:44:01.630597Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa545ba3-ee82-440d-9f46-59ba167bee5f · inbound

Software Engineering for Large Language Models: Research Status, Challenges and the Road Ahead cites this paper.

Software Engineering for Large Language Models: Research Status, Challenges and the Road Ahead How Much Can RAG Help the Reasoning of LLM?

Reference 209

Resolution: unresolved; no resolver link, observed 2026-08-06T21:36:38.681531Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 45453462-2a67-4761-b6f0-c51aa657ca71 · inbound

LLMs Should Express Uncertainty Explicitly cites this paper.

LLMs Should Express Uncertainty Explicitly How Much Can RAG Help the Reasoning of LLM?

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-10T22:10:48.096603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 288a9a2d-b437-457d-99af-a3b6d5751521 · inbound

LLMs Should Express Uncertainty Explicitly cites this paper.

LLMs Should Express Uncertainty Explicitly How Much Can RAG Help the Reasoning of LLM?

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-15T07:15:11.854038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation cea842fd-24a0-4129-8272-4b9ac249e735 · inbound

Conjecture and Inquiry: Quantifying Software Performance Requirements via Interactive Retrieval-Augmented Preference Elicitation cites this paper.

Conjecture and Inquiry: Quantifying Software Performance Requirements via Interactive Retrieval-Augmented Preference Elicitation How Much Can RAG Help the Reasoning of LLM?

Reference 60

Resolution: verified exact; arxiv_id, observed 2026-05-09T21:23:25.877751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9336ae2d-0141-4df7-b6e1-2cb8e078fd0b · inbound

RAG over Thinking Traces Can Improve Reasoning Tasks cites this paper.

RAG over Thinking Traces Can Improve Reasoning Tasks How Much Can RAG Help the Reasoning of LLM?

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-05-12T00:41:25.391636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 77557933-b217-49ef-a0f4-29bf33f91999 · inbound

Learning to Reason by Analogy via Retrieval-Augmented Reinforcement Fine-Tuning cites this paper.

Learning to Reason by Analogy via Retrieval-Augmented Reinforcement Fine-Tuning How Much Can RAG Help the Reasoning of LLM?

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-07-03T15:28:33.910054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d0071f79-82ea-448f-9f5b-7b8a105eb24b · inbound

Revisiting Chain-of-Thought Reasoning under Limited Supervision: Semi-supervised Chain-of-Thought Learning cites this paper.

Revisiting Chain-of-Thought Reasoning under Limited Supervision: Semi-supervised Chain-of-Thought Learning How Much Can RAG Help the Reasoning of LLM?

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-07-03T20:08:54.901845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.