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Paper Citation Record · LEDGER

Enhancing Large Language Model Performance To Answer Questions and Extract Information More Accurately

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2402.01722.

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

pith.paper-citation-record.v1
2402.01722 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:22:48.233252Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T03:09:28.884922Z

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 7e4cd5a1-43e0-4c78-9d6e-7bfbefa58ee6 · inbound

KaFT: Knowledge-aware Fine-tuning for Boosting LLMs' Domain-specific Question-Answering Performance cites this paper.

KaFT: Knowledge-aware Fine-tuning for Boosting LLMs' Domain-specific Question-Answering Performance Enhancing Large Language Model Performance To Answer Questions and Extract Information More Accurately

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-07T15:22:48.233252Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:22:48.233252Z digest=sha256:8a60db3e99477a0c47130708e5623b2ef84e4c7261817249a89a7371b5c6cb1a

Observation fc6e08ff-a687-433f-ba27-895d8c430f65 · inbound

Deep Research Agents: A Systematic Examination And Roadmap cites this paper.

Deep Research Agents: A Systematic Examination And Roadmap Enhancing Large Language Model Performance To Answer Questions and Extract Information More Accurately

Reference 133

Resolution
unresolved
no resolver link, observed 2026-08-06T23:26:57.012419Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:26:57.012419Z digest=sha256:f3af9c51630594a9e4b7984edfa3b2b9e2078a005719cd77f9c9a3795c8d760a

Observation 48e57b3c-118a-489a-9c57-1cdfb721107a · inbound

SelfAug: Mitigating Catastrophic Forgetting in Retrieval-Augmented Generation via Distribution Self-Alignment cites this paper.

SelfAug: Mitigating Catastrophic Forgetting in Retrieval-Augmented Generation via Distribution Self-Alignment Enhancing Large Language Model Performance To Answer Questions and Extract Information More Accurately

Reference 101

Resolution
unresolved
no resolver link, observed 2026-08-05T10:34:46.856881Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T10:34:46.856881Z digest=sha256:50d85ba59379f75e58da16ce2c2810aa938d0d3ef8d764c38ed92d2b74a756d0

Observation 3840225a-e185-447c-8e7e-3466c0a981a4 · inbound

BioHarness: Substrate-Aware Evidence Assembly for Biomedical Question Answering across Literature, Knowledge Bases, and Biological Atlases cites this paper.

BioHarness: Substrate-Aware Evidence Assembly for Biomedical Question Answering across Literature, Knowledge Bases, and Biological Atlases Enhancing Large Language Model Performance To Answer Questions and Extract Information More Accurately

Reference 13

Resolution
verified exact
arxiv_id, observed 2026-07-04T03:09:28.887205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-26T18:34:09.474000Z digest=sha256:bcf7fb37b17a87f666dc33455752da2eca8d8f74a5c35a8c454072ece811ea7d

Observation 00996242-e823-477e-870d-1e93aee5a5b1 · inbound

Less is More: Lightweight Prompt Compression for Question Answering Applications on Edge Devices cites this paper.

Less is More: Lightweight Prompt Compression for Question Answering Applications on Edge Devices Enhancing Large Language Model Performance To Answer Questions and Extract Information More Accurately

Reference 16

Resolution
metadata mismatch
arxiv_id, observed 2026-07-01T08:55:34.890257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-07-01T08:49:28.538659Z digest=sha256:c29cdacab5b847baca2206db54b24631047dd97c83fae252c62819a52629bc9c