Pith. sign in

REVIEW 2 cited by

CiteAssist: A System for Automated Preprint Citation and BibTeX Generation

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2407.03192 v1 pith:OA3SPHFM submitted 2024-07-03 cs.DL cs.CL

classification cs.DLcs.CL
keywords citeassistpreprintsystemannotationsbibtexcitationresearchersaccess
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

We present CiteAssist, a system to automate the generation of BibTeX entries for preprints, streamlining the process of bibliographic annotation. Our system extracts metadata, such as author names, titles, publication dates, and keywords, to create standardized annotations within the document. CiteAssist automatically attaches the BibTeX citation to the end of a PDF and links it on the first page of the document so other researchers gain immediate access to the correct citation of the article. This method promotes platform flexibility by ensuring that annotations remain accessible regardless of the repository used to publish or access the preprint. The annotations remain available even if the preprint is viewed externally to CiteAssist. Additionally, the system adds relevant related papers based on extracted keywords to the preprint, providing researchers with additional publications besides those in related work for further reading. Researchers can enhance their preprints organization and reference management workflows through a free and publicly available web interface.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. RWGBench: Evaluating Scholarly Positioning in Related Work Generation

    cs.DL 2026-05 unverdicted novelty 7.0 of 10

    RWGBench measures related-work generation by citation choices, and shows citation-focused metrics expose failures that text-similarity and LLM-judge scores miss.

  2. WaveVerify: A Novel Audio Watermarking Framework for Media Authentication and Combatting Deepfakes

    cs.CR 2025-07 conditional novelty 6.0 of 10

    WaveVerify embeds audio watermarks with a FiLM-based generator and extracts them with a Mixture-of-Experts detector, reporting zero bit error and high localization under common distortions.

Pith tools