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Paper Citation Record · LEDGER

Revolutionizing Pharma: Unveiling the AI and LLM Trends in the Pharmaceutical Industry

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2401.10273.

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

pith.paper-citation-record.v1
2401.10273 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T14:29:25.620806Z

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

5
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 a17714ae-3522-484f-93ec-d7f4aad26d1c · inbound

Regulator-Manufacturer AI Agents Modeling: Mathematical Feedback-Driven Multi-Agent LLM Framework cites this paper.

Regulator-Manufacturer AI Agents Modeling: Mathematical Feedback-Driven Multi-Agent LLM Framework Revolutionizing Pharma: Unveiling the AI and LLM Trends in the Pharmaceutical Industry

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-12T14:29:25.620806Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:29:25.620806Z digest=sha256:f3e70522563e63d21a648024abacd26d47d530aad060ebbbff5c3efa2443207c

Observation 6452ed37-1111-465f-a49c-dccdebb4b155 · inbound

From Text to Discovery: How Large Language Models Are Reshaping Research Across Scientific and Humanistic Disciplines cites this paper.

From Text to Discovery: How Large Language Models Are Reshaping Research Across Scientific and Humanistic Disciplines Revolutionizing Pharma: Unveiling the AI and LLM Trends in the Pharmaceutical Industry

Reference 84

Resolution
verified exact
arxiv_id, observed 2026-06-27T17:31:07.415597Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-27T17:22:30.784806Z digest=sha256:b5de5c3f6d51e06258154c611c1b1cacee9729773fb72565b73f328489f9756e

Observation feb64256-7ae1-4b21-baeb-13d08f33836a · inbound

From Text to Discovery: How Large Language Models Are Reshaping Research Across Scientific and Humanistic Disciplines cites this paper.

From Text to Discovery: How Large Language Models Are Reshaping Research Across Scientific and Humanistic Disciplines Revolutionizing Pharma: Unveiling the AI and LLM Trends in the Pharmaceutical Industry

Reference 84

Resolution
unresolved
no resolver link, observed 2026-08-02T12:03:31.935961Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T12:03:31.935961Z digest=sha256:8bb6930bd261f933633133fe45a5e6dce3c6447c3174143bdbad54ac8d4b88a3