Paper Citation Record · LEDGER

OmniScience: A Domain-Specialized LLM for Scientific Reasoning and Discovery

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2503.17604.

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

pith.paper-citation-record.v1
2503.17604 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:18:43.053755Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-25T03:06:34.043739Z

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 7ca5859d-0d10-40e7-8891-887696e7c03b · inbound

Make Still Further Progress: Chain of Thoughts for Tabular Data Leaderboard cites this paper.

Make Still Further Progress: Chain of Thoughts for Tabular Data Leaderboard OmniScience: A Domain-Specialized LLM for Scientific Reasoning and Discovery

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-15T20:18:43.053755Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 398dcbeb-5310-4dbe-bf77-71a92d4be9d6 · inbound

Toward Scientific Reasoning in LLMs: Training from Expert Discussions via Reinforcement Learning cites this paper.

Toward Scientific Reasoning in LLMs: Training from Expert Discussions via Reinforcement Learning OmniScience: A Domain-Specialized LLM for Scientific Reasoning and Discovery

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T14:16:35.092269Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4633f76a-d45a-4b1b-aa3c-8ee02793b761 · inbound

Evolving Roles of LLMs in Scientific Innovation: Assistant, Collaborator, Scientist, and Evaluator cites this paper.

Evolving Roles of LLMs in Scientific Innovation: Assistant, Collaborator, Scientist, and Evaluator OmniScience: A Domain-Specialized LLM for Scientific Reasoning and Discovery

Reference 139

Resolution: metadata mismatch; arxiv_id, observed 2026-05-19T05:17:06.364222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b31c13fb-7e25-4bed-a014-f516bd5f5501 · inbound

History Rhymes: Accelerating LLM Reinforcement Learning with RhymeRL cites this paper.

History Rhymes: Accelerating LLM Reinforcement Learning with RhymeRL OmniScience: A Domain-Specialized LLM for Scientific Reasoning and Discovery

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T17:01:18.358564Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 95134791-18b3-4072-a888-65beb49dda52 · inbound

RLBoost: Harvesting Preemptible Resources for Cost-Efficient Reinforcement Learning on LLMs cites this paper.

RLBoost: Harvesting Preemptible Resources for Cost-Efficient Reinforcement Learning on LLMs OmniScience: A Domain-Specialized LLM for Scientific Reasoning and Discovery

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-18T05:30:55.219555Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation be282e5f-6f4c-4c9e-a7fb-a5772cdfc344 · inbound

Self-Refining Topology Optimization via an LLM-Based Multi-Agent Framework cites this paper.

Self-Refining Topology Optimization via an LLM-Based Multi-Agent Framework OmniScience: A Domain-Specialized LLM for Scientific Reasoning and Discovery

Reference 57

Resolution: verified exact; arxiv_id, observed 2026-05-25T03:06:34.046847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.