Paper Citation Record · LEDGER

Advancing the Scientific Method with Large Language Models: From Hypothesis to Discovery

As of 10 August 2026, this Paper Citation Record lists 10 of 10 outbound references and 5 inbound Pith citation observations for arXiv:2505.16477.

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

pith.paper-citation-record.v1
2505.16477 v1

Coverage vector

measured 10 of 10 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:02:39.106196Z

measured 15 of 15 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 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-01T05:22:38.232552Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T03:06:29.178249Z

Reference resolution

10 of 10 outbound references displayed

verified exact0
verified fuzzy1
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8b24c18f-b9c5-4dcf-a01c-aaa46821fd23 · outbound

This paper cites & Zhang, Y.

Advancing the Scientific Method with Large Language Models: From Hypothesis to Discovery & Zhang, Y

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T15:02:38.301206Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 16495d07-30ed-43e8-b1e3-a6486a470a17 · outbound

This paper cites A Survey of Deep Learning for Scientific Discovery.

Advancing the Scientific Method with Large Language Models: From Hypothesis to Discovery A Survey of Deep Learning for Scientific Discovery

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T15:02:38.445357Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0eeeaadc-08dc-4a66-9c9d-d3821bb1025b · outbound

This paper cites How Predictable Are Large Language Model Capabilities? A Case Study on BIG-bench.

Advancing the Scientific Method with Large Language Models: From Hypothesis to Discovery How Predictable Are Large Language Model Capabilities? A Case Study on BIG-bench

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-07T15:02:38.559536Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b393a8a-fcd1-4bbb-8438-390f1d3f401a · outbound

This paper cites LLM-SR: Scientific Equation Discovery via Programming with Large Language Models.

Advancing the Scientific Method with Large Language Models: From Hypothesis to Discovery LLM-SR: Scientific Equation Discovery via Programming with Large Language Models

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-07T15:02:38.598202Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c09b6d77-518d-41cf-9750-315933f3d92a · outbound

This paper cites an unresolved cited work.

Advancing the Scientific Method with Large Language Models: From Hypothesis to Discovery Unresolved cited work

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-07T15:02:38.627818Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6db53a27-f065-4ef9-b636-e230bb382638 · outbound

This paper cites Simulation Intelligence: Towards a New Generation of Scientific Methods.

Advancing the Scientific Method with Large Language Models: From Hypothesis to Discovery Simulation Intelligence: Towards a New Generation of Scientific Methods

Reference 109

Resolution: unresolved; no resolver link, observed 2026-08-07T15:02:38.719265Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a9aed053-a45f-4e13-aa44-1d9657529b80 · outbound

This paper cites Assessing and Understanding Creativity in Large Language Models.

Advancing the Scientific Method with Large Language Models: From Hypothesis to Discovery Assessing and Understanding Creativity in Large Language Models

Reference 130

Resolution: unresolved; no resolver link, observed 2026-08-07T15:02:38.831145Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a6bbc18d-4ef1-4d9a-a1c6-ac7d17e7e13d · outbound

This paper cites an unresolved cited work.

Advancing the Scientific Method with Large Language Models: From Hypothesis to Discovery Unresolved cited work

Reference 167

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:02:39.562978Z

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 3196706d-3c01-45fe-a031-98a204c2d92f · outbound

This paper cites Negative.

Advancing the Scientific Method with Large Language Models: From Hypothesis to Discovery Negative

Reference 185

Resolution: unresolved; no resolver link, observed 2026-08-07T15:02:38.997190Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation de68183b-0290-4066-bb51-a99d98580ce2 · outbound

This paper cites Awkward wording. Rephrase.

Advancing the Scientific Method with Large Language Models: From Hypothesis to Discovery Awkward wording. Rephrase

Reference 205

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:02:39.402656Z

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.

Pith citing papers

Observation ef73d227-5ea0-4c87-bd2d-2b5da5742700 · 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 Advancing the Scientific Method with Large Language Models: From Hypothesis to Discovery

Reference 223

Resolution: verified exact; arxiv_id, observed 2026-05-19T05:17:06.132344Z

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 f7da91b1-f1dd-456a-b1ba-5e84fe50dbd7 · inbound

AInstein: Can LLMs Solve Research Problems From Parametric Memory Alone? cites this paper.

AInstein: Can LLMs Solve Research Problems From Parametric Memory Alone? Advancing the Scientific Method with Large Language Models: From Hypothesis to Discovery

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-18T09:31:11.211183Z

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 bda187b7-cfe0-4c8e-a580-25701d004aea · inbound

Can LLMs Use Linguistic Uncertainty Markers to Reliably Reflect Intrinsic Confidence? cites this paper.

Can LLMs Use Linguistic Uncertainty Markers to Reliably Reflect Intrinsic Confidence? Advancing the Scientific Method with Large Language Models: From Hypothesis to Discovery

Reference 101

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T12:23:24.418814Z

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 aebf2e5f-9c47-4c5e-b5c2-bc39f9d572ad · inbound

Quantifying Faithful Confidence Expression in Large Reasoning Models cites this paper.

Quantifying Faithful Confidence Expression in Large Reasoning Models Advancing the Scientific Method with Large Language Models: From Hypothesis to Discovery

Reference 67

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T03:06:29.180653Z

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 d58aba64-7420-4011-b6ec-8772e00c6469 · inbound

Reinforcement Learning with Metacognitive Feedback Elicits Faithful Uncertainty Expression in LLMs cites this paper.

Reinforcement Learning with Metacognitive Feedback Elicits Faithful Uncertainty Expression in LLMs Advancing the Scientific Method with Large Language Models: From Hypothesis to Discovery

Reference 130

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T10:35:42.074451Z

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.