Paper Citation Record · LEDGER

Reasoning Strategies in Large Language Models: Can They Follow, Prefer, and Optimize?

As of 18 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2507.11423.

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

pith.paper-citation-record.v1
2507.11423 v2

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:12:12.638442Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

33 of 33 outbound references displayed

verified exact3
verified fuzzy3
unresolved27
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 80fa1598-2438-4c1b-a255-6913bf0c93b6 · outbound

This paper cites online" 'onlinestring :=.

Reasoning Strategies in Large Language Models: Can They Follow, Prefer, and Optimize? online" 'onlinestring :=

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T17:12:10.003644Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7912f430-77f1-4414-a777-93539f8fe85d · outbound

This paper cites write newline.

Reasoning Strategies in Large Language Models: Can They Follow, Prefer, and Optimize? write newline

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T17:12:10.081942Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1b46ae84-0625-4f4a-9832-1137c4f1e8cb · outbound

This paper cites Hewett, Mojan Javaheripi, Piero Kauffmann, James R.

Reasoning Strategies in Large Language Models: Can They Follow, Prefer, and Optimize? Hewett, Mojan Javaheripi, Piero Kauffmann, James R

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:15.747712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d3e73f11-bd35-4cd6-a167-823c9e5d9a6d · outbound

This paper cites an unresolved cited work.

Reasoning Strategies in Large Language Models: Can They Follow, Prefer, and Optimize? Unresolved cited work

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T17:12:10.259368Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6cf7ede1-3445-48b1-b5ee-49a38fd7f672 · outbound

This paper cites Large Language Monkeys: Scaling Inference Compute with Repeated Sampling.

Reasoning Strategies in Large Language Models: Can They Follow, Prefer, and Optimize? Large Language Monkeys: Scaling Inference Compute with Repeated Sampling

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T17:12:10.335440Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 05e0cbc9-c1a8-4fa1-bed3-57aeca1a56fd · outbound

This paper cites People will agree what I think: Investigating LLM's False Consensus Effect.

Reasoning Strategies in Large Language Models: Can They Follow, Prefer, and Optimize? People will agree what I think: Investigating LLM's False Consensus Effect

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T17:12:10.435494Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 043c324a-8bea-4184-8cd7-1e7dcf495a3b · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Reasoning Strategies in Large Language Models: Can They Follow, Prefer, and Optimize? Training Verifiers to Solve Math Word Problems

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T17:12:10.537779Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 45f56f5b-fc5a-493f-938f-548f4dc37a86 · outbound

This paper cites A Systematic Comparison of Syllogistic Reasoning in Humans and Language Models.

Reasoning Strategies in Large Language Models: Can They Follow, Prefer, and Optimize? A Systematic Comparison of Syllogistic Reasoning in Humans and Language Models

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T17:12:10.620274Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bb596875-2202-47a2-90d9-9750964e7378 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Reasoning Strategies in Large Language Models: Can They Follow, Prefer, and Optimize? DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T17:12:10.704936Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06dccd0e-48bd-4e19-b6bb-c44b11409ec6 · outbound

This paper cites an unresolved cited work.

Reasoning Strategies in Large Language Models: Can They Follow, Prefer, and Optimize? Unresolved cited work

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T17:12:10.804850Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 79db456b-bdc2-4c2b-bc50-f999142a8b93 · outbound

This paper cites an unresolved cited work.

Reasoning Strategies in Large Language Models: Can They Follow, Prefer, and Optimize? Unresolved cited work

Reference 11

Resolution: verified exact; doi, observed 2026-08-06T17:12:13.023852Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b8d8e523-b589-4a19-b280-f9a1e00e6955 · outbound

This paper cites an unresolved cited work.

Reasoning Strategies in Large Language Models: Can They Follow, Prefer, and Optimize? Unresolved cited work

Reference 12

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:12:15.511447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f194b4cf-04e5-4daa-ae52-12397f3ee7e5 · outbound

This paper cites an unresolved cited work.

Reasoning Strategies in Large Language Models: Can They Follow, Prefer, and Optimize? Unresolved cited work

Reference 13

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:12:15.199994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation cb1fb8de-e48b-4175-b802-3def664383a9 · outbound

This paper cites ZebraLogic: On the Scaling Limits of LLMs for Logical Reasoning.

Reasoning Strategies in Large Language Models: Can They Follow, Prefer, and Optimize? ZebraLogic: On the Scaling Limits of LLMs for Logical Reasoning

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T17:12:11.173121Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fb534dc7-09db-4d84-8ffd-a8d40b4f78b5 · outbound

This paper cites an unresolved cited work.

Reasoning Strategies in Large Language Models: Can They Follow, Prefer, and Optimize? Unresolved cited work

Reference 15

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:12:14.864125Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3b356d2d-d71a-4936-b246-4607be5c5755 · outbound

This paper cites Embers of Autoregression: Understanding Large Language Models Through the Problem They are Trained to Solve.

Reasoning Strategies in Large Language Models: Can They Follow, Prefer, and Optimize? Embers of Autoregression: Understanding Large Language Models Through the Problem They are Trained to Solve

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T17:12:11.347497Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 44cb72d4-9ef9-4f91-b7b0-8a93257b26ce · outbound

This paper cites an unresolved cited work.

Reasoning Strategies in Large Language Models: Can They Follow, Prefer, and Optimize? Unresolved cited work

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T17:12:11.433051Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cbaddd2a-73e2-4f9b-a4bb-59294dc5c35c · outbound

This paper cites an unresolved cited work.

Reasoning Strategies in Large Language Models: Can They Follow, Prefer, and Optimize? Unresolved cited work

Reference 18

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:12:14.594062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 095a10d6-6a97-48d5-b7f0-c37129b20106 · outbound

This paper cites an unresolved cited work.

Reasoning Strategies in Large Language Models: Can They Follow, Prefer, and Optimize? Unresolved cited work

Reference 19

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:12:14.365462Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 443cc4b1-0fde-42d1-96f3-1fe20b333c47 · outbound

This paper cites Do Language Models Exhibit the Same Cognitive Biases in Problem Solving as Human Learners?.

Reasoning Strategies in Large Language Models: Can They Follow, Prefer, and Optimize? Do Language Models Exhibit the Same Cognitive Biases in Problem Solving as Human Learners?

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T17:12:11.672549Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 43d71121-9bf3-4a26-abc6-13a748106daf · outbound

This paper cites Language Models Prefer What They Know: Relative Confidence Estimation via Confidence Preferences.

Reasoning Strategies in Large Language Models: Can They Follow, Prefer, and Optimize? Language Models Prefer What They Know: Relative Confidence Estimation via Confidence Preferences

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T17:12:11.740039Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e6d8643b-e552-4196-8d9d-daf544af67e5 · outbound

This paper cites an unresolved cited work.

Reasoning Strategies in Large Language Models: Can They Follow, Prefer, and Optimize? Unresolved cited work

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T17:12:11.770104Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 86bc81b9-8e51-45a4-8e6e-aa7fb88592e0 · outbound

This paper cites an unresolved cited work.

Reasoning Strategies in Large Language Models: Can They Follow, Prefer, and Optimize? Unresolved cited work

Reference 23

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:12:14.224698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 380bd802-0f95-4fde-a777-0e94aacfb1a9 · outbound

This paper cites Johnson-Laird.

Reasoning Strategies in Large Language Models: Can They Follow, Prefer, and Optimize? Johnson-Laird

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T17:12:11.948355Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 167f74af-e83c-4ba9-949d-1452e1541c93 · outbound

This paper cites Le, Ed H.

Reasoning Strategies in Large Language Models: Can They Follow, Prefer, and Optimize? Le, Ed H

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T17:12:12.031545Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a887bf77-e9c2-4fa3-9385-5d831ae220c6 · outbound

This paper cites Chain-of-Thought Reasoning Without Prompting.

Reasoning Strategies in Large Language Models: Can They Follow, Prefer, and Optimize? Chain-of-Thought Reasoning Without Prompting

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T17:12:12.085092Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c66d500-88b2-4e90-94d2-5e585ca78118 · outbound

This paper cites Chi, Quoc V.

Reasoning Strategies in Large Language Models: Can They Follow, Prefer, and Optimize? Chi, Quoc V

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:14.093140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation de6377dc-8689-4e05-aabc-166eb680c2f4 · outbound

This paper cites an unresolved cited work.

Reasoning Strategies in Large Language Models: Can They Follow, Prefer, and Optimize? Unresolved cited work

Reference 28

Resolution: verified exact; raw_fallback, observed 2026-08-06T17:12:13.320656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 03aec223-acbd-470d-b8c2-ab38cd31c679 · outbound

This paper cites an unresolved cited work.

Reasoning Strategies in Large Language Models: Can They Follow, Prefer, and Optimize? Unresolved cited work

Reference 29

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:12:13.895302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation cf2f2d25-b60f-4cbd-ade0-d45f3878cebc · outbound

This paper cites an unresolved cited work.

Reasoning Strategies in Large Language Models: Can They Follow, Prefer, and Optimize? Unresolved cited work

Reference 30

Resolution: verified exact; doi, observed 2026-08-06T17:12:12.792696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation dc5c0bc3-df2f-4ea1-970c-d10f2862fd18 · outbound

This paper cites Generative Verifiers: Reward Modeling as Next-Token Prediction.

Reasoning Strategies in Large Language Models: Can They Follow, Prefer, and Optimize? Generative Verifiers: Reward Modeling as Next-Token Prediction

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T17:12:12.536426Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a96c604d-d742-4f51-a587-9c321247b1c8 · outbound

This paper cites Le, and Ed H.

Reasoning Strategies in Large Language Models: Can They Follow, Prefer, and Optimize? Le, and Ed H

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:13.744520Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8960f8e4-c561-49ab-9052-981e696077e2 · outbound

This paper cites Bridging Internal Probability and Self-Consistency for Effective and Efficient LLM Reasoning.

Reasoning Strategies in Large Language Models: Can They Follow, Prefer, and Optimize? Bridging Internal Probability and Self-Consistency for Effective and Efficient LLM Reasoning

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-06T17:12:12.638442Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.