Paper Citation Record · LEDGER

Making Large Language Models Better Reasoners with Alignment

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2309.02144.

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

pith.paper-citation-record.v1
2309.02144 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:41:10.817384Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-24T03:23:49.571699Z

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 008d00d3-9ae8-46b2-ba24-9919bac0ca63 · inbound

MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning cites this paper.

MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning Making Large Language Models Better Reasoners with Alignment

Reference 50

Resolution: metadata mismatch; arxiv_id, observed 2026-05-17T23:46:39.501439Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c313013d-4410-4ccf-98b7-131ae7229763 · inbound

MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models cites this paper.

MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models Making Large Language Models Better Reasoners with Alignment

Reference 72

Resolution: verified exact; arxiv_id, observed 2026-05-13T10:07:53.846541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c6c64d2c-807a-429a-851a-8d08a585b2ff · inbound

Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations cites this paper.

Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations Making Large Language Models Better Reasoners with Alignment

Reference 84

Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T22:34:15.818946Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 613359b5-d014-40fd-9e4a-830c05c623ec · inbound

DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models cites this paper.

DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models Making Large Language Models Better Reasoners with Alignment

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-05-24T03:23:49.574772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c3fdd2a5-3450-469e-98a9-35b0d2c12475 · inbound

Smaug: Fixing Failure Modes of Preference Optimisation with DPO-Positive cites this paper.

Smaug: Fixing Failure Modes of Preference Optimisation with DPO-Positive Making Large Language Models Better Reasoners with Alignment

Reference 77

Resolution: verified exact; arxiv_id, observed 2026-05-17T23:04:44.448082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 621b2a06-61ae-4422-ba01-feebf281b7a3 · inbound

DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model cites this paper.

DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model Making Large Language Models Better Reasoners with Alignment

Reference 174

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:36:26.926578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 35313d04-0742-432d-a711-1b42a4ae0b89 · inbound

Empowering LLMs in Task-Oriented Dialogues: A Domain-Independent Multi-Agent Framework and Fine-Tuning Strategy cites this paper.

Empowering LLMs in Task-Oriented Dialogues: A Domain-Independent Multi-Agent Framework and Fine-Tuning Strategy Making Large Language Models Better Reasoners with Alignment

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T15:41:10.817384Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b3030e9e-68e9-480a-9bf1-70470f0c9d15 · inbound

RACE-Align: Retrieval-Augmented and Chain-of-Thought Enhanced Preference Alignment for Large Language Models cites this paper.

RACE-Align: Retrieval-Augmented and Chain-of-Thought Enhanced Preference Alignment for Large Language Models Making Large Language Models Better Reasoners with Alignment

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T11:21:42.079243Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2d7d8e3f-7de2-4906-9ce9-1d2397627f19 · inbound

Does Math Reasoning Improve General LLM Capabilities? Understanding Transferability of LLM Reasoning cites this paper.

Does Math Reasoning Improve General LLM Capabilities? Understanding Transferability of LLM Reasoning Making Large Language Models Better Reasoners with Alignment

Reference 259

Resolution: verified exact; arxiv_id, observed 2026-05-19T01:01:10.458067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 979b9f9f-457d-40bb-b45b-00bc72a5ce05 · inbound

Spectral Origins of the Self-Correction Blind Spot in Autoregressive Generation cites this paper.

Spectral Origins of the Self-Correction Blind Spot in Autoregressive Generation Making Large Language Models Better Reasoners with Alignment

Reference 25

Resolution: unresolved; no resolver link, observed 2026-07-14T15:30:23.485228Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 14f08006-1968-4052-8dcd-74d19b9a8000 · inbound

Lost in Context: Addressing Context Anxiety in Large Language Models cites this paper.

Lost in Context: Addressing Context Anxiety in Large Language Models Making Large Language Models Better Reasoners with Alignment

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-02T12:47:56.368969Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.