Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Achieving >97% on GSM8K: Deeply Understanding the Problems Makes LLMs Better Solvers for Math Word Problems
As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2404.14963.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T16:39:23.878516Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T22:47:25.855113Z
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 32bcaa5c-0895-47bd-ba0c-c1ec1d526e2a · inbound
DataComp-LM: In search of the next generation of training sets for language models cites this paper.
DataComp-LM: In search of the next generation of training sets for language models Achieving >97% on GSM8K: Deeply Understanding the Problems Makes LLMs Better Solvers for Math Word Problems
Reference 219
- Resolution
- verified exact
- arxiv_id, observed 2026-05-17T22:58:17.376433Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 1ee2b976-ab5e-4217-909f-7f503434e9f2 · inbound
Enhancing the Reasoning Capabilities of Small Language Models via Solution Guidance Fine-Tuning cites this paper.
Enhancing the Reasoning Capabilities of Small Language Models via Solution Guidance Fine-Tuning Achieving >97% on GSM8K: Deeply Understanding the Problems Makes LLMs Better Solvers for Math Word Problems
Reference 32
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T16:39:23.878516Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3bc16c09-7d89-4bf9-be69-3758410c8410 · inbound
Leveraging Metamemory Agent for Enhanced Data-Free Code Generation in Large Language Models cites this paper.
Leveraging Metamemory Agent for Enhanced Data-Free Code Generation in Large Language Models Achieving >97% on GSM8K: Deeply Understanding the Problems Makes LLMs Better Solvers for Math Word Problems
Reference 52
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T20:35:28.683538Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2aa3e9ba-73b0-476d-9f36-219b6f159f2b · inbound
ScoreFlow: Mastering LLM Agent Workflows via Score-based Preference Optimization cites this paper.
ScoreFlow: Mastering LLM Agent Workflows via Score-based Preference Optimization Achieving >97% on GSM8K: Deeply Understanding the Problems Makes LLMs Better Solvers for Math Word Problems
Reference 47
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T22:57:12.828725Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3420d96c-d2ef-4ba2-8100-5139a36d6b13 · inbound
Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning cites this paper.
Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning Achieving >97% on GSM8K: Deeply Understanding the Problems Makes LLMs Better Solvers for Math Word Problems
Reference 22
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:24:50.840520Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation df1099cd-a899-4471-9e7e-b93e71de27c0 · inbound
Revisiting Overthinking in Long Chain-of-Thought from the Perspective of Self-Doubt cites this paper.
Revisiting Overthinking in Long Chain-of-Thought from the Perspective of Self-Doubt Achieving >97% on GSM8K: Deeply Understanding the Problems Makes LLMs Better Solvers for Math Word Problems
Reference 22
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:49:11.691171Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation dd28f020-592d-4f50-a337-ffa99d744497 · inbound
A Survey on Large Language Models for Mathematical Reasoning cites this paper.
A Survey on Large Language Models for Mathematical Reasoning Achieving >97% on GSM8K: Deeply Understanding the Problems Makes LLMs Better Solvers for Math Word Problems
Reference 113
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:14:47.608760Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bf3d2dc8-ec3f-4f87-a879-840526c9c941 · inbound
DuaShepherd: Integrating Stepwise Correctness and Potential Rewards for Mathematical Reasoning cites this paper.
DuaShepherd: Integrating Stepwise Correctness and Potential Rewards for Mathematical Reasoning Achieving >97% on GSM8K: Deeply Understanding the Problems Makes LLMs Better Solvers for Math Word Problems
Reference 37
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T23:35:36.314813Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a7347745-e66c-4bba-9d84-dc542ece43ea · inbound
Robust Knowledge Editing via Explicit Reasoning Chains for Distractor-Resilient Multi-Hop QA cites this paper.
Robust Knowledge Editing via Explicit Reasoning Chains for Distractor-Resilient Multi-Hop QA Achieving >97% on GSM8K: Deeply Understanding the Problems Makes LLMs Better Solvers for Math Word Problems
Reference 27
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T12:37:47.063326Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ab469978-3ffd-433b-8226-b96bc3807d16 · inbound
Artificial Intelligence for Mathematical Reasoning: An Integrated Survey of Language Models, Neuro-symbolic Systems, and Verified Discovery cites this paper.
Artificial Intelligence for Mathematical Reasoning: An Integrated Survey of Language Models, Neuro-symbolic Systems, and Verified Discovery Achieving >97% on GSM8K: Deeply Understanding the Problems Makes LLMs Better Solvers for Math Word Problems
Reference 55
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T22:47:25.856979Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 85b8aca7-90ed-4794-b701-79f6778060f0 · inbound
Artificial Intelligence for Mathematical Reasoning: An Integrated Survey of Language Models, Neuro-symbolic Systems, and Verified Discovery cites this paper.
Artificial Intelligence for Mathematical Reasoning: An Integrated Survey of Language Models, Neuro-symbolic Systems, and Verified Discovery Achieving >97% on GSM8K: Deeply Understanding the Problems Makes LLMs Better Solvers for Math Word Problems
Reference 55
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T12:05:07.267016Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a1a67a53-9e59-41b1-ac60-86a35eb639c8 · inbound
Soft Guidance Starts to Outperform CoT Prompting as LLMs Improve cites this paper.
Soft Guidance Starts to Outperform CoT Prompting as LLMs Improve Achieving >97% on GSM8K: Deeply Understanding the Problems Makes LLMs Better Solvers for Math Word Problems
Reference 2
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T17:02:19.680880Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.