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Paper Citation Record · LEDGER

TinyGSM: achieving >80% on GSM8k with small language models

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 20 inbound Pith citation observations for arXiv:2312.09241.

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

pith.paper-citation-record.v1
2312.09241 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 20 of 20 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T22:57:01.806123Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
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  • malformed identifier0
  • metadata mismatch0

External citation measurements

1
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation f4de4c5f-8337-42fa-a51e-01a43efb4122 · inbound

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models cites this paper.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models TinyGSM: achieving >80% on GSM8k with small language models

Reference 114

Resolution
unresolved
no resolver link, observed 2026-08-11T22:57:01.806123Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T22:57:01.806123Z digest=sha256:1ff458b196a8293f4fb4cd995268b6c22bdedcc786c7ff4cdc972d83925c6dbc

Observation 70c1638b-bb12-497c-909d-ae58ab738edd · inbound

Proposing and solving olympiad geometry with guided tree search cites this paper.

Proposing and solving olympiad geometry with guided tree search TinyGSM: achieving >80% on GSM8k with small language models

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-11T15:47:53.570902Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T15:47:53.570902Z digest=sha256:de11870f23c938111ce0529127c9c4768834671a23d5717131bbab7c4a8b971b

Observation 99640ccc-657f-44d5-8058-9f77df5c207f · inbound

Chasing Progress, Not Perfection: Revisiting Strategies for End-to-End LLM Plan Generation cites this paper.

Chasing Progress, Not Perfection: Revisiting Strategies for End-to-End LLM Plan Generation TinyGSM: achieving >80% on GSM8k with small language models

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-11T15:47:21.267762Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T15:47:21.267762Z digest=sha256:2d25d290a14f3a31d2dbd538d356acad18a1e458f2486177cf207d65b9800b61

Observation 72361e13-963d-4b7a-b88c-1dee1cede574 · inbound

System-2 Mathematical Reasoning via Enriched Instruction Tuning cites this paper.

System-2 Mathematical Reasoning via Enriched Instruction Tuning TinyGSM: achieving >80% on GSM8k with small language models

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-11T05:58:55.066821Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T05:58:55.066821Z digest=sha256:2921e686ff3ceeb03e12d432bca5c4135bcb0777331f4598c8a6960851d652e8

Observation 671813f5-9b31-4143-ad8a-e3807df93b9d · inbound

Small Language Models (SLMs) Can Still Pack a Punch: A survey (updated 2026) cites this paper.

Small Language Models (SLMs) Can Still Pack a Punch: A survey (updated 2026) TinyGSM: achieving >80% on GSM8k with small language models

Reference 74

Resolution
verified exact
arxiv_id, observed 2026-05-23T05:52:37.294007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-23T05:47:48.488826Z digest=sha256:026c988d80fd5efbeaf0e58b808dc1d660caff4fb624fc2517076951aab12dc0

Observation 2a94db03-1fd9-44d4-b52f-aaccfd86e1d6 · inbound

Online Knowledge Distillation with Reward Guidance cites this paper.

Online Knowledge Distillation with Reward Guidance TinyGSM: achieving >80% on GSM8k with small language models

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-07T14:27:49.328172Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:27:49.328172Z digest=sha256:9a93726e88433fd4e0af5f848a5b4d9a2c7da4acd7135c5e0a7fac84511e8ad3

Observation b5f09a3d-156e-4e19-989c-d8df0b841ea7 · inbound

Tag-Evol: Achieving Efficient Instruction Evolving via Tag Injection cites this paper.

Tag-Evol: Achieving Efficient Instruction Evolving via Tag Injection TinyGSM: achieving >80% on GSM8k with small language models

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-07T12:39:05.502062Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:39:05.502062Z digest=sha256:5acd49f099a5c2df79f0f4594c7142134f1ec295750eff3c5f5dcb5f0806a015

Observation cd0b74e3-e7f8-4123-afa6-7e9b08e39471 · inbound

A Survey on Large Language Models for Mathematical Reasoning cites this paper.

A Survey on Large Language Models for Mathematical Reasoning TinyGSM: achieving >80% on GSM8k with small language models

Reference 59

Resolution
unresolved
no resolver link, observed 2026-08-07T05:14:47.355966Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:14:47.355966Z digest=sha256:dfe797c878642ef4aa61d4ac03286504ac28406505531b8148319881f54c5fa6

Observation 4f1c0be2-c49c-4bb4-b9bc-83c557d278e0 · inbound

SLM-Bench: A Comprehensive Benchmark of Small Language Models on Environmental Impacts--Extended Version cites this paper.

SLM-Bench: A Comprehensive Benchmark of Small Language Models on Environmental Impacts--Extended Version TinyGSM: achieving >80% on GSM8k with small language models

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-05T17:57:17.092837Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T17:57:17.092837Z digest=sha256:2c7858ffd2002503e1f0a5bc86e74824e58030b6f6804d1dee27a7dcea209393

Observation bea759e0-67c0-4c0e-93dc-3a52fe4a97a1 · inbound

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation cites this paper.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation TinyGSM: achieving >80% on GSM8k with small language models

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-05T13:35:36.663056Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:35:36.663056Z digest=sha256:eb047080257193134924ecebd682524067a0ca43ed6446248aa8a5b28f685924

Observation 6046303a-eb30-49b4-bc19-96d503b11dda · inbound

Don't Pass@k: A Bayesian Framework for Large Language Model Evaluation cites this paper.

Don't Pass@k: A Bayesian Framework for Large Language Model Evaluation TinyGSM: achieving >80% on GSM8k with small language models

Reference 52

Resolution
verified exact
arxiv_id, observed 2026-05-18T10:06:13.910982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-18T10:04:39.223895Z digest=sha256:78f8963d5ac9a9749337763a3a226f1f0ae93694b0e87551ed9f4383dc638f4a

Observation 15ef4678-cb6b-4a42-86c5-49ba8403a77c · inbound

Self-Supervised Bootstrapping of Action-Predictive Embodied Reasoning cites this paper.

Self-Supervised Bootstrapping of Action-Predictive Embodied Reasoning TinyGSM: achieving >80% on GSM8k with small language models

Reference 60

Resolution
verified exact
arxiv_id, observed 2026-05-21T14:04:11.970652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-21T14:03:48.795572Z digest=sha256:66aff193b179a402e8a9de0095629b4544f82197d4ed35b3ceedf217988cf97b

Observation 5d47610c-a9b7-46a5-85c3-645ba74c12c6 · inbound

Looped Diffusion Language Models cites this paper.

Looped Diffusion Language Models TinyGSM: achieving >80% on GSM8k with small language models

Reference 37

Resolution
verified exact
arxiv_id, observed 2026-06-29T23:14:01.129009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-06-29T23:13:12.343355Z digest=sha256:d9cddd550cd4030695754fa814b95b2753c3fbc55e0a9f20e4f098f287e622e5

Observation bd00998c-4f2f-4b07-b4a8-4bb16cd850fe · inbound

Dense2MoE: Pushing the Pareto Frontier of On-Device LLMs via Unified Pruning and Upcycling cites this paper.

Dense2MoE: Pushing the Pareto Frontier of On-Device LLMs via Unified Pruning and Upcycling TinyGSM: achieving >80% on GSM8k with small language models

Reference 11

Resolution
verified exact
arxiv_id, observed 2026-06-29T19:43:55.011998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-06-29T19:34:17.270161Z digest=sha256:4a114bd41ef74b9be1106f767dda4169eb3f2a115d93f756515b1bb5e9fc414d

Observation 9c2e99aa-f0d8-4a44-a97f-6f108bb97f73 · inbound

OCC-RAG: Optimal Cognitive Core for Faithful Question Answering cites this paper.

OCC-RAG: Optimal Cognitive Core for Faithful Question Answering TinyGSM: achieving >80% on GSM8k with small language models

Reference 4

Resolution
verified exact
arxiv_id, observed 2026-06-28T19:12:34.353666Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-06-28T19:02:40.530363Z digest=sha256:81375c59924d9f9e22d0e3e0d64d058745cc17b1cb4bf92beee66726a4dfcdc7

Observation 23eb022b-3af4-4a57-ace2-f961cc82e938 · inbound

BlockGen: Flexible Blockwise Sequence Modeling with Hybrid Samplers cites this paper.

BlockGen: Flexible Blockwise Sequence Modeling with Hybrid Samplers TinyGSM: achieving >80% on GSM8k with small language models

Reference 138

Resolution
metadata mismatch
arxiv_id, observed 2026-07-01T22:16:16.983935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-06-28T15:29:08.917412Z digest=sha256:e4bd87ee958dddb6a86edd567a35b5b3bf5db6a118eae29efc4547b9c240e58c

Observation 45769b1a-260f-4e16-a74a-edb410f85207 · inbound

Which Models Are Our Models Built On? Auditing Invisible Dependencies in Modern LLMs cites this paper.

Which Models Are Our Models Built On? Auditing Invisible Dependencies in Modern LLMs TinyGSM: achieving >80% on GSM8k with small language models

Reference 36

Resolution
verified exact
arxiv_id, observed 2026-07-03T10:37:56.557828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-06-27T09:57:14.328157Z digest=sha256:a36f69a2018cf3aef8035322f72fcd1bc5e30d28188520ba56f93f9032ad7f7d

Observation d244bc3b-717b-4dba-900d-be1d31ff86d5 · inbound

Counsel: A Meta-Evaluation Dataset for Agentic Tasks cites this paper.

Counsel: A Meta-Evaluation Dataset for Agentic Tasks TinyGSM: achieving >80% on GSM8k with small language models

Reference 27

Resolution
metadata mismatch
arxiv_id, observed 2026-07-04T06:49:38.218954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-06-26T14:07:59.446478Z digest=sha256:5ac39c1b6158619170940b583081c4f73e784051c1d6ff0f2d77f63ad29b64db

Observation 7d9fdb11-0947-46fb-bed3-9ce98b21e159 · inbound

Posterior Refinement: Fast Language Generation via Any-Order Flow Maps cites this paper.

Posterior Refinement: Fast Language Generation via Any-Order Flow Maps TinyGSM: achieving >80% on GSM8k with small language models

Reference 55

Resolution
verified exact
arxiv_id, observed 2026-07-04T17:09:58.835783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-06-25T23:55:07.047233Z digest=sha256:4b2c5e6aba64ffbba1fdf9373ae6850d06a71b5b71c80fe0fd85ccffcac6de8b

Observation 67b7a7d1-dd36-4169-ac13-3b223f207cfb · inbound

Training with (Swap) Regret Loss in a Single-Layer Self-Attention Model: A Case Study on the Probability Simplex cites this paper.

Training with (Swap) Regret Loss in a Single-Layer Self-Attention Model: A Case Study on the Probability Simplex TinyGSM: achieving >80% on GSM8k with small language models

Reference 72

Resolution
unresolved
no resolver link, observed 2026-07-31T23:51:55.169989Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-31T23:51:55.169989Z digest=sha256:9a75adc5f2ec5d3e0824eafdd1770142538cb14ad2329679be0e7831c9375e73