Pith. sign in

Paper Citation Record · LEDGER

Mining Hidden Thoughts from Texts: Evaluating Continual Pretraining with Synthetic Data for LLM Reasoning

As of 16 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2505.10182.

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

pith.paper-citation-record.v1
2505.10182 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:19:11.606064Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

37 of 37 outbound references displayed

  • verified exact1
  • verified fuzzy7
  • unresolved29
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 10935a2c-d8b7-430b-a008-5edbf15726fb · outbound

This paper cites OpenAI o1 System Card.

Mining Hidden Thoughts from Texts: Evaluating Continual Pretraining with Synthetic Data for LLM Reasoning OpenAI o1 System Card

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-15T21:19:11.410604Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:19:11.410604Z digest=sha256:bbc7a4239f97ca755af0215edcb49adb6aedcf940da13580d26ff318b26c0a30

Observation acc68ba5-366c-49b2-b73d-6287cbcc7701 · outbound

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

Mining Hidden Thoughts from Texts: Evaluating Continual Pretraining with Synthetic Data for LLM Reasoning DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-15T21:19:11.416696Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:19:11.416696Z digest=sha256:e0b63af788d11478e58a4a83d9a6739375d5afe07aea3a21bcd52b8de5f9246f

Observation bd2dbeb3-e572-494f-8fb7-a312f26a32ab · outbound

This paper cites Aime, February 2024.

Mining Hidden Thoughts from Texts: Evaluating Continual Pretraining with Synthetic Data for LLM Reasoning Aime, February 2024

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:19:12.296180Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T21:19:11.422492Z digest=sha256:7437a5597f55fb487971874f674af13c142781e5b5a41721452c0d147430a07e

Observation 1f87ae0c-bc24-4968-92de-088dedb01013 · outbound

This paper cites GPQA: A Graduate-Level Google-Proof Q&A Benchmark.

Mining Hidden Thoughts from Texts: Evaluating Continual Pretraining with Synthetic Data for LLM Reasoning GPQA: A Graduate-Level Google-Proof Q&A Benchmark

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-15T21:19:11.428367Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:19:11.428367Z digest=sha256:042f3a0535c4a6b7d591caf2732aa64ae6d73bf17425b54371d8245f8181ac13

Observation dc5ec225-280e-4a51-b52a-356d3af8af6e · outbound

This paper cites s1: Simple test-time scaling.

Mining Hidden Thoughts from Texts: Evaluating Continual Pretraining with Synthetic Data for LLM Reasoning s1: Simple test-time scaling

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-15T21:19:11.434569Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:19:11.434569Z digest=sha256:d824d30a0245ce37ac7b0faa1b9be3554a5264f07b45d4c13b42d8a1f295c188

Observation 9578c198-18fb-4377-b5d1-59258b3a66c5 · outbound

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

Mining Hidden Thoughts from Texts: Evaluating Continual Pretraining with Synthetic Data for LLM Reasoning DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-15T21:19:11.440751Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:19:11.440751Z digest=sha256:0772b8033fc714916219c478a25de094b6741f10d8ae36da2ca899abe39a2626

Observation 660c3e1f-2ddd-466f-909b-c386d3d1f956 · outbound

This paper cites Openwebmath: An open dataset of high-quality mathematical web text.

Mining Hidden Thoughts from Texts: Evaluating Continual Pretraining with Synthetic Data for LLM Reasoning Openwebmath: An open dataset of high-quality mathematical web text

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:19:12.281867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T21:19:11.447022Z digest=sha256:23f158496453ef7cebf16d9ecd69f12256f5fafe89f63f73af90499c21ea01b9

Observation 550d1d9b-df95-4b4e-a4fd-e6576a352641 · outbound

This paper cites The Pile: An 800GB Dataset of Diverse Text for Language Modeling.

Mining Hidden Thoughts from Texts: Evaluating Continual Pretraining with Synthetic Data for LLM Reasoning The Pile: An 800GB Dataset of Diverse Text for Language Modeling

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-15T21:19:11.452909Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:19:11.452909Z digest=sha256:d460be866f8ebabf2c6008d60a0d7703a90945311ae18724619360b6f14f6c04

Observation d709fb50-d897-4e9d-a236-3701e5e567ae · outbound

This paper cites Reasoning to learn from latent thoughts.

Mining Hidden Thoughts from Texts: Evaluating Continual Pretraining with Synthetic Data for LLM Reasoning Reasoning to learn from latent thoughts

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-15T21:19:11.458743Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:19:11.458743Z digest=sha256:f540b6eb1c13ef76aaa2422fc76584cf0ae4001aaf7233fdff9554c5f4d89d63

Observation 57a129c0-6cf9-432e-a53b-600507421aee · outbound

This paper cites Gemini 2.5: Our most intelligent ai model, March 2025.

Mining Hidden Thoughts from Texts: Evaluating Continual Pretraining with Synthetic Data for LLM Reasoning Gemini 2.5: Our most intelligent ai model, March 2025

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:19:12.266849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T21:19:11.463844Z digest=sha256:bd0c5d2a739535ee4de6f839c7d99b9bf7a57fe7ee9fb3c936a8965cfbcbb3fd

Observation 04ed7a77-aa28-4a07-881e-16d59dbfad03 · outbound

This paper cites Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen - Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen.

Mining Hidden Thoughts from Texts: Evaluating Continual Pretraining with Synthetic Data for LLM Reasoning Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen - Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-15T21:19:11.469033Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:19:11.469033Z digest=sha256:dc23dcd4f0cb69ba606fcad3a6b1e2c4f698dc9fb9e7c18200c5de519880a9c5

Observation 3fbe24fd-43fa-4606-846f-2527260adec6 · outbound

This paper cites Decoupled weight decay regularization.

Mining Hidden Thoughts from Texts: Evaluating Continual Pretraining with Synthetic Data for LLM Reasoning Decoupled weight decay regularization

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-15T21:19:11.474949Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:19:11.474949Z digest=sha256:a8ec929fc6e1edf3ba5c67c947019558a8e2b4ab549a0de2bc03ea29c126914c

Observation 77fc7e87-93c2-4f4a-bac7-1bfede272d7e · outbound

This paper cites Measuring massive multitask language understanding.

Mining Hidden Thoughts from Texts: Evaluating Continual Pretraining with Synthetic Data for LLM Reasoning Measuring massive multitask language understanding

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-15T21:19:11.480520Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:19:11.480520Z digest=sha256:cf3f6b40f1f2096fb74e4033a4da3f53465a510a0f10bf1fbbf3f444306a7c7f

Observation 81d7a7f2-650f-4dbf-92b1-44a82584f7f7 · outbound

This paper cites Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models.

Mining Hidden Thoughts from Texts: Evaluating Continual Pretraining with Synthetic Data for LLM Reasoning Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-15T21:19:11.486677Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:19:11.486677Z digest=sha256:b9bdbb3d944ddb2f8b3f5a2b72125b223da526445260e22c9713ec6201008785

Observation bbf07aed-0d64-4444-8cdf-b401d97a86fc · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Mining Hidden Thoughts from Texts: Evaluating Continual Pretraining with Synthetic Data for LLM Reasoning Evaluating Large Language Models Trained on Code

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-15T21:19:11.491911Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:19:11.491911Z digest=sha256:dbc9cccea16bb4e402c459f26ac60a7a88b0a757f278735a652a82a063081b3a

Observation ca4dc4ef-4fa9-4bbc-80d2-c85a969d4c5a · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Mining Hidden Thoughts from Texts: Evaluating Continual Pretraining with Synthetic Data for LLM Reasoning Training Verifiers to Solve Math Word Problems

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-15T21:19:11.497074Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:19:11.497074Z digest=sha256:a1176f895ca0db7cb47b3a3eb1bb8db5184d347228a41f445629596dbd7695dc

Observation c1f83a44-f7f0-4fbd-8491-feaa3eb82968 · outbound

This paper cites Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?.

Mining Hidden Thoughts from Texts: Evaluating Continual Pretraining with Synthetic Data for LLM Reasoning Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-15T21:19:11.502454Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:19:11.502454Z digest=sha256:2211047e805341f5df419fa859ac04940eddb52bc4a4341083f66cd74ed4ad83

Observation b65333f7-e15f-4063-a65d-c67fbcec0e07 · outbound

This paper cites Tulu 3: Pushing Frontiers in Open Language Model Post-Training.

Mining Hidden Thoughts from Texts: Evaluating Continual Pretraining with Synthetic Data for LLM Reasoning Tulu 3: Pushing Frontiers in Open Language Model Post-Training

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-15T21:19:11.507629Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:19:11.507629Z digest=sha256:bbe21c30872f59a8e7ec53ba58b90193f574a9c6f24573ba609f87343b29351b

Observation e8bfabb7-6399-4b70-bf12-6879d01ef5dc · outbound

This paper cites Optimizing Language Models for Inference Time Objectives using Reinforcement Learning.

Mining Hidden Thoughts from Texts: Evaluating Continual Pretraining with Synthetic Data for LLM Reasoning Optimizing Language Models for Inference Time Objectives using Reinforcement Learning

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-15T21:19:11.512505Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:19:11.512505Z digest=sha256:8be9c9f13cee0f222542c1416468926176d854041924d2a345ab7adeb5af59fb

Observation cb9c93bd-9054-481c-a540-25336f6abc1a · outbound

This paper cites Le, Ed H.

Mining Hidden Thoughts from Texts: Evaluating Continual Pretraining with Synthetic Data for LLM Reasoning Le, Ed H

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-15T21:19:11.519182Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:19:11.519182Z digest=sha256:576121a0bf78033ca3dcb4a4ac1f81cd09eb6e38ff6bd8d3cc32998db2b7a919

Observation a52351be-85b8-4af4-a86d-1257b5f78018 · outbound

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

Mining Hidden Thoughts from Texts: Evaluating Continual Pretraining with Synthetic Data for LLM Reasoning Large Language Monkeys: Scaling Inference Compute with Repeated Sampling

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-15T21:19:11.523971Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:19:11.523971Z digest=sha256:c67ba71ab4991998e808a134047bd20855b50f184a40d63297dcc5a2afea2cb3

Observation 22da0afe-aeca-4a94-a9b2-2585ab5bb26b · outbound

This paper cites Self-refine: Iterative refinement with self-feedback.

Mining Hidden Thoughts from Texts: Evaluating Continual Pretraining with Synthetic Data for LLM Reasoning Self-refine: Iterative refinement with self-feedback

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-15T21:19:11.529227Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:19:11.529227Z digest=sha256:0a28acc21ad92418746e94080df829b3339477317fd75488ccece8e69bab543b

Observation 6e1002fe-0b2d-432c-b59b-fa5056047efa · outbound

This paper cites Reflexion: language agents with verbal reinforcement learning.

Mining Hidden Thoughts from Texts: Evaluating Continual Pretraining with Synthetic Data for LLM Reasoning Reflexion: language agents with verbal reinforcement learning

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-15T21:19:11.533971Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:19:11.533971Z digest=sha256:bb35ef83127934111866240757e482682a7dfbe6a06c028f519ce0239db6c934

Observation c0f1f884-ce5c-4c95-a944-61b2c418c69c · outbound

This paper cites Teaching large language models to self-debug.

Mining Hidden Thoughts from Texts: Evaluating Continual Pretraining with Synthetic Data for LLM Reasoning Teaching large language models to self-debug

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:19:12.197895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T21:19:11.538739Z digest=sha256:35e9b58cb374091cba63018cce3dd8648767e9e60f83b8b7feff3c77a338cc38

Observation 38161081-f239-48aa-9d05-d36faf6491e7 · outbound

This paper cites Tree of thoughts: Deliberate problem solving with large language models.

Mining Hidden Thoughts from Texts: Evaluating Continual Pretraining with Synthetic Data for LLM Reasoning Tree of thoughts: Deliberate problem solving with large language models

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:19:12.180488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T21:19:11.543299Z digest=sha256:f8d69ce9322cd568e6277850d2d0b51211809bbf49c2c85de079e2d46b9b38ed

Observation ec9364ca-766b-46b8-b0f4-f1f4135196c9 · outbound

This paper cites Mutual Reasoning Makes Smaller LLMs Stronger Problem-Solvers.

Mining Hidden Thoughts from Texts: Evaluating Continual Pretraining with Synthetic Data for LLM Reasoning Mutual Reasoning Makes Smaller LLMs Stronger Problem-Solvers

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-15T21:19:11.548905Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:19:11.548905Z digest=sha256:9d8dcd00501371945a1894657a40aa73d6655187f473297d3e1a9353a3ce5ddd

Observation d1fffe49-1e3e-4eba-bac4-7575d412aaae · outbound

This paper cites an unresolved cited work.

Mining Hidden Thoughts from Texts: Evaluating Continual Pretraining with Synthetic Data for LLM Reasoning Unresolved cited work

Reference 27

Resolution
unresolved
raw_fallback, observed 2026-08-15T21:19:12.164558Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T21:19:11.554899Z digest=sha256:60beb3c06461098374181c3e614f1058483583a84e0ae1cbd5fd9ba0d6f3c02c

Observation 1bee59f1-e5df-4a0f-8ae0-ddfa45bd44a5 · outbound

This paper cites If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents.

Mining Hidden Thoughts from Texts: Evaluating Continual Pretraining with Synthetic Data for LLM Reasoning If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-15T21:19:11.560898Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:19:11.560898Z digest=sha256:30b33982ec097909b0253204310bd1a9bf04f0bdbf8b7e3196ead6c83ec4f7dd

Observation ae71f412-ec7a-4448-8b74-119cf80e816c · outbound

This paper cites To Code, or Not To Code? Exploring Impact of Code in Pre-training.

Mining Hidden Thoughts from Texts: Evaluating Continual Pretraining with Synthetic Data for LLM Reasoning To Code, or Not To Code? Exploring Impact of Code in Pre-training

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-15T21:19:11.565842Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:19:11.565842Z digest=sha256:2276c9ffa08d46077d38539df5388c3b04ec5422d639df06a3bda45f019c5efe

Observation 3683302c-82a6-4084-b460-6c3e6781b608 · outbound

This paper cites How Does Code Pretraining Affect Language Model Task Performance?.

Mining Hidden Thoughts from Texts: Evaluating Continual Pretraining with Synthetic Data for LLM Reasoning How Does Code Pretraining Affect Language Model Task Performance?

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-15T21:19:11.570810Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:19:11.570810Z digest=sha256:c8f2e17995a39f128471880078f751ea65f35954aa8864c650d727563825d2c3

Observation fbefbe97-23e9-49be-ab8d-c833b318209d · outbound

This paper cites Code Pretraining Improves Entity Tracking Abilities of Language Models.

Mining Hidden Thoughts from Texts: Evaluating Continual Pretraining with Synthetic Data for LLM Reasoning Code Pretraining Improves Entity Tracking Abilities of Language Models

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-15T21:19:11.576093Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:19:11.576093Z digest=sha256:b8c2a73ffd3824c39fc6b777386957840d2cbb2eafb028365c17723674663679

Observation d826bc08-d91e-479b-a6b7-b75cae84e445 · outbound

This paper cites an unresolved cited work.

Mining Hidden Thoughts from Texts: Evaluating Continual Pretraining with Synthetic Data for LLM Reasoning Unresolved cited work

Reference 32

Resolution
unresolved
raw_fallback, observed 2026-08-15T21:19:12.148624Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T21:19:11.581152Z digest=sha256:f4d864113bd96a529f903ce25b26c16fbdd4bca41b2134dd1240c013534c3e8a

Observation 0a58e86e-7d0b-47b8-8cfc-4fe83a69b774 · outbound

This paper cites Lexpam: Legal procedure awareness-guided mathematical reasoning.

Mining Hidden Thoughts from Texts: Evaluating Continual Pretraining with Synthetic Data for LLM Reasoning Lexpam: Legal procedure awareness-guided mathematical reasoning

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:19:12.133674Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T21:19:11.585727Z digest=sha256:f1c8ca960158a7a3afde53af39f72384161104807725a727e2b57305962cafdb

Observation dfc13669-de20-484b-98f1-96764ec2fd00 · outbound

This paper cites Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking.

Mining Hidden Thoughts from Texts: Evaluating Continual Pretraining with Synthetic Data for LLM Reasoning Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-15T21:19:11.590629Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:19:11.590629Z digest=sha256:90b23ebd0c006b81f68252a1cb9119030a96df7679788eaefd39df23eae205de

Observation 08057371-22ad-46e2-95fa-b7fb9928f5fa · outbound

This paper cites Explain yourself! leveraging language models for commonsense reasoning.

Mining Hidden Thoughts from Texts: Evaluating Continual Pretraining with Synthetic Data for LLM Reasoning Explain yourself! leveraging language models for commonsense reasoning

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-15T21:19:11.595792Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:19:11.595792Z digest=sha256:a363e6f42ce656299fe191637691b620343256ac4016a1dcbf0d05e748a2824e

Observation 16c7d3b1-e8a8-440d-9d23-0ebbeb235ea9 · outbound

This paper cites Reinstruct: Building instruction data from unlabeled corpus.

Mining Hidden Thoughts from Texts: Evaluating Continual Pretraining with Synthetic Data for LLM Reasoning Reinstruct: Building instruction data from unlabeled corpus

Reference 36

Resolution
verified exact
doi, observed 2026-08-15T21:19:11.643936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T21:19:11.601337Z digest=sha256:2fc3ea4b7f67c14a568c70297357ba8d0ffbe773ca983ab935da5c0f111770fc

Observation 5ac925d0-dcc3-4944-b8a6-fe5a411ea794 · outbound

This paper cites Learning to reason and memorize with self-notes.

Mining Hidden Thoughts from Texts: Evaluating Continual Pretraining with Synthetic Data for LLM Reasoning Learning to reason and memorize with self-notes

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:19:12.119282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T21:19:11.606064Z digest=sha256:0762630c9bfba0e511dc19686a4b3834403295a8dad3f64ecfb6c8df09dd8018

Pith citing papers

No inbound Pith citation observations are available.