Paper Citation Record · LEDGER

Let's Think Dot by Dot: Hidden Computation in Transformer Language Models

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 47 inbound Pith citation observations for arXiv:2404.15758.

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

pith.paper-citation-record.v1
2404.15758 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 47 of 47 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T22:58:23.946035Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T20:58:58.552877Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
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malformed identifier0
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External citation measurements

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Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 2f9c2cad-b5e1-4ab0-b5a8-390798bd0e5b · inbound

Training Large Language Models to Reason in a Continuous Latent Space cites this paper.

Training Large Language Models to Reason in a Continuous Latent Space Let's Think Dot by Dot: Hidden Computation in Transformer Language Models

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-11T10:29:05.785952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f46f76ef-39c5-4fdc-9ecc-dba856ec9c3c · inbound

Thoughts Are All Over the Place: On the Underthinking of o1-Like LLMs cites this paper.

Thoughts Are All Over the Place: On the Underthinking of o1-Like LLMs Let's Think Dot by Dot: Hidden Computation in Transformer Language Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-09T22:58:23.946035Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d8b59338-403d-40bd-8971-6425ee665355 · inbound

Token Assorted: Mixing Latent and Text Tokens for Improved Language Model Reasoning cites this paper.

Token Assorted: Mixing Latent and Text Tokens for Improved Language Model Reasoning Let's Think Dot by Dot: Hidden Computation in Transformer Language Models

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-09T05:22:33.083474Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df247a5a-d8d6-4116-a3b1-fb8cb5459c95 · inbound

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

Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models Let's Think Dot by Dot: Hidden Computation in Transformer Language Models

Reference 141

Resolution: verified exact; arxiv_id, observed 2026-05-14T01:29:56.685051Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4693b956-7d98-4bf8-a54a-9b9992f85407 · inbound

System-1.5 Reasoning: Traversal in Language and Latent Spaces with Dynamic Shortcuts cites this paper.

System-1.5 Reasoning: Traversal in Language and Latent Spaces with Dynamic Shortcuts Let's Think Dot by Dot: Hidden Computation in Transformer Language Models

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T14:27:39.319606Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 08632386-54fd-492a-b9b8-4e09eae1a1c4 · inbound

Knowing Before Saying: LLM Representations Encode Information About Chain-of-Thought Success Before Completion cites this paper.

Knowing Before Saying: LLM Representations Encode Information About Chain-of-Thought Success Before Completion Let's Think Dot by Dot: Hidden Computation in Transformer Language Models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T12:35:20.647972Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation db05feb9-54b6-4203-8c5c-699fc4108da5 · inbound

Learning a Continue-Thinking Token for Enhanced Test-Time Scaling cites this paper.

Learning a Continue-Thinking Token for Enhanced Test-Time Scaling Let's Think Dot by Dot: Hidden Computation in Transformer Language Models

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-19T09:12:14.870372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 20782098-b0fb-492a-abd4-0c5c67f22e62 · inbound

Efficient Reasoning Through Suppression of Self-Affirmation Reflections in Large Reasoning Models cites this paper.

Efficient Reasoning Through Suppression of Self-Affirmation Reflections in Large Reasoning Models Let's Think Dot by Dot: Hidden Computation in Transformer Language Models

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T00:57:02.090047Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d36e93d-40fb-420b-8d44-7e5f1603eac7 · inbound

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey cites this paper.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Let's Think Dot by Dot: Hidden Computation in Transformer Language Models

Reference 151

Resolution: unresolved; no resolver link, observed 2026-08-06T17:54:17.584595Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e03ea8b4-ab42-4168-af01-b52f9b633829 · inbound

Performative Thinking? The Brittle Correlation Between CoT Length and Problem Complexity cites this paper.

Performative Thinking? The Brittle Correlation Between CoT Length and Problem Complexity Let's Think Dot by Dot: Hidden Computation in Transformer Language Models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-04T22:25:54.891821Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e096682-f5c6-4c12-a0c8-90468a4103e8 · inbound

Mind-Paced Speaking: A Dual-Brain Approach to Real-Time Reasoning in Spoken Language Models cites this paper.

Mind-Paced Speaking: A Dual-Brain Approach to Real-Time Reasoning in Spoken Language Models Let's Think Dot by Dot: Hidden Computation in Transformer Language Models

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-18T07:46:03.529211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation dd317f51-d7b9-4f65-8241-71acc5ec96be · inbound

Can Aha Moments Be Fake? Towards Quantifying Decorative and True Thinking in Chain-of-Thought cites this paper.

Can Aha Moments Be Fake? Towards Quantifying Decorative and True Thinking in Chain-of-Thought Let's Think Dot by Dot: Hidden Computation in Transformer Language Models

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-18T02:45:46.226074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a2a03e77-a100-486e-8598-260ef2650e3b · inbound

Can Aha Moments Be Fake? Towards Quantifying Decorative and True Thinking in Chain-of-Thought cites this paper.

Can Aha Moments Be Fake? Towards Quantifying Decorative and True Thinking in Chain-of-Thought Let's Think Dot by Dot: Hidden Computation in Transformer Language Models

Reference 2009

Resolution: unresolved; no resolver link, observed 2026-08-04T07:43:10.510909Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d7bca8da-8e30-4df5-9f02-9c859435b5b3 · inbound

Enabling Agents to Communicate Entirely in Latent Space cites this paper.

Enabling Agents to Communicate Entirely in Latent Space Let's Think Dot by Dot: Hidden Computation in Transformer Language Models

Reference 2

Resolution: metadata mismatch; arxiv_id, observed 2026-05-17T23:42:13.095987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2bfe0816-717a-4cd9-8202-89b20d29a6c8 · inbound

Enabling Agents to Communicate Entirely in Latent Space cites this paper.

Enabling Agents to Communicate Entirely in Latent Space Let's Think Dot by Dot: Hidden Computation in Transformer Language Models

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-03T22:48:26.260010Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c310a12e-841a-4c0a-b0dc-8c8ec13a3316 · inbound

Diagnosing Pathological Chain-of-Thought in Reasoning Models cites this paper.

Diagnosing Pathological Chain-of-Thought in Reasoning Models Let's Think Dot by Dot: Hidden Computation in Transformer Language Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-02T23:26:30.391525Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f01a3207-56e3-46db-8380-a4b26eca4a47 · inbound

NEST: Nascent Encoded Steganographic Thoughts cites this paper.

NEST: Nascent Encoded Steganographic Thoughts Let's Think Dot by Dot: Hidden Computation in Transformer Language Models

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-02T23:21:34.496686Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ba280d59-bc6c-4202-9d25-bd8dfbd683cc · inbound

The Latent Space: Foundation, Evolution, Mechanism, Ability, and Outlook cites this paper.

The Latent Space: Foundation, Evolution, Mechanism, Ability, and Outlook Let's Think Dot by Dot: Hidden Computation in Transformer Language Models

Reference 162

Resolution: unresolved; no resolver link, observed 2026-07-13T14:03:01.974171Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 40a95346-5555-4576-ad42-fa5c1ddb6c42 · inbound

PLUME: Latent Reasoning Based Universal Multimodal Embedding cites this paper.

PLUME: Latent Reasoning Based Universal Multimodal Embedding Let's Think Dot by Dot: Hidden Computation in Transformer Language Models

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-13T21:53:20.031713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b9364ff2-526f-4e4c-b08c-4ba388f830e5 · inbound

SeLaR: Selective Latent Reasoning in Large Language Models cites this paper.

SeLaR: Selective Latent Reasoning in Large Language Models Let's Think Dot by Dot: Hidden Computation in Transformer Language Models

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-11T00:35:49.536966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 87c7244e-4814-4342-9a2c-894b1da98917 · inbound

LLM Reasoning Is Latent, Not the Chain of Thought cites this paper.

LLM Reasoning Is Latent, Not the Chain of Thought Let's Think Dot by Dot: Hidden Computation in Transformer Language Models

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-10T08:53:04.655705Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3f408342-7b1d-4181-a8ae-18af0645e78d · inbound

Measuring AI Reasoning: A Guide for Researchers cites this paper.

Measuring AI Reasoning: A Guide for Researchers Let's Think Dot by Dot: Hidden Computation in Transformer Language Models

Reference 134

Resolution: verified exact; arxiv_id, observed 2026-05-09T06:05:36.693608Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1ff86c47-0257-4883-9501-b2a2f3590bff · inbound

Post Reasoning: Improving the Performance of Non-Thinking Models at No Cost cites this paper.

Post Reasoning: Improving the Performance of Non-Thinking Models at No Cost Let's Think Dot by Dot: Hidden Computation in Transformer Language Models

Reference 74

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:06:09.743012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 58887614-f3d7-42cc-827b-337fee00533d · inbound

Rethinking Dense Sequential Chains: Reasoning Language Models Can Extract Answers from Sparse, Order-Shuffling Chain-of-Thoughts cites this paper.

Rethinking Dense Sequential Chains: Reasoning Language Models Can Extract Answers from Sparse, Order-Shuffling Chain-of-Thoughts Let's Think Dot by Dot: Hidden Computation in Transformer Language Models

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-11T03:50:57.499759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4fee061a-8949-4e14-ac3a-409f70b884e6 · inbound

NoisyCoconut: Counterfactual Consensus via Latent Space Reasoning cites this paper.

NoisyCoconut: Counterfactual Consensus via Latent Space Reasoning Let's Think Dot by Dot: Hidden Computation in Transformer Language Models

Reference 49

Resolution: verified exact; arxiv_id, observed 2026-05-12T08:41:24.240276Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3beedbf2-1335-4883-9e3f-ba4e1946b432 · inbound

The Last Word Often Wins: A Format Confound in Chain-of-Thought Corruption Studies cites this paper.

The Last Word Often Wins: A Format Confound in Chain-of-Thought Corruption Studies Let's Think Dot by Dot: Hidden Computation in Transformer Language Models

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-12T07:06:26.203675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0a9f92d5-0ef4-430f-9fe9-9206575aed1a · inbound

The Last Word Often Wins: A Format Confound in Chain-of-Thought Corruption Studies cites this paper.

The Last Word Often Wins: A Format Confound in Chain-of-Thought Corruption Studies Let's Think Dot by Dot: Hidden Computation in Transformer Language Models

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-19T17:27:41.352482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d2c2985e-1e67-4217-9a14-cbf345cf0bb3 · inbound

CopT: Contrastive On-Policy Thinking with Continuous Spaces for General and Agentic Reasoning cites this paper.

CopT: Contrastive On-Policy Thinking with Continuous Spaces for General and Agentic Reasoning Let's Think Dot by Dot: Hidden Computation in Transformer Language Models

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-20T05:28:04.872871Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6f6c4b37-a154-48ac-b36a-7c27ac0932b5 · inbound

Training-Free Looped Transformers cites this paper.

Training-Free Looped Transformers Let's Think Dot by Dot: Hidden Computation in Transformer Language Models

Reference 72

Resolution: verified exact; arxiv_id, observed 2026-05-25T04:36:36.795609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5e4fe772-d203-4124-ba14-358bc358bc46 · inbound

Understanding and Mitigating Premature Confidence for Better LLM Reasoning cites this paper.

Understanding and Mitigating Premature Confidence for Better LLM Reasoning Let's Think Dot by Dot: Hidden Computation in Transformer Language Models

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-06-30T14:04:44.423161Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1be094ae-b060-47d9-9aac-a3f6f6ea333b · inbound

What Does Chain-of-Thought Contribute at Probe Time? Evidence for Local Co-Occurrence Activation cites this paper.

What Does Chain-of-Thought Contribute at Probe Time? Evidence for Local Co-Occurrence Activation Let's Think Dot by Dot: Hidden Computation in Transformer Language Models

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-06-29T17:33:45.509891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation af8cf6c3-ab12-4979-a2de-fcc235b3dfc2 · inbound

What Does Chain-of-Thought Contribute at Probe Time? Evidence for Local Co-Occurrence Activation cites this paper.

What Does Chain-of-Thought Contribute at Probe Time? Evidence for Local Co-Occurrence Activation Let's Think Dot by Dot: Hidden Computation in Transformer Language Models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-02T13:08:39.771469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation efb1b40d-02f1-405a-85e2-3ba3052b2561 · inbound

Latent Recurrent Transformer: Architecture Exploration, Training Strategies, and Scaling Behavior cites this paper.

Latent Recurrent Transformer: Architecture Exploration, Training Strategies, and Scaling Behavior Let's Think Dot by Dot: Hidden Computation in Transformer Language Models

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-06-29T19:43:54.963801Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4c7a9297-289e-45ce-9162-17ef0b4d8911 · inbound

Integrated and Cross-Architecture Interpretation of LLM Reasoning cites this paper.

Integrated and Cross-Architecture Interpretation of LLM Reasoning Let's Think Dot by Dot: Hidden Computation in Transformer Language Models

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-06-29T13:23:28.353037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 57a09f09-ba93-4dc9-a71b-ed50f84c72a4 · inbound

CIRF: Tokenizing Chain-of-Thoughts into Reusable Functional Units for Efficient Latent Reasoning in Large Language Models cites this paper.

CIRF: Tokenizing Chain-of-Thoughts into Reusable Functional Units for Efficient Latent Reasoning in Large Language Models Let's Think Dot by Dot: Hidden Computation in Transformer Language Models

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-06-29T13:23:28.179571Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 01b44cc3-95c4-4d3f-b357-f4e32a50adc9 · inbound

Transformers Provably Learn to Internalize Chain-of-Thought cites this paper.

Transformers Provably Learn to Internalize Chain-of-Thought Let's Think Dot by Dot: Hidden Computation in Transformer Language Models

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-06-29T14:33:30.620196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bcbf571f-fc54-4d72-a004-570c97277fb9 · inbound

Unlocking the Working Memory of Large Language Models for Latent Reasoning cites this paper.

Unlocking the Working Memory of Large Language Models for Latent Reasoning Let's Think Dot by Dot: Hidden Computation in Transformer Language Models

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-06-29T08:03:13.919517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 82bb867e-f6f6-4f13-8b34-77640d020827 · inbound

Test-Time Compute Scaling for ASR with Depth-Conditioned Looped Transformers cites this paper.

Test-Time Compute Scaling for ASR with Depth-Conditioned Looped Transformers Let's Think Dot by Dot: Hidden Computation in Transformer Language Models

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-07-02T07:26:45.909324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c8340cde-cf43-4286-97ee-d94decc9351f · inbound

Demystifying Hidden-State Recurrence: Switchable Latent Reasoning with On-Policy Reinforcement Learning cites this paper.

Demystifying Hidden-State Recurrence: Switchable Latent Reasoning with On-Policy Reinforcement Learning Let's Think Dot by Dot: Hidden Computation in Transformer Language Models

Reference 55

Resolution: verified exact; arxiv_id, observed 2026-07-03T13:58:22.077657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8b739a62-c2db-4974-9af8-31002b685716 · inbound

PearlVLA: Progressive Embodied Action-Plan Refinement in Latent Space cites this paper.

PearlVLA: Progressive Embodied Action-Plan Refinement in Latent Space Let's Think Dot by Dot: Hidden Computation in Transformer Language Models

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-07-03T20:58:58.554410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 72d591b1-d100-49f5-a9a7-d55f01b25c91 · inbound

Does Verbose Chain-of-Thought Really Help? In-Distribution Evidence that Content, Not Length, Matters cites this paper.

Does Verbose Chain-of-Thought Really Help? In-Distribution Evidence that Content, Not Length, Matters Let's Think Dot by Dot: Hidden Computation in Transformer Language Models

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-06-30T06:54:21.081707Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 92b91c18-1988-4afa-945c-609ce301c984 · inbound

DiscoLoop: Looping Discrete Embeddings and Continuous Hidden States for Multi-hop Reasoning cites this paper.

DiscoLoop: Looping Discrete Embeddings and Continuous Hidden States for Multi-hop Reasoning Let's Think Dot by Dot: Hidden Computation in Transformer Language Models

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-07-02T13:56:59.329578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a303873d-7de6-468d-ae20-8bb05f3b38b8 · inbound

DiscoLoop: Looping Discrete Embeddings and Continuous Hidden States for Multi-hop Reasoning cites this paper.

DiscoLoop: Looping Discrete Embeddings and Continuous Hidden States for Multi-hop Reasoning Let's Think Dot by Dot: Hidden Computation in Transformer Language Models

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-02T09:18:44.427622Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3b8542ff-28be-499d-9c0c-815e6a1a4953 · inbound

Conversable Complexity: Agentic LLM Collectives as Interpretable Substrates cites this paper.

Conversable Complexity: Agentic LLM Collectives as Interpretable Substrates Let's Think Dot by Dot: Hidden Computation in Transformer Language Models

Reference 86

Resolution: verified exact; arxiv_id, observed 2026-07-02T12:46:56.207324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation de7e11ce-22d6-48ca-98a7-3ea4928b7b90 · inbound

Training Continuous Chain of Thought Models: A Tale of Two Regimes cites this paper.

Training Continuous Chain of Thought Models: A Tale of Two Regimes Let's Think Dot by Dot: Hidden Computation in Transformer Language Models

Reference 168

Resolution: unresolved; no resolver link, observed 2026-08-01T19:29:06.397389Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fb0ee4df-1eb3-46a7-8116-08306c40ab80 · inbound

J-CoT: Chain-of-Thought in J-Space cites this paper.

J-CoT: Chain-of-Thought in J-Space Let's Think Dot by Dot: Hidden Computation in Transformer Language Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-01T06:14:11.133634Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 96743c93-651d-4dee-9e82-3687c717c4de · inbound

Not All LLM Reasoning is Visible in the Chain-of-Thought cites this paper.

Not All LLM Reasoning is Visible in the Chain-of-Thought Let's Think Dot by Dot: Hidden Computation in Transformer Language Models

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-01T04:14:45.133222Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.