Paper Citation Record · LEDGER

Do Large Language Models Latently Perform Multi-Hop Reasoning?

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 35 inbound Pith citation observations for arXiv:2402.16837.

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

pith.paper-citation-record.v1
2402.16837 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 35 of 35 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 35 of 35 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T16:56:24.995196Z

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
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

0
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 b710ffb6-40d7-4418-b1e8-cf7c41b575d7 · inbound

Training Language Models to Self-Correct via Reinforcement Learning cites this paper.

Training Language Models to Self-Correct via Reinforcement Learning Do Large Language Models Latently Perform Multi-Hop Reasoning?

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-05-17T12:04:10.460906Z

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 9eaadc97-3dd9-43bc-bda8-f5b496fd6295 · 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 Do Large Language Models Latently Perform Multi-Hop Reasoning?

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-11T10:29:05.857757Z

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 2ee4facd-2769-43bd-b3b7-8cfd541e1edc · inbound

Paradigm-Based Automatic HDL Code Generation Using LLMs cites this paper.

Paradigm-Based Automatic HDL Code Generation Using LLMs Do Large Language Models Latently Perform Multi-Hop Reasoning?

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-10T16:56:24.995196Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cae666ff-4b37-4ac4-b1a2-8828e38e4120 · inbound

State Stream Transformer (SST) : Emergent Metacognitive Behaviours Through Latent State Persistence cites this paper.

State Stream Transformer (SST) : Emergent Metacognitive Behaviours Through Latent State Persistence Do Large Language Models Latently Perform Multi-Hop Reasoning?

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-09T23:52:06.065071Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b86bfb85-7f0d-47ef-bfc9-33b8afbc0cd8 · inbound

Efficient Reasoning with Hidden Thinking cites this paper.

Efficient Reasoning with Hidden Thinking Do Large Language Models Latently Perform Multi-Hop Reasoning?

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-23T04:47:33.673125Z

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 c36ab761-5ec1-4de4-b878-52d13683f14e · inbound

Investigating Compositional Reasoning in Time Series Foundation Models cites this paper.

Investigating Compositional Reasoning in Time Series Foundation Models Do Large Language Models Latently Perform Multi-Hop Reasoning?

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-08T17:06:03.386382Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b702e528-20a2-40a8-9370-468968a25892 · inbound

Position: Episodic Memory is the Missing Piece for Long-Term LLM Agents cites this paper.

Position: Episodic Memory is the Missing Piece for Long-Term LLM Agents Do Large Language Models Latently Perform Multi-Hop Reasoning?

Reference 103

Resolution: unresolved; no resolver link, observed 2026-08-08T14:13:38.418362Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c803a1f3-b23a-424c-9c33-cca2ba79e142 · inbound

Compromising Honesty and Harmlessness in Language Models via Deception Attacks cites this paper.

Compromising Honesty and Harmlessness in Language Models via Deception Attacks Do Large Language Models Latently Perform Multi-Hop Reasoning?

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-08T05:42:43.551459Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aec01433-9c1d-49e3-876f-6f3a0dd0a2c5 · inbound

Soft Thinking: Unlocking the Reasoning Potential of LLMs in Continuous Concept Space cites this paper.

Soft Thinking: Unlocking the Reasoning Potential of LLMs in Continuous Concept Space Do Large Language Models Latently Perform Multi-Hop Reasoning?

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T15:15:43.540485Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b05d59e1-3e0a-4dd5-b1a4-5f59b28d1b82 · inbound

BioHopR: A Benchmark for Multi-Hop, Multi-Answer Reasoning in Biomedical Domain cites this paper.

BioHopR: A Benchmark for Multi-Hop, Multi-Answer Reasoning in Biomedical Domain Do Large Language Models Latently Perform Multi-Hop Reasoning?

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T13:15:59.661139Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 48406109-4071-4bce-a63b-9704781068d3 · inbound

Scalable Complexity Control Facilitates Reasoning Ability of LLMs cites this paper.

Scalable Complexity Control Facilitates Reasoning Ability of LLMs Do Large Language Models Latently Perform Multi-Hop Reasoning?

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:14.988483Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2f6f0c7b-8d67-432a-b51b-fb187d7f4894 · inbound

Learning Compositional Functions with Transformers from Easy-to-Hard Data cites this paper.

Learning Compositional Functions with Transformers from Easy-to-Hard Data Do Large Language Models Latently Perform Multi-Hop Reasoning?

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-07T12:46:40.093818Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c528e740-4087-4c9d-a4a3-ba90d01358c5 · inbound

Relational reasoning and inductive bias in transformers and large language models cites this paper.

Relational reasoning and inductive bias in transformers and large language models Do Large Language Models Latently Perform Multi-Hop Reasoning?

Reference 24

Resolution: metadata mismatch; arxiv_id, observed 2026-05-19T11:32:17.030178Z

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 58c6e6fe-54aa-418f-ae14-fe48498396b8 · inbound

Output Scaling: YingLong-Delayed Chain of Thought in a Large Pretrained Time Series Forecasting Model cites this paper.

Output Scaling: YingLong-Delayed Chain of Thought in a Large Pretrained Time Series Forecasting Model Do Large Language Models Latently Perform Multi-Hop Reasoning?

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T15:39:26.672211Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4a91d68-ac92-4773-a86b-1f300fa3a6dc · inbound

Distinct Computations Emerge From Compositional Curricula in In-Context Learning cites this paper.

Distinct Computations Emerge From Compositional Curricula in In-Context Learning Do Large Language Models Latently Perform Multi-Hop Reasoning?

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-07T00:41:59.556203Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34988dc6-feec-4d4a-855b-26cf813545c2 · inbound

A Survey on Latent Reasoning cites this paper.

A Survey on Latent Reasoning Do Large Language Models Latently Perform Multi-Hop Reasoning?

Reference 120

Resolution: unresolved; no resolver link, observed 2026-08-06T19:14:32.725694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e8c858ae-4451-4738-85bc-81a339a9e098 · inbound

Unveiling the Mechanisms of Multi-Hop Reasoning in Transformers via Identity Bridge cites this paper.

Unveiling the Mechanisms of Multi-Hop Reasoning in Transformers via Identity Bridge Do Large Language Models Latently Perform Multi-Hop Reasoning?

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-04T13:54:18.569016Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 048f9597-689c-4420-97fb-1446b7b0552c · inbound

Attention Illuminates LLM Reasoning: The Preplan-and-Anchor Rhythm Enables Fine-Grained Policy Optimization cites this paper.

Attention Illuminates LLM Reasoning: The Preplan-and-Anchor Rhythm Enables Fine-Grained Policy Optimization Do Large Language Models Latently Perform Multi-Hop Reasoning?

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-04T09:48:10.675483Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8db51c76-c766-452d-a7b0-d365be575c04 · 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 Do Large Language Models Latently Perform Multi-Hop Reasoning?

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-18T02:45:46.214308Z

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 5ec7ad7a-df65-446d-bf40-6aa87876a466 · 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 Do Large Language Models Latently Perform Multi-Hop Reasoning?

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-04T07:43:11.632882Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1d899097-a7a3-44c9-8a4f-950fb72b03c9 · inbound

Learning Self-Interpretation from Interpretability Artifacts: Training Lightweight Adapters on Vector-Label Pairs cites this paper.

Learning Self-Interpretation from Interpretability Artifacts: Training Lightweight Adapters on Vector-Label Pairs Do Large Language Models Latently Perform Multi-Hop Reasoning?

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-03T01:14:58.578330Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b4f08d4-a41f-4180-bf61-b0d6667bec2b · inbound

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

SeLaR: Selective Latent Reasoning in Large Language Models Do Large Language Models Latently Perform Multi-Hop Reasoning?

Reference 52

Resolution: verified exact; arxiv_id, observed 2026-05-11T00:35:49.655868Z

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 be1186bb-c9fb-4445-812b-64786b3e6e5c · inbound

LACE: Lattice Attention for Cross-thread Exploration cites this paper.

LACE: Lattice Attention for Cross-thread Exploration Do Large Language Models Latently Perform Multi-Hop Reasoning?

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-05-10T10:24:21.278624Z

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 6317cc9c-2618-4616-8831-0806e224524c · inbound

LACE: Lattice Attention for Cross-thread Exploration cites this paper.

LACE: Lattice Attention for Cross-thread Exploration Do Large Language Models Latently Perform Multi-Hop Reasoning?

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-05-11T00:50:50.144622Z

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 eea2fe31-344d-47cc-9b60-193cb9b93019 · inbound

LACE: Lattice Attention for Cross-thread Exploration cites this paper.

LACE: Lattice Attention for Cross-thread Exploration Do Large Language Models Latently Perform Multi-Hop Reasoning?

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-05-12T06:36:28.633744Z

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 d52e8b0d-c62a-4355-9135-5e45d783a24c · inbound

EHRAG: Bridging Semantic Gaps in Lightweight GraphRAG via Hybrid Hypergraph Construction and Retrieval cites this paper.

EHRAG: Bridging Semantic Gaps in Lightweight GraphRAG via Hybrid Hypergraph Construction and Retrieval Do Large Language Models Latently Perform Multi-Hop Reasoning?

Reference 172

Resolution: verified exact; arxiv_id, observed 2026-05-10T05:51:10.671407Z

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 5e20edb5-609e-4567-9cf7-2bb9003b826a · inbound

HypEHR: Hyperbolic Modeling of Electronic Health Records for Efficient Question Answering cites this paper.

HypEHR: Hyperbolic Modeling of Electronic Health Records for Efficient Question Answering Do Large Language Models Latently Perform Multi-Hop Reasoning?

Reference 102

Resolution: verified exact; arxiv_id, observed 2026-05-09T23:54:45.064991Z

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 780d4da8-597a-4dcf-9034-687e8de595c6 · inbound

The Power of Power Law: Asymmetry Enables Compositional Reasoning cites this paper.

The Power of Power Law: Asymmetry Enables Compositional Reasoning Do Large Language Models Latently Perform Multi-Hop Reasoning?

Reference 56

Resolution: verified exact; arxiv_id, observed 2026-05-11T19:31:08.225792Z

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 0d4344c0-95fb-4103-bd41-d6625e10ceec · inbound

The Power of Power Law: Asymmetry Enables Compositional Reasoning cites this paper.

The Power of Power Law: Asymmetry Enables Compositional Reasoning Do Large Language Models Latently Perform Multi-Hop Reasoning?

Reference 56

Resolution: unresolved; no resolver link, observed 2026-07-12T18:26:05.728364Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31a7b3af-4a34-479d-a0c7-1a4ff8a2b7f3 · inbound

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

NoisyCoconut: Counterfactual Consensus via Latent Space Reasoning Do Large Language Models Latently Perform Multi-Hop Reasoning?

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-05-12T08:41:24.466243Z

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 e7722079-e52b-4b9e-b654-b298b321fd60 · inbound

Think Fast: Estimating No-CoT Task-Completion Time Horizons of Frontier AI Models cites this paper.

Think Fast: Estimating No-CoT Task-Completion Time Horizons of Frontier AI Models Do Large Language Models Latently Perform Multi-Hop Reasoning?

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-07-02T17:07:12.978958Z

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 033ae7bb-7b03-4482-add9-7381bfbaccc9 · inbound

Think Fast: Estimating No-CoT Task-Completion Time Horizons of Frontier AI Models cites this paper.

Think Fast: Estimating No-CoT Task-Completion Time Horizons of Frontier AI Models Do Large Language Models Latently Perform Multi-Hop Reasoning?

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-06-29T05:53:08.723518Z

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 5556419a-d99d-4be2-ae1c-a973271fb81e · 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 Do Large Language Models Latently Perform Multi-Hop Reasoning?

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-07-02T13:56:59.360506Z

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 4dda5438-1583-401d-b354-44247404042b · 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 Do Large Language Models Latently Perform Multi-Hop Reasoning?

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-02T09:18:44.931948Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 95edb54a-6153-42ca-8d77-edd2957068c2 · inbound

Verbalizable Representations Form a Global Workspace in Language Models cites this paper.

Verbalizable Representations Form a Global Workspace in Language Models Do Large Language Models Latently Perform Multi-Hop Reasoning?

Reference 181

Resolution: unresolved; no resolver link, observed 2026-08-01T23:15:30.900344Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.