Paper Citation Record · LEDGER

Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2301.11916.

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

pith.paper-citation-record.v1
2301.11916 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:06:05.217310Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-28T19:42:35.841225Z

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 c9ed54df-e669-4b18-bf2d-8dae7df4a92e · inbound

SketchFlex: Facilitating Spatial-Semantic Coherence in Text-to-Image Generation with Region-Based Sketches cites this paper.

SketchFlex: Facilitating Spatial-Semantic Coherence in Text-to-Image Generation with Region-Based Sketches Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-08T12:22:43.309145Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 87b90de3-5d58-4759-b5ba-dfb89f49cf86 · inbound

Scaling sparse feature circuit finding for in-context learning cites this paper.

Scaling sparse feature circuit finding for in-context learning Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-16T12:06:05.217310Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3d52a43f-9462-4e91-838d-1191469dd8d1 · inbound

Towards Contamination Resistant Benchmarks cites this paper.

Towards Contamination Resistant Benchmarks Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-15T22:00:10.410510Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c49dabba-4ec1-4428-b127-1210c2bbe75b · inbound

The Role of Diversity in In-Context Learning for Large Language Models cites this paper.

The Role of Diversity in In-Context Learning for Large Language Models Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-07T14:21:38.755009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31ef48d3-c761-481b-8322-7b40f84d5bf2 · inbound

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models cites this paper.

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T11:40:58.649659Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20bf3ef1-b7d0-4a42-b34a-69bac13ae6d5 · inbound

Adaptive Task Vectors for Large Language Models cites this paper.

Adaptive Task Vectors for Large Language Models Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T11:12:59.307070Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 634222a3-d718-430b-aa62-dfa2fc904114 · inbound

Pre-trained Large Language Models Learn Hidden Markov Models In-context cites this paper.

Pre-trained Large Language Models Learn Hidden Markov Models In-context Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning

Reference 54

Resolution: verified exact; arxiv_id, observed 2026-05-19T10:37:15.084636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 98f6ec0a-aa65-47ea-bb7a-d0ba07a38281 · inbound

DICE: Dynamic In-Context Example Selection in LLM Agents via Efficient Knowledge Transfer cites this paper.

DICE: Dynamic In-Context Example Selection in LLM Agents via Efficient Knowledge Transfer Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-06T10:43:50.969414Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce59dc1d-1e18-4283-8904-cb4542c484b5 · inbound

Online In-Context Distillation for Low-Resource Vision Language Models cites this paper.

Online In-Context Distillation for Low-Resource Vision Language Models Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-18T05:40:55.994236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c69367d9-27e4-406c-ad5e-2326968d1b7c · inbound

One for All: A Non-Linear Transformer can Enable Cross-Domain Generalization for In-Context Reinforcement Learning cites this paper.

One for All: A Non-Linear Transformer can Enable Cross-Domain Generalization for In-Context Reinforcement Learning Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-12T06:56:32.071862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a88e6791-44bc-4926-a163-a77df9fa8633 · inbound

The Assistant as a Privileged Persona: A canonical reference in cross-persona self-recognition cites this paper.

The Assistant as a Privileged Persona: A canonical reference in cross-persona self-recognition Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning

Reference 13

Resolution: metadata mismatch; arxiv_id, observed 2026-06-28T19:42:35.842762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.