Paper Citation Record · LEDGER

Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective

As of 13 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 1 inbound Pith citation observation for arXiv:2412.06033.

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

pith.paper-citation-record.v1
2412.06033 v1

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T20:10:53.694502Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:10:01.813523Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T17:10:04.458833Z

Reference resolution

20 of 20 outbound references displayed

verified exact0
verified fuzzy5
unresolved15
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation df76fb40-48b7-44d9-8c41-184c47e7b2ab · outbound

This paper cites ∼.” It means “sampled according to.

Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective ∼.” It means “sampled according to

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:10:53.877956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 3fa7fc8b-9978-4093-a9c5-657682076da2 · outbound

This paper cites Let F ∼ Pθ and X1, X2,.

Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective Let F ∼ Pθ and X1, X2,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:10:53.865769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 36a5099a-1845-45b0-b7ac-a82cf49d6cea · outbound

This paper cites A Survey on In-context Learning.

Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective A Survey on In-context Learning

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-11T20:10:53.633581Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c95af48a-f17b-4400-adf8-8e2473aafc31 · outbound

This paper cites Is In-Context Learning in Large Language Models Bayesian? A Martingale Perspective.

Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective Is In-Context Learning in Large Language Models Bayesian? A Martingale Perspective

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T20:10:53.642612Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2e940a73-1e8b-49b1-a730-f0f7a9429856 · outbound

This paper cites Trapping LLM Hallucinations Using Tagged Context Prompts.

Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective Trapping LLM Hallucinations Using Tagged Context Prompts

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T20:10:53.646609Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b6cea9e-3bc4-43ee-b728-a6703e40d079 · outbound

This paper cites Language Models (Mostly) Know What They Know.

Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective Language Models (Mostly) Know What They Know

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-11T20:10:53.651237Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 13633014-b693-47e7-bef7-a1a6a54e4bd4 · outbound

This paper cites A detailed treatment of Doob's theorem.

Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective A detailed treatment of Doob's theorem

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-11T20:10:53.659009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3db7b092-fcbc-40f8-9a01-db12d13b276e · outbound

This paper cites Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback.

Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T20:10:53.665287Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 18dcd7c4-7d5f-45f3-a090-1e8952386d51 · outbound

This paper cites Steering Llama 2 via Contrastive Activation Addition.

Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective Steering Llama 2 via Contrastive Activation Addition

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T20:10:53.668432Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ecfc4592-ed19-4eab-a14c-9271b23c2702 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T20:10:53.675184Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1142fc7c-15eb-4982-9a63-3f09f4cebc05 · outbound

This paper cites A Stitch in Time Saves Nine: Detecting and Mitigating Hallucinations of LLMs by Validating Low-Confidence Generation.

Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective A Stitch in Time Saves Nine: Detecting and Mitigating Hallucinations of LLMs by Validating Low-Confidence Generation

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T20:10:53.678380Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f45a331b-1e17-49a6-a265-638908d3bc85 · outbound

This paper cites Exchangeable Sequence Models Quantify Uncertainty Over Latent Concepts.

Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective Exchangeable Sequence Models Quantify Uncertainty Over Latent Concepts

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T20:10:53.681473Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation adcf29d0-9123-40ec-a144-794010f963ae · outbound

This paper cites The Knowledge Alignment Problem: Bridging Human and External Knowledge for Large Language Models.

Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective The Knowledge Alignment Problem: Bridging Human and External Knowledge for Large Language Models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-11T20:10:53.686505Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53d4d0b2-8d90-4e15-ae3d-b1625590ad10 · outbound

This paper cites Trusting your evidence: Hallucinate less with context-aware decoding.

Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective Trusting your evidence: Hallucinate less with context-aware decoding

Reference 1984

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:10:53.892033Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f13f502e-1af6-4cfc-9c86-b97c0724445a · outbound

This paper cites ISBN 978-3-540-33428-6.

Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective ISBN 978-3-540-33428-6

Reference 2006

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:10:53.920325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 7c446083-8348-4126-bac1-c4b8bbbf68c8 · outbound

This paper cites Holdout predictive checks for [b]ayesian model criticism.

Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective Holdout predictive checks for [b]ayesian model criticism

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:10:53.907395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 912d4a32-08a5-4b8a-9815-006fb0967ffe · outbound

This paper cites Halo: Estimation and Reduction of Hallucinations in Open-Source Weak Large Language Models.

Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective Halo: Estimation and Reduction of Hallucinations in Open-Source Weak Large Language Models

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-11T20:10:53.637979Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21992463-d560-438f-976c-ff5a4fb9e3b1 · outbound

This paper cites Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMs.

Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMs

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-11T20:10:53.655136Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2850642d-7ce1-4cbf-9004-f436f7e259b8 · outbound

This paper cites Linguistic Calibration of Long-Form Generations.

Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective Linguistic Calibration of Long-Form Generations

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-11T20:10:53.620826Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 77ec781f-4635-4f2c-9abd-e3fab0152fc8 · outbound

This paper cites Language models are few-shot learners.

Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective Language models are few-shot learners

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-11T20:10:53.625528Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 0f267321-c566-41e5-ba69-8e09b064217c · inbound

LLMs are Bayesian, In Expectation, Not in Realization cites this paper.

LLMs are Bayesian, In Expectation, Not in Realization Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:10:04.510089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.