Paper Citation Record · LEDGER

A Unified Framework for In-Context Learning with Causal and Masked Language Models

As of 8 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 0 inbound Pith citation observations for arXiv:2607.04081.

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

pith.paper-citation-record.v1
2607.04081 v1

Coverage vector

measured 18 of 18 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-11T21:50:04.825590Z

measured 18 of 18 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

18 of 18 outbound references displayed

verified exact0
verified fuzzy0
unresolved18
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d47c386b-379d-4b93-b410-dc314a308d52 · outbound

This paper cites Context-Scaling versus Task-Scaling in In-Context Learning.

A Unified Framework for In-Context Learning with Causal and Masked Language Models Context-Scaling versus Task-Scaling in In-Context Learning

Reference 1

Resolution: unresolved; no resolver link, observed 2026-07-11T21:50:04.825590Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 623a1b00-885b-4585-b89d-c6b0bd5a6ff0 · outbound

This paper cites Lan- 31 A Unified Framework for In-Context Learning guage models are few-shot learners.Advances in neural information processing systems, 33:1877–1901,.

A Unified Framework for In-Context Learning with Causal and Masked Language Models Lan- 31 A Unified Framework for In-Context Learning guage models are few-shot learners.Advances in neural information processing systems, 33:1877–1901,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-07-11T21:50:04.825590Z

Source-reported events for the cited work

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Observation fec76d36-c513-44ea-9537-78189ea5aca4 · outbound

This paper cites Generalization Properties of Score-matching Diffusion Models for Intrinsically Low-dimensional Data.

A Unified Framework for In-Context Learning with Causal and Masked Language Models Generalization Properties of Score-matching Diffusion Models for Intrinsically Low-dimensional Data

Reference 3

Resolution: unresolved; no resolver link, observed 2026-07-11T21:50:04.825590Z

Source-reported events for the cited work

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Observation 54bf830d-64d0-432e-b8ad-5dacf7c9b85c · outbound

This paper cites Efficient and Minimax Optimal In-context Nonparametric Regression with Transformers.

A Unified Framework for In-Context Learning with Causal and Masked Language Models Efficient and Minimax Optimal In-context Nonparametric Regression with Transformers

Reference 4

Resolution: unresolved; no resolver link, observed 2026-07-11T21:50:04.825590Z

Source-reported events for the cited work

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Observation ccb638f8-d95f-4e73-866e-a567ffbcde31 · outbound

This paper cites A survey and taxonomy of loss functions in machine learning.

A Unified Framework for In-Context Learning with Causal and Masked Language Models A survey and taxonomy of loss functions in machine learning

Reference 5

Resolution: unresolved; no resolver link, observed 2026-07-11T21:50:04.825590Z

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Observation 9a6aa890-b1d4-4e42-b85e-d1ca416d6f39 · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding.

A Unified Framework for In-Context Learning with Causal and Masked Language Models Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 6

Resolution: unresolved; no resolver link, observed 2026-07-11T21:50:04.825590Z

Source-reported events for the cited work

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Observation 630080f3-aaaa-462b-96dc-089596bf019e · outbound

This paper cites Approximation theory for lipschitz continuous transformers.arXiv preprint arXiv:2602.15503,.

A Unified Framework for In-Context Learning with Causal and Masked Language Models Approximation theory for lipschitz continuous transformers.arXiv preprint arXiv:2602.15503,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-07-11T21:50:04.825590Z

Source-reported events for the cited work

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Observation 39f2ead3-fda7-426c-a661-3ccde3057cec · outbound

This paper cites When can in-context learning generalize out of task distribution?arXiv preprint arXiv:2506.05574,.

A Unified Framework for In-Context Learning with Causal and Masked Language Models When can in-context learning generalize out of task distribution?arXiv preprint arXiv:2506.05574,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-07-11T21:50:04.825590Z

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Observation 037cda46-ec00-4b80-81b2-574818a2c4a9 · outbound

This paper cites Automatic Domain Adaptation by Transformers in In-Context Learning.

A Unified Framework for In-Context Learning with Causal and Masked Language Models Automatic Domain Adaptation by Transformers in In-Context Learning

Reference 9

Resolution: unresolved; no resolver link, observed 2026-07-11T21:50:04.825590Z

Source-reported events for the cited work

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Observation e8b40d04-da8e-4443-b3b5-2010f07494b9 · outbound

This paper cites Beyond the prompt in large language models: Comprehension, in-context learning, and chain-of- thought.arXiv preprint arXiv:2603.10000,.

A Unified Framework for In-Context Learning with Causal and Masked Language Models Beyond the prompt in large language models: Comprehension, in-context learning, and chain-of- thought.arXiv preprint arXiv:2603.10000,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-07-11T21:50:04.825590Z

Source-reported events for the cited work

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Observation 490ae28e-9622-470c-91fa-a9d91b1fb2b3 · outbound

This paper cites Transformers as measure-theoretic associative memory: A statistical perspective and minimax optimality.arXiv preprint arXiv:2602.01863,.

A Unified Framework for In-Context Learning with Causal and Masked Language Models Transformers as measure-theoretic associative memory: A statistical perspective and minimax optimality.arXiv preprint arXiv:2602.01863,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-07-11T21:50:04.825590Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 006c9486-5fc2-4804-81d9-2b698ce7f5b8 · outbound

This paper cites In-Context Learning as Nonparametric Conditional Probability Estimation: Risk Bounds and Optimality.

A Unified Framework for In-Context Learning with Causal and Masked Language Models In-Context Learning as Nonparametric Conditional Probability Estimation: Risk Bounds and Optimality

Reference 12

Resolution: unresolved; no resolver link, observed 2026-07-11T21:50:04.825590Z

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Observation 36e29605-9129-474e-ba2e-ce212c7da954 · outbound

This paper cites Optimal In-context Adaptivity and Distributional Robustness of Transformers.

A Unified Framework for In-Context Learning with Causal and Masked Language Models Optimal In-context Adaptivity and Distributional Robustness of Transformers

Reference 13

Resolution: unresolved; no resolver link, observed 2026-07-11T21:50:04.825590Z

Source-reported events for the cited work

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Observation 77b6247a-c45e-4762-91b7-ba45de6c7f68 · outbound

This paper cites Towards a statistical theory of learning to learn in-context with trans- formers.

A Unified Framework for In-Context Learning with Causal and Masked Language Models Towards a statistical theory of learning to learn in-context with trans- formers

Reference 14

Resolution: unresolved; no resolver link, observed 2026-07-11T21:50:04.825590Z

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Observation 79b9c5a2-4d9a-4752-9498-d0428a2ef4a7 · outbound

This paper cites On the Regularity of Attention.

A Unified Framework for In-Context Learning with Causal and Masked Language Models On the Regularity of Attention

Reference 15

Resolution: unresolved; no resolver link, observed 2026-07-11T21:50:04.825590Z

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Observation 4811d1b1-ace7-4f58-8087-c164c80438d7 · outbound

This paper cites In-context learning is provably bayesian inference: a generalization theory for meta-learning.arXiv preprint arXiv:2510.10981,.

A Unified Framework for In-Context Learning with Causal and Masked Language Models In-context learning is provably bayesian inference: a generalization theory for meta-learning.arXiv preprint arXiv:2510.10981,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-07-11T21:50:04.825590Z

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Observation 4c517821-9875-4462-8113-b180d62443e4 · outbound

This paper cites Can in-context learning re- ally generalize to out-of-distribution tasks? InInternational Conference on Learning Representations, volume 2025, pages 83553–83574,.

A Unified Framework for In-Context Learning with Causal and Masked Language Models Can in-context learning re- ally generalize to out-of-distribution tasks? InInternational Conference on Learning Representations, volume 2025, pages 83553–83574,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-07-11T21:50:04.825590Z

Source-reported events for the cited work

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Observation e57aaaa5-0220-424c-8969-70e91d770ec3 · outbound

This paper cites Data Management For Training Large Language Models: A Survey.

A Unified Framework for In-Context Learning with Causal and Masked Language Models Data Management For Training Large Language Models: A Survey

Reference 18

Resolution: unresolved; no resolver link, observed 2026-07-11T21:50:04.825590Z

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Pith citing papers

No inbound Pith citation observations are available.