Paper Citation Record · LEDGER

Next-Token Prediction Should be Ambiguity-Sensitive: A Meta-Learning Perspective

As of 17 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2506.16288.

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

pith.paper-citation-record.v1
2506.16288 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:48:43.508912Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

41 of 41 outbound references displayed

verified exact5
verified fuzzy7
unresolved29
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 94f17705-0efa-4c8f-b421-47824e15711b · outbound

This paper cites What learning algorithm is in-context learning? Investigations with linear models.

Next-Token Prediction Should be Ambiguity-Sensitive: A Meta-Learning Perspective What learning algorithm is in-context learning? Investigations with linear models

Reference 1

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Source-reported events for the cited work

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Observation 40f64f2b-99eb-4057-9109-7f69868ba827 · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

Next-Token Prediction Should be Ambiguity-Sensitive: A Meta-Learning Perspective On the Opportunities and Risks of Foundation Models

Reference 3

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Observation 71dbcc7d-98ba-4a84-ab08-013cdf62394e · outbound

This paper cites Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning.

Next-Token Prediction Should be Ambiguity-Sensitive: A Meta-Learning Perspective Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T23:48:39.299295Z

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Observation 23b16767-c8ad-424b-be6e-c6238d866fb9 · outbound

This paper cites Beyond Bayes-optimality: meta-learning what you know you don't know.

Next-Token Prediction Should be Ambiguity-Sensitive: A Meta-Learning Perspective Beyond Bayes-optimality: meta-learning what you know you don't know

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-08-06T23:48:45.294402Z

Source-reported events for the cited work

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Observation b98ce003-2956-457d-a2d4-3bb0cfe13fbf · outbound

This paper cites Learning Universal Predictors.

Next-Token Prediction Should be Ambiguity-Sensitive: A Meta-Learning Perspective Learning Universal Predictors

Reference 7

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Source-reported events for the cited work

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Observation 125db3a6-fbdf-40be-ae63-4281e1312b1d · outbound

This paper cites Classifier-Free Diffusion Guidance.

Next-Token Prediction Should be Ambiguity-Sensitive: A Meta-Learning Perspective Classifier-Free Diffusion Guidance

Reference 11

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Observation ed593654-ec87-448a-8235-b04e628dcf7c · outbound

This paper cites Distilling Knowledge from Reader to Retriever for Question Answering.

Next-Token Prediction Should be Ambiguity-Sensitive: A Meta-Learning Perspective Distilling Knowledge from Reader to Retriever for Question Answering

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T23:48:40.164696Z

Source-reported events for the cited work

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Observation f87cb6d6-18ac-405a-a528-10d6c054c788 · outbound

This paper cites Transformer Language Models Handle Word Frequency in Prediction Head.

Next-Token Prediction Should be Ambiguity-Sensitive: A Meta-Learning Perspective Transformer Language Models Handle Word Frequency in Prediction Head

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-06T23:48:44.958567Z

Source-reported events for the cited work

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Observation b78e2f5f-391b-4bd1-b70d-39754051f770 · outbound

This paper cites The broader spectrum of in-context learning.

Next-Token Prediction Should be Ambiguity-Sensitive: A Meta-Learning Perspective The broader spectrum of in-context learning

Reference 15

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Source-reported events for the cited work

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Observation 91c82530-462e-4fb2-ac1a-cfab562b3042 · outbound

This paper cites Towards an Information Theoretic Framework of Context-Based Offline Meta-Reinforcement Learning.

Next-Token Prediction Should be Ambiguity-Sensitive: A Meta-Learning Perspective Towards an Information Theoretic Framework of Context-Based Offline Meta-Reinforcement Learning

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-08-06T23:48:44.606108Z

Source-reported events for the cited work

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Observation 1a72bd3e-90a2-4d86-92b3-d484694218d9 · outbound

This paper cites Diffusion Guided Language Modeling.

Next-Token Prediction Should be Ambiguity-Sensitive: A Meta-Learning Perspective Diffusion Guided Language Modeling

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T23:48:40.869877Z

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Observation c1a8465b-4d2a-4521-ba30-05d75ae518f3 · outbound

This paper cites Understanding the Origin of Information-Seeking Exploration in Probabilistic Objectives for Control.

Next-Token Prediction Should be Ambiguity-Sensitive: A Meta-Learning Perspective Understanding the Origin of Information-Seeking Exploration in Probabilistic Objectives for Control

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-06T23:48:44.297349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 23b4a6d8-3547-4ea7-808a-152654af5fbd · outbound

This paper cites WebGPT: Browser-assisted question-answering with human feedback.

Next-Token Prediction Should be Ambiguity-Sensitive: A Meta-Learning Perspective WebGPT: Browser-assisted question-answering with human feedback

Reference 21

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Observation cf58b1a5-6f91-427f-b6e4-1bad83f42003 · outbound

This paper cites AmbigNLG: Addressing Task Ambiguity in Instruction for NLG.

Next-Token Prediction Should be Ambiguity-Sensitive: A Meta-Learning Perspective AmbigNLG: Addressing Task Ambiguity in Instruction for NLG

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T23:48:41.424893Z

Source-reported events for the cited work

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Observation e4193223-d037-41a5-b8ec-363c7b52f2dd · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

Next-Token Prediction Should be Ambiguity-Sensitive: A Meta-Learning Perspective Representation Learning with Contrastive Predictive Coding

Reference 23

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Observation 959c59fe-03a0-44dc-8958-dd15504a0437 · outbound

This paper cites OpenAI o1 System Card.

Next-Token Prediction Should be Ambiguity-Sensitive: A Meta-Learning Perspective OpenAI o1 System Card

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T23:48:41.642535Z

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Observation 0d42f79b-8470-4740-a33c-c4b25df37a28 · outbound

This paper cites In-Context Learning through the Bayesian Prism.

Next-Token Prediction Should be Ambiguity-Sensitive: A Meta-Learning Perspective In-Context Learning through the Bayesian Prism

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T23:48:41.749879Z

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Observation 53ed3057-9575-420f-9188-a7e0bbb29143 · outbound

This paper cites Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters.

Next-Token Prediction Should be Ambiguity-Sensitive: A Meta-Learning Perspective Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T23:48:41.998582Z

Source-reported events for the cited work

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Observation 6068db52-04e5-46c5-96ca-47914c2b3bc1 · outbound

This paper cites An Explanation of In-context Learning as Implicit Bayesian Inference.

Next-Token Prediction Should be Ambiguity-Sensitive: A Meta-Learning Perspective An Explanation of In-context Learning as Implicit Bayesian Inference

Reference 30

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Source-reported events for the cited work

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Observation 58e0303e-f69c-461b-8d9b-bd0b818e5ba0 · outbound

This paper cites Task Vectors in In-Context Learning: Emergence, Formation, and Benefit.

Next-Token Prediction Should be Ambiguity-Sensitive: A Meta-Learning Perspective Task Vectors in In-Context Learning: Emergence, Formation, and Benefit

Reference 31

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Source-reported events for the cited work

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Observation 7f75c7c1-d6dc-4921-9410-b8353192be3b · outbound

This paper cites Clarify When Necessary: Resolving Ambiguity Through Interaction with LMs.

Next-Token Prediction Should be Ambiguity-Sensitive: A Meta-Learning Perspective Clarify When Necessary: Resolving Ambiguity Through Interaction with LMs

Reference 32

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Source-reported events for the cited work

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Observation 77733cb0-0401-4916-b3d1-dc960aa3e031 · outbound

This paper cites Vector-ICL: In-context Learning with Continuous Vector Representations.

Next-Token Prediction Should be Ambiguity-Sensitive: A Meta-Learning Perspective Vector-ICL: In-context Learning with Continuous Vector Representations

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-06T23:48:42.605607Z

Source-reported events for the cited work

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Observation 868fc8cb-55c4-4a2e-b94c-fbed6863a8de · outbound

This paper cites Xie et al.

Next-Token Prediction Should be Ambiguity-Sensitive: A Meta-Learning Perspective Xie et al

Reference 34

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 71b5a4f0-f8ce-4d67-a11d-6a2dfd777247 · outbound

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Next-Token Prediction Should be Ambiguity-Sensitive: A Meta-Learning Perspective Unresolved cited work

Reference 35

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5659bdfb-af6b-4c01-a924-426f72d2284b · outbound

This paper cites an unresolved cited work.

Next-Token Prediction Should be Ambiguity-Sensitive: A Meta-Learning Perspective Unresolved cited work

Reference 36

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9619e619-8938-4e72-a1a0-08ad34992a98 · outbound

This paper cites Further, system prompts, such as those used in chatbots, serve to disambiguate the model’s role and task (Niwa & Iso, 2024).

Next-Token Prediction Should be Ambiguity-Sensitive: A Meta-Learning Perspective Further, system prompts, such as those used in chatbots, serve to disambiguate the model’s role and task (Niwa & Iso, 2024)

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:46.313999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 55fd4966-d295-4cdc-ac4c-7e2843e73a11 · outbound

This paper cites However, theses methods enhance can be seen as addressing cases where the conditional prediction p(xt | x<t, θ), not the task inference, is too difficult for the model.

Next-Token Prediction Should be Ambiguity-Sensitive: A Meta-Learning Perspective However, theses methods enhance can be seen as addressing cases where the conditional prediction p(xt | x<t, θ), not the task inference, is too difficult for the model

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:46.090531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 08868ebf-e618-41fb-b51d-4c5e835334ad · outbound

This paper cites In transformers, this has evolved into early exiting mechanisms (Zhu, 2021), which conditionally terminate processing.

Next-Token Prediction Should be Ambiguity-Sensitive: A Meta-Learning Perspective In transformers, this has evolved into early exiting mechanisms (Zhu, 2021), which conditionally terminate processing

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:45.881161Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c138dcd4-b4b3-4915-b30b-0a1d8337a34a · outbound

This paper cites Additionally, the embedding z may encode high- frequency details from xctx that are hard to sample accurately.

Next-Token Prediction Should be Ambiguity-Sensitive: A Meta-Learning Perspective Additionally, the embedding z may encode high- frequency details from xctx that are hard to sample accurately

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:45.606173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b21875f1-a162-41c1-a70b-9780dbcbf530 · outbound

This paper cites Other hyperparameters are the same as in (Lovelace et al., 2023).

Next-Token Prediction Should be Ambiguity-Sensitive: A Meta-Learning Perspective Other hyperparameters are the same as in (Lovelace et al., 2023)

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:45.442634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a29f8fdb-ee3a-4cb0-a9e1-091b23fe9896 · outbound

This paper cites Progressive Distillation for Fast Sampling of Diffusion Models.

Next-Token Prediction Should be Ambiguity-Sensitive: A Meta-Learning Perspective Progressive Distillation for Fast Sampling of Diffusion Models

Reference 1999

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This paper cites Schick, T., Dwivedi-Yu, J., Dess`ı, R., Raileanu, R., Lomeli, M., Hambro, E., Zettlemoyer, L., Cancedda, N., and Scialom, T.

Next-Token Prediction Should be Ambiguity-Sensitive: A Meta-Learning Perspective Schick, T., Dwivedi-Yu, J., Dess`ı, R., Raileanu, R., Lomeli, M., Hambro, E., Zettlemoyer, L., Cancedda, N., and Scialom, T

Reference 2010

Resolution: verified exact; doi, observed 2026-08-06T23:48:43.860153Z

Source-reported events for the cited work

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Observation b5b9b46a-6e8e-4a24-9594-38619402dfe5 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Next-Token Prediction Should be Ambiguity-Sensitive: A Meta-Learning Perspective DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 2016

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Observation c2151987-b613-4adf-9ef4-067511a0b3bb · outbound

This paper cites D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.

Next-Token Prediction Should be Ambiguity-Sensitive: A Meta-Learning Perspective D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al

Reference 2018

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Observation 9225116f-b9dc-4673-a31f-deaa807e29eb · outbound

This paper cites Function Vectors in Large Language Models.

Next-Token Prediction Should be Ambiguity-Sensitive: A Meta-Learning Perspective Function Vectors in Large Language Models

Reference 2019

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Observation ad3e892c-8a99-4457-a61d-9d0a1c7a44c4 · outbound

This paper cites Do llms un- derstand ambiguity in text? a case study in open-world question answering.

Next-Token Prediction Should be Ambiguity-Sensitive: A Meta-Learning Perspective Do llms un- derstand ambiguity in text? a case study in open-world question answering

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:48:47.065482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5763f258-be75-44b1-9827-cc292dce71bb · outbound

This paper cites Does learning the right latent variables necessarily improve in-context learning?.

Next-Token Prediction Should be Ambiguity-Sensitive: A Meta-Learning Perspective Does learning the right latent variables necessarily improve in-context learning?

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-06T23:48:41.127955Z

Source-reported events for the cited work

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Observation e1e8f6b3-cd97-4661-ace3-9b8430c64eb2 · outbound

This paper cites In-Context Language Learning: Architectures and Algorithms.

Next-Token Prediction Should be Ambiguity-Sensitive: A Meta-Learning Perspective In-Context Language Learning: Architectures and Algorithms

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-06T23:48:38.966817Z

Source-reported events for the cited work

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Observation 0f53c2e4-135e-4923-a896-8eb5ef557d51 · outbound

This paper cites A Theory of Emergent In-Context Learning as Implicit Structure Induction.

Next-Token Prediction Should be Ambiguity-Sensitive: A Meta-Learning Perspective A Theory of Emergent In-Context Learning as Implicit Structure Induction

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-06T23:48:39.925862Z

Source-reported events for the cited work

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Observation 2f038ec8-d941-4055-87ed-93ea990b4feb · outbound

This paper cites Adaptive Computation Time for Recurrent Neural Networks.

Next-Token Prediction Should be Ambiguity-Sensitive: A Meta-Learning Perspective Adaptive Computation Time for Recurrent Neural Networks

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-06T23:48:39.631083Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 90f3bcfb-2ae8-4fa9-805e-d66dce55b5d2 · outbound

This paper cites We're Afraid Language Models Aren't Modeling Ambiguity.

Next-Token Prediction Should be Ambiguity-Sensitive: A Meta-Learning Perspective We're Afraid Language Models Aren't Modeling Ambiguity

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-06T23:48:40.728627Z

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Pith citing papers

No inbound Pith citation observations are available.