Paper Citation Record · LEDGER

Hidden Biases in Conditioning Autoregressive Models

As of 10 August 2026, this Paper Citation Record lists 11 of 11 outbound references and 0 inbound Pith citation observations for arXiv:2604.07855.

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

pith.paper-citation-record.v1
2604.07855 v1

Coverage vector

measured 11 of 11 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-10T17:25:16.452480Z

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+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

11 of 11 outbound references displayed

verified exact1
verified fuzzy10
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation dabfd016-c0cd-4ca5-8bc0-ef87fe14d200 · outbound

This paper cites Is MAP Decoding All You Need? The Inadequacy of the Mode in Neural Machine Translation.

Hidden Biases in Conditioning Autoregressive Models Is MAP Decoding All You Need? The Inadequacy of the Mode in Neural Machine Translation

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T10:34:26.972948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d987f7f8-767b-425c-b62b-3888802c8ab1 · outbound

This paper cites Language (Technology) is Power: A Critical Survey of “Bias” in NLP.

Hidden Biases in Conditioning Autoregressive Models Language (Technology) is Power: A Critical Survey of “Bias” in NLP

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T10:34:26.976097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 82437009-5751-4ff1-b209-507bce397d49 · outbound

This paper cites If Beam Search is the Answer, What was the Question? InProceedings of EMNLP 2020, pages 2173–2185.

Hidden Biases in Conditioning Autoregressive Models If Beam Search is the Answer, What was the Question? InProceedings of EMNLP 2020, pages 2173–2185

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T10:34:26.956312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d8271d4c-08e0-4c99-8ffb-4b92cbefbc0a · outbound

This paper cites On NMT Search Errors and Model Errors: Cat Got Your Tongue? InProceedings of EMNLP-IJCNLP 2019, pages 3356–3362.

Hidden Biases in Conditioning Autoregressive Models On NMT Search Errors and Model Errors: Cat Got Your Tongue? InProceedings of EMNLP-IJCNLP 2019, pages 3356–3362

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T10:34:26.948651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation aa4cad0e-5f3f-4c8f-b603-5fb938fb3826 · outbound

This paper cites Anticipation-RNN: enforcing unary constraints in sequence generation, with application to interactive music generation.Neural Computing and Applications, 32:995–1005.

Hidden Biases in Conditioning Autoregressive Models Anticipation-RNN: enforcing unary constraints in sequence generation, with application to interactive music generation.Neural Computing and Applications, 32:995–1005

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T10:34:26.953500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4fb38c8b-4688-45f9-a058-e7025c8901fd · outbound

This paper cites Learning to Traverse Latent Spaces for Musical Score Inpainting.

Hidden Biases in Conditioning Autoregressive Models Learning to Traverse Latent Spaces for Musical Score Inpainting

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T10:34:26.970262Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation cff46a8d-5b96-49e7-b1ff-dbb3e0d0e31e · outbound

This paper cites The Piano Inpainting Application.

Hidden Biases in Conditioning Autoregressive Models The Piano Inpainting Application

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-11T06:51:25.276333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ebfbcda0-1cd3-4089-9aa7-bdb0da07319d · outbound

This paper cites Enabling Language Models to Fill in the Blanks.

Hidden Biases in Conditioning Autoregressive Models Enabling Language Models to Fill in the Blanks

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T10:34:26.967479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c995704c-6027-491d-9e02-c53b834ec6ac · outbound

This paper cites DeepBach: a Steerable Model for Bach Chorales Generation.

Hidden Biases in Conditioning Autoregressive Models DeepBach: a Steerable Model for Bach Chorales Generation

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T10:34:26.964807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 27e244a8-e50d-4921-a9ea-e6e192b4bb67 · outbound

This paper cites Markov Constraints for Controlled Sequence Generation.

Hidden Biases in Conditioning Autoregressive Models Markov Constraints for Controlled Sequence Generation

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T10:34:26.961977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f583ebca-c6dc-4dad-b7d3-3969c583d75b · outbound

This paper cites Exact Sampling for Regular and Markov Constraints with Belief Propagation.

Hidden Biases in Conditioning Autoregressive Models Exact Sampling for Regular and Markov Constraints with Belief Propagation

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T10:34:26.959253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Pith citing papers

No inbound Pith citation observations are available.