Pith. sign in

Paper Citation Record · LEDGER

Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 inbound Pith citation observations for arXiv:2104.08771.

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

pith.paper-citation-record.v1
2104.08771 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 13 of 13 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:45:59.384043Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T10:27:56.869648Z

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 31b3a4f7-d5e1-4ab3-b6cf-59849bd93974 · inbound

Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey cites this paper.

Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation

Reference 73

Resolution
verified exact
arxiv_id, observed 2026-05-13T11:32:36.935383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-13T11:32:36.738536Z digest=sha256:544934b3befbf90fd266eeea9b78387c45e3d2ce3113daa3bdcebc3f035e9e3c

Observation e6bd5b49-6839-403b-a668-fbdc7677c71d · inbound

Generalizable Radio-Frequency Radiance Fields for Spatial Spectrum Synthesis cites this paper.

Generalizable Radio-Frequency Radiance Fields for Spatial Spectrum Synthesis Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation

Reference 16

Resolution
verified exact
arxiv_id, observed 2026-05-23T03:52:29.664581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-23T03:48:06.278544Z digest=sha256:46499ee2286842d9a21fa8732783a5bc5ca44fd1b0d5ed3b14a15839cfcd7227

Observation fb7ef939-5c21-4a1d-af87-bf16968e097a · inbound

Mixture of Low Rank Adaptation with Partial Parameter Sharing for Time Series Forecasting cites this paper.

Mixture of Low Rank Adaptation with Partial Parameter Sharing for Time Series Forecasting Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-07T14:45:59.384043Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:45:59.384043Z digest=sha256:1a26e1c91b69978d56359f5e4fc6f07014965d8cc1f21d97d529676f66972ead

Observation ebe0dc82-6d24-47d1-9a7e-10b1ddef8cf9 · inbound

BlastOFormer: Attention and Neural Operator Deep Learning Methods for Explosive Blast Prediction cites this paper.

BlastOFormer: Attention and Neural Operator Deep Learning Methods for Explosive Blast Prediction Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-07T13:58:18.949329Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:58:18.949329Z digest=sha256:fc6ff84bba87993957a94ceea6e38da26320237314c66709d73a4b8c45b75563

Observation 95e8b8db-8584-4b0c-91fa-6b7c367415db · inbound

TALL -- A Trainable Architecture for Enhancing LLM Performance in Low-Resource Languages cites this paper.

TALL -- A Trainable Architecture for Enhancing LLM Performance in Low-Resource Languages Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-07T10:32:45.330650Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T10:32:45.330650Z digest=sha256:d8cefc78c8b3c10a2447cfa1215f57b8031e0b460181a00093ea05a7a5b1d455

Observation 6102ceff-bbf2-45f1-8b94-602a10a7f861 · inbound

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models cites this paper.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-06T22:50:12.706998Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:50:12.706998Z digest=sha256:1b9bc6a45dd043e3c1edbaf87f8c6cbb3a63f6b0b365df1915a37b5c6b1f1daf

Observation 7021283b-f1d0-4f7b-87bf-90266815e3c6 · inbound

Krul: Efficient State Restoration for Multi-turn Conversations with Dynamic Cross-layer KV Sharing cites this paper.

Krul: Efficient State Restoration for Multi-turn Conversations with Dynamic Cross-layer KV Sharing Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-06T18:46:57.357120Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:46:57.357120Z digest=sha256:224a6111242583d47542c6fd0cd7653346b647d4e10c12226e9c8f7717d24140

Observation ae4eea82-f5f5-4a74-a509-c016a9ae35b1 · inbound

Causal Fingerprints of AI Generative Models cites this paper.

Causal Fingerprints of AI Generative Models Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation

Reference 29

Resolution
verified exact
arxiv_id, observed 2026-05-18T15:31:33.686903Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-18T15:28:25.607154Z digest=sha256:bbc3ad7766e487fc0b15c051b703c832d01a8f39781faeac1f65f5a92df580de

Observation e69a5714-8108-4508-b397-fc032006951f · inbound

One Prompt, Many Sounds: Modeling Listener Variability in LLM-Based Equalization cites this paper.

One Prompt, Many Sounds: Modeling Listener Variability in LLM-Based Equalization Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation

Reference 40

Resolution
verified exact
arxiv_id, observed 2026-05-16T14:27:59.206139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-16T14:26:36.236424Z digest=sha256:e528191979b1afb301ba449a7f4e1eb351cb996ed13e7979bdcf30a57e25127d

Observation 2a755685-540f-4bba-b0c9-afa0df7593ce · inbound

One Prompt, Many Sounds: Modeling Listener Variability in LLM-Based Equalization cites this paper.

One Prompt, Many Sounds: Modeling Listener Variability in LLM-Based Equalization Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-03T10:39:37.529785Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T10:39:37.529785Z digest=sha256:58bd2ce2d7f459b21d48c1a6994524d19e543d372394ac0590d1eb37224bc912

Observation c9f8285b-c8b4-4538-ae47-91d3ff49cc94 · inbound

Value-Decomposed Reinforcement Learning Framework for Taxiway Routing with Hierarchical Conflict-Aware Observations cites this paper.

Value-Decomposed Reinforcement Learning Framework for Taxiway Routing with Hierarchical Conflict-Aware Observations Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation

Reference 35

Resolution
verified exact
arxiv_id, observed 2026-05-12T08:01:32.349501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-05-12T01:21:47.109822Z digest=sha256:478d0368c6e238dcc10c0b21a65fb57958f2b660efd078a480b5e7b216f1fae2

Observation 7005529e-ca52-432e-bd59-937f572b9022 · inbound

Value-Decomposed Reinforcement Learning Framework for Taxiway Routing with Hierarchical Conflict-Aware Observations cites this paper.

Value-Decomposed Reinforcement Learning Framework for Taxiway Routing with Hierarchical Conflict-Aware Observations Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation

Reference 35

Resolution
verified exact
arxiv_id, observed 2026-05-13T07:17:28.913162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-05-13T07:14:59.491808Z digest=sha256:800f6139a834d8699a3f5ac57b3241205f77555736494f3a81ac71c866620c86

Observation ed6b878f-c4a1-414a-9687-7578f0441b89 · inbound

Time-Conditioned and Multi-Time Survival Prediction from 2D PET/CT Projections in Lung Cancer cites this paper.

Time-Conditioned and Multi-Time Survival Prediction from 2D PET/CT Projections in Lung Cancer Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-07-03T10:27:56.871192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-27T09:59:19.332545Z digest=sha256:233c720fedeaad9f7b199ea443a422d5fdfadca88b35bc62eda944b83653877c