Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-07T13:33:22.947565Z
Paper Citation Record · LEDGER
Breaking the Autoregressive Chain: Hyper-Parallel Decoding for Efficient LLM-Based Attribute Value Extraction
As of 4 August 2026, this Paper Citation Record lists 9 of 9 outbound references and 0 inbound Pith citation observations for arXiv:2604.26209.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-03T06:30:56.289259+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
9 of 9 outbound references displayed
- verified exact2
- verified fuzzy5
- unresolved0
- parse uncertain0
- malformed identifier1
- metadata mismatch1
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation c0e01740-9bd3-44f4-9b6a-29066a5e02fa · outbound
This paper cites InProceedings of the 2020 Con- ference on Empirical Methods in Natural Language Processing (EMNLP), pages 268–284.
Breaking the Autoregressive Chain: Hyper-Parallel Decoding for Efficient LLM-Based Attribute Value Extraction InProceedings of the 2020 Con- ference on Empirical Methods in Natural Language Processing (EMNLP), pages 268–284
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-27T05:38:38.538515Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation ee4bd050-b821-4bbd-826e-17e432af893f · outbound
This paper cites FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning.
Breaking the Autoregressive Chain: Hyper-Parallel Decoding for Efficient LLM-Based Attribute Value Extraction FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning
Reference 2
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-12T08:51:25.711704Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation ac8a0982-2697-4e45-9b84-e97fc8fcffaa · outbound
This paper cites Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoders.
Breaking the Autoregressive Chain: Hyper-Parallel Decoding for Efficient LLM-Based Attribute Value Extraction Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoders
Reference 3
- Resolution
- verified exact
- local_arxiv, observed 2026-05-12T08:51:25.717446Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation ef7ee5bf-39bb-49df-8fee-8a889a56031b · outbound
This paper cites Yaniv Leviathan, Matan Kalman, and Yossi Matias.
Breaking the Autoregressive Chain: Hyper-Parallel Decoding for Efficient LLM-Based Attribute Value Extraction Yaniv Leviathan, Matan Kalman, and Yossi Matias
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-27T05:38:38.526570Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 85861b8f-65e1-4bb6-9607-8a27998040e9 · outbound
This paper cites APAR: LLMs Can Do Auto-Parallel Auto-Regressive Decoding.
Breaking the Autoregressive Chain: Hyper-Parallel Decoding for Efficient LLM-Based Attribute Value Extraction APAR: LLMs Can Do Auto-Parallel Auto-Regressive Decoding
Reference 5
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T08:51:25.714733Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 2cd16d52-7972-4e3d-92eb-fca274cd1103 · outbound
This paper cites InProceedings of the Fif- teenth ACM International Conference on Web Search and Data Mining, WSDM ’22, page 1256–1265.
Breaking the Autoregressive Chain: Hyper-Parallel Decoding for Efficient LLM-Based Attribute Value Extraction InProceedings of the Fif- teenth ACM International Conference on Web Search and Data Mining, WSDM ’22, page 1256–1265
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-27T05:38:38.522739Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 3952ba9c-9208-45c8-b3fb-a7c830e55cd3 · outbound
This paper cites InProceedings of the 2020 Con- ference on Empirical Methods in Natural Language Processing (EMNLP), pages 2407–2417, Online.
Breaking the Autoregressive Chain: Hyper-Parallel Decoding for Efficient LLM-Based Attribute Value Extraction InProceedings of the 2020 Con- ference on Empirical Methods in Natural Language Processing (EMNLP), pages 2407–2417, Online
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-27T05:38:38.531100Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation b569ecd5-0c79-4571-b3de-c3ebf9beced2 · outbound
This paper cites A Survey on Efficient Inference for Large Language Models.
Breaking the Autoregressive Chain: Hyper-Parallel Decoding for Efficient LLM-Based Attribute Value Extraction A Survey on Efficient Inference for Large Language Models
Reference 8
- Resolution
- malformed identifier
- arxiv_id, observed 2026-05-15T02:39:33.856027Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation dfd8173d-e87a-4326-bd0f-c186b611ab7c · outbound
This paper cites correct", CN=.
Breaking the Autoregressive Chain: Hyper-Parallel Decoding for Efficient LLM-Based Attribute Value Extraction correct", CN=
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-27T05:38:38.534654Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Pith citing papers
No inbound Pith citation observations are available.