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Paper Citation Record · LEDGER

Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation

As of 20 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 0 inbound Pith citation observations for arXiv:2601.22546.

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

pith.paper-citation-record.v1
2601.22546 v2

Coverage vector

measured 51 of 51 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-03T06:38:26.239585Z

measured 51 of 51 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 0 of 0 inbound itemization

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measured 0 of 1 external citation measurements

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Reference resolution

51 of 51 outbound references displayed

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Outbound references

Observation 38494005-0e78-4ba1-a5b0-ddb93788f42f · outbound

This paper cites Gqa: Training generalized multi-query transformer models from multi-head checkpoints, in: The 2023 Conference on Empirical Methods in Natural Language Processing.

Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation Gqa: Training generalized multi-query transformer models from multi-head checkpoints, in: The 2023 Conference on Empirical Methods in Natural Language Processing

Reference 1

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Observation 643048f8-0f72-4ad1-b82f-6faad2a86ac6 · outbound

This paper cites PaLM 2 Technical Report.

Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation PaLM 2 Technical Report

Reference 2

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Observation 99d8ae49-fcaf-4b4c-901c-655b3b18752f · outbound

This paper cites Plato-2:Towardsbuildinganopen-domainchatbotvia curriculum learning, in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pp.

Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation Plato-2:Towardsbuildinganopen-domainchatbotvia curriculum learning, in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pp

Reference 3

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Observation fcc3db24-9273-4276-9f66-7eb3f9c82c29 · outbound

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Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation Unresolved cited work

Reference 4

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Observation a0c6badd-8f65-406b-972e-99912f690b32 · outbound

This paper cites Glat:Glancingatlatentvariablesforparalleltextgeneration, in: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp.

Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation Glat:Glancingatlatentvariablesforparalleltextgeneration, in: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp

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Observation 0f100e3c-1ba8-4785-a4e1-ad63dd532e8e · outbound

This paper cites Sparks of Artificial General Intelligence: Early experiments with GPT-4.

Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation Sparks of Artificial General Intelligence: Early experiments with GPT-4

Reference 6

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Observation cc4174a5-8f34-4af4-80b5-8c82acdfcd34 · outbound

This paper cites Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads.

Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads

Reference 7

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Observation a32d5cfd-dde4-4f47-8826-6abf0c64a9ce · outbound

This paper cites YAKE! Collection-Independent Automatic Keyword Extractor.

Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation YAKE! Collection-Independent Automatic Keyword Extractor

Reference 8

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Observation 1030f10c-9219-4728-b09c-72f6b1ea42db · outbound

This paper cites Accelerating Large Language Model Decoding with Speculative Sampling.

Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation Accelerating Large Language Model Decoding with Speculative Sampling

Reference 9

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Observation 1a218641-116b-4b8f-bf11-f9ee74bb2931 · outbound

This paper cites Learning to memorize entailment and discourse relations for persona-consistent dialogues, in: Proceedings of the AAAI conference on artificial intelligence, pp.

Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation Learning to memorize entailment and discourse relations for persona-consistent dialogues, in: Proceedings of the AAAI conference on artificial intelligence, pp

Reference 10

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Observation a7408400-63bd-40a4-8745-4b5bbc91004c · outbound

This paper cites PaLM: Scaling Language Modeling with Pathways.

Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation PaLM: Scaling Language Modeling with Pathways

Reference 11

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Observation 5c8ce92e-f966-4db8-b2a1-2e373656cf37 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 12

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Observation 7b90ddc2-dd06-47e1-a449-2defbdb91208 · outbound

This paper cites Wizard of wikipedia: Knowledge-powered conversational agents, in: International Conference on Learning Representations.

Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation Wizard of wikipedia: Knowledge-powered conversational agents, in: International Conference on Learning Representations

Reference 13

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Observation 40af7e74-2b97-4801-95e8-97c5fb23593c · outbound

This paper cites Understandingandimprovinglexicalchoiceinnon-autoregressivetranslation, in: International Conference on Learning Representations.

Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation Understandingandimprovinglexicalchoiceinnon-autoregressivetranslation, in: International Conference on Learning Representations

Reference 14

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Observation 27f95582-2bee-4410-b34c-4fc9a7cf1fda · outbound

This paper cites GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers.

Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

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Observation 79cce821-2df2-4a78-9e78-63ae0d7bf354 · outbound

This paper cites How does gpt obtain its ability? tracing emergent abilities of language models to their sources.

Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation How does gpt obtain its ability? tracing emergent abilities of language models to their sources

Reference 16

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Observation 0ae0a675-a9fb-481e-b497-f1926fa59fea · outbound

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Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation Unresolved cited work

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Observation d5ee81f2-9e02-4e28-816d-f288ab49051a · outbound

This paper cites Non-autoregressive neural machine translation, in: International Conference on Learning Representations (ICLR).

Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation Non-autoregressive neural machine translation, in: International Conference on Learning Representations (ICLR)

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Observation d573642e-eee8-49e3-b42f-fc6e7e05c060 · outbound

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Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation Unresolved cited work

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Observation 747fea7e-534b-40df-8f6d-0fa858c0ebff · outbound

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Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation Unresolved cited work

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Observation 73de5433-a363-404a-a014-6a1aca076fb2 · outbound

This paper cites Improvingnon-autoregressivetranslationmodelswithoutdistillation,in:InternationalConference on Learning Representations.

Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation Improvingnon-autoregressivetranslationmodelswithoutdistillation,in:InternationalConference on Learning Representations

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Observation 395036c6-4ec3-47c8-a9f1-c824402ad838 · outbound

This paper cites Belle: Be everyone’s large language model engine.https://github.

Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation Belle: Be everyone’s large language model engine.https://github

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Observation 0985625e-76f9-437f-ad97-54c08b11d733 · outbound

This paper cites SqueezeLLM: Dense-and-Sparse Quantization.

Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation SqueezeLLM: Dense-and-Sparse Quantization

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Observation 3ccddc24-6558-4c89-8309-71758dc101f6 · outbound

This paper cites Efficient memory management for large language model serving with pagedattention, in: Proceedings of the 29th Symposium on Operating Systems Principles, pp.

Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation Efficient memory management for large language model serving with pagedattention, in: Proceedings of the 29th Symposium on Operating Systems Principles, pp

Reference 24

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Observation 40523bb7-840c-4176-be7f-2e4c0f5f7cf2 · outbound

This paper cites Fast inference from transformers via speculative decoding, in: International Conference on Machine Learning, PMLR.

Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation Fast inference from transformers via speculative decoding, in: International Conference on Machine Learning, PMLR

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Observation 343aebdf-befb-4ef9-bbf4-45d7d3b0db7f · outbound

This paper cites A Diversity-Promoting Objective Function for Neural Conversation Models.

Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation A Diversity-Promoting Objective Function for Neural Conversation Models

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Observation c5138aea-7360-4425-91a9-92b60ab11276 · outbound

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Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation Unresolved cited work

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Observation 1f1e0dd7-be05-4215-89ad-b506a494f724 · outbound

This paper cites G-eval: Nlg evaluation using gpt-4 with better human alignment, in: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp.

Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation G-eval: Nlg evaluation using gpt-4 with better human alignment, in: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp

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Observation 39420f4a-9755-4264-9d37-04e201f48ee9 · outbound

This paper cites Are Emergent Abilities in Large Language Models just In-Context Learning?.

Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation Are Emergent Abilities in Large Language Models just In-Context Learning?

Reference 29

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Observation 1fbc296b-fb3e-4e4d-b424-24b7a6915b44 · outbound

This paper cites SpecInfer: Accelerating Generative Large Language Model Serving with Tree-based Speculative Inference and Verification.

Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation SpecInfer: Accelerating Generative Large Language Model Serving with Tree-based Speculative Inference and Verification

Reference 30

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Observation 9a4f0955-1173-456a-93bf-83876b6b7a99 · outbound

This paper cites Bleu: a method for automatic evaluation of machine translation, in: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp.

Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation Bleu: a method for automatic evaluation of machine translation, in: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp

Reference 31

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Observation f01d6135-5f46-49f1-8ac3-5fce494bd7ea · outbound

This paper cites Guiding non-autoregressive neural machine translation decoding with reordering information, in: Proceedings of the AAAI Conference on Artificial Intelligence, pp.

Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation Guiding non-autoregressive neural machine translation decoding with reordering information, in: Proceedings of the AAAI Conference on Artificial Intelligence, pp

Reference 32

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Observation 96c750c0-91ad-45d9-b07d-f0101b56823d · outbound

This paper cites Non-autoregressive machine translation with latent alignments, in: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp.

Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation Non-autoregressive machine translation with latent alignments, in: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp

Reference 33

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Observation 69051fe0-fdae-4f41-8648-2fcb4d329fbe · outbound

This paper cites Are emergent abilities of large language models a mirage? Advances in Neural Information Processing Systems 36.

Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation Are emergent abilities of large language models a mirage? Advances in Neural Information Processing Systems 36

Reference 34

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Observation 59ef127c-b125-49b2-8085-50c093ff4c82 · outbound

This paper cites an unresolved cited work.

Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation Unresolved cited work

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Observation 98718e3d-b721-4645-afe7-c006615e3821 · outbound

This paper cites Accelerating llm inference with staged speculative decoding, in: Workshop on Efficient Systems for Foundation Models@ ICML2023.

Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation Accelerating llm inference with staged speculative decoding, in: Workshop on Efficient Systems for Foundation Models@ ICML2023

Reference 36

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Observation a163674c-dbc7-4c4a-86c1-198d459fab9d · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation LLaMA: Open and Efficient Foundation Language Models

Reference 37

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Observation 917b1b96-dd3e-43db-a5de-865efcd0544a · outbound

This paper cites A Large-Scale Chinese Short-Text Conversation Dataset.

Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation A Large-Scale Chinese Short-Text Conversation Dataset

Reference 38

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Observation 0d1f649e-6420-41ac-9341-0b2fb4d9ab74 · outbound

This paper cites an unresolved cited work.

Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation Unresolved cited work

Reference 39

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source=pdf_text observed=2026-08-03T06:38:25.002447Z digest=sha256:767cb46b7355484380f748cc3bc0f1580798681b83f3ff8b2168877b86b64210

Observation 966541c9-6a62-4ef1-b1a0-375cd2fee106 · outbound

This paper cites Emergent Abilities of Large Language Models.

Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation Emergent Abilities of Large Language Models

Reference 40

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Observation eaad5adb-a540-40e1-b2e1-c300d8e4872a · outbound

This paper cites Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.

Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Reference 41

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source=pdf_text observed=2026-08-03T06:38:25.228554Z digest=sha256:1bb6616e10b36fa87e2c3e3fb700686ec5ee709a586901ac81df56e0475c3746

Observation 3d733739-c299-4344-9644-2bdfdffbf7f6 · outbound

This paper cites an unresolved cited work.

Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation Unresolved cited work

Reference 42

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source=pdf_text observed=2026-08-03T06:38:25.363545Z digest=sha256:50d2d86bdbc2b2de8f3fa94692d3e5cc3e8157029cc1be17446bf7a2daf22381

Observation 856b312c-e034-4921-9436-c987b4274780 · outbound

This paper cites Smoothquant: Accurate and efficient post-training quantization for large language models, in: International Conference on Machine Learning, PMLR.

Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation Smoothquant: Accurate and efficient post-training quantization for large language models, in: International Conference on Machine Learning, PMLR

Reference 43

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Observation 045c0603-796b-468f-96a1-578743aeb42c · outbound

This paper cites Asurveyonnon-autoregressivegenerationforneuralmachinetranslation and beyond.

Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation Asurveyonnon-autoregressivegenerationforneuralmachinetranslation and beyond

Reference 44

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source=pdf_text observed=2026-08-03T06:38:25.574829Z digest=sha256:1990428f33e250aa7c51d8c4ad4568f8d0fc724b548ee773859c12c1f13c0645

Observation 95442f78-d93b-4026-9afa-cbd503dae63d · outbound

This paper cites An explanation of in-context learning as implicit bayesian inference, in: International Conference on Learning Representations.

Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation An explanation of in-context learning as implicit bayesian inference, in: International Conference on Learning Representations

Reference 45

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Observation b9a12df0-ab6f-47cd-9ea5-f0a2c3a5418e · outbound

This paper cites GLM-130b: An open bilingual pre-trained model, in: The Eleventh International Conference on Learning Representations (ICLR).

Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation GLM-130b: An open bilingual pre-trained model, in: The Eleventh International Conference on Learning Representations (ICLR)

Reference 46

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Observation 3aa9dd53-0a21-46dc-9ef4-f3842b0944de · outbound

This paper cites GLM-130B: An Open Bilingual Pre-trained Model.

Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation GLM-130B: An Open Bilingual Pre-trained Model

Reference 47

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Observation 52d8bb3c-9135-426c-9067-787698760169 · outbound

This paper cites OPT: Open Pre-trained Transformer Language Models.

Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation OPT: Open Pre-trained Transformer Language Models

Reference 48

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Observation 54c03478-3499-4ea7-a5c7-9a8363bf1d89 · outbound

This paper cites an unresolved cited work.

Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation Unresolved cited work

Reference 49

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Observation 1bd9d050-e8d9-45f3-8a7a-e65abf4d03f8 · outbound

This paper cites an unresolved cited work.

Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation Unresolved cited work

Reference 50

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source=pdf_text observed=2026-08-03T06:38:26.108386Z digest=sha256:fb477a998274f767b12a092b64b860bf05561a8da1596c4062e28f8c14b7e30c

Observation 4855820e-5fbd-4791-8e15-ad11e57a4857 · outbound

This paper cites KPT: Keyword-guided Pre-training for Grounded Dialog Generation.

Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation KPT: Keyword-guided Pre-training for Grounded Dialog Generation

Reference 51

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source=pdf_text observed=2026-08-03T06:38:26.239585Z digest=sha256:5b47b76e4ba6fed82a7e090840814e93e51a310b3a708419dbc432c90866408b

Pith citing papers

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