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Paper Citation Record · LEDGER

Optimising Language Models for Downstream Tasks: A Post-Training Perspective

As of 7 August 2026, this Paper Citation Record lists 100 of 277 outbound references and 0 inbound Pith citation observations for arXiv:2506.20917.

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

pith.paper-citation-record.v1
2506.20917 v1

Coverage vector

measured 100 of 277 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:44:44.107619Z

measured 100 of 100 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

100 of 277 outbound references displayed

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Outbound references

Observation 4b73827d-5f4f-4f31-819b-a794dfb137a7 · outbound

This paper cites Nemotron-4 340B Technical Report.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Nemotron-4 340B Technical Report

Reference 2

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Observation 8d58da42-ab95-45b2-a929-d5c97fcc36c8 · outbound

This paper cites Publicly available clinical BERT embeddings.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Publicly available clinical BERT embeddings

Reference 3

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Observation d875b45e-72aa-4a79-97fe-5717c8862855 · outbound

This paper cites Reid, Stephen Gould, and Anton van den Hengel.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Reid, Stephen Gould, and Anton van den Hengel

Reference 4

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Observation de2b65ba-e9fa-490f-b0aa-9524bb270450 · outbound

This paper cites Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning

Reference 5

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Observation 13c2475a-6020-4db0-bf03-fbd2d68919fc · outbound

This paper cites Tran, Dara Bahri, Jianmo Ni, Jai Prakash Gupta, Kai Hui, Sebastian Ruder, and Donald Metzler.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Tran, Dara Bahri, Jianmo Ni, Jai Prakash Gupta, Kai Hui, Sebastian Ruder, and Donald Metzler

Reference 6

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Observation b706fb71-2eaa-44fe-b1ee-ab20c66b1dcf · outbound

This paper cites A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings

Reference 7

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Observation 7c41c763-bf8e-4833-8255-181336720d8c · outbound

This paper cites ATTEMPT: Parameter-efficient multi-task tuning via attentional mixtures of soft prompts.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective ATTEMPT: Parameter-efficient multi-task tuning via attentional mixtures of soft prompts

Reference 8

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Observation cb805e14-e2aa-457e-93b7-6ac967060ede · outbound

This paper cites Program Synthesis with Large Language Models.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Program Synthesis with Large Language Models

Reference 9

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Observation 326ed919-0310-4d12-96cc-abb66d392730 · outbound

This paper cites A general theoret- ical paradigm to understand learning from human preferences.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective A general theoret- ical paradigm to understand learning from human preferences

Reference 10

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Observation c588d5d2-b57b-4974-9429-dfde47e4da1b · outbound

This paper cites Qwen Technical Report.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Qwen Technical Report

Reference 11

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Observation 73ab6ab2-a9fc-4a1f-9b19-3e278d9fb56f · outbound

This paper cites Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 12

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Observation 1d63966b-582c-458a-a353-c8ce30255fa7 · outbound

This paper cites Brain power.Proceedings of the National Academy of Sciences, 118(32):e2107022118, 2021.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Brain power.Proceedings of the National Academy of Sciences, 118(32):e2107022118, 2021

Reference 13

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Observation c1dcd433-9c1f-4cc8-9b5a-81a82ff68d29 · outbound

This paper cites SciBERT: A pretrained language model for scientific text.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective SciBERT: A pretrained language model for scientific text

Reference 14

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Observation 45cd9730-f44b-49f8-b7a3-f76a57fa7777 · outbound

This paper cites BitFit: Sim- ple parameter-efficient fine-tuning for transformer-based masked language- models.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective BitFit: Sim- ple parameter-efficient fine-tuning for transformer-based masked language- models

Reference 15

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Observation f4135c37-405d-45af-8bad-6890dd4292b4 · outbound

This paper cites Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell

Reference 16

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Observation fe5548a5-b899-4d00-bc42-83efd4540425 · outbound

This paper cites Bender and Alexander Koller.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Bender and Alexander Koller

Reference 17

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Observation e892c41f-67e4-4c8e-b2f6-0e9409c15c00 · outbound

This paper cites Curriculum learning.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Curriculum learning

Reference 18

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Observation 8ba42f1b-4a05-44d1-a5f5-188fe7c5356d · outbound

This paper cites The fifth PASCAL recognizing textual entailment challenge.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective The fifth PASCAL recognizing textual entailment challenge

Reference 19

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Observation a0832af2-56e1-47af-8bba-2b9b04704280 · outbound

This paper cites Cubuk, Alex Kurakin, Kihyuk Sohn, Han Zhang, and Colin Raffel.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Cubuk, Alex Kurakin, Kihyuk Sohn, Han Zhang, and Colin Raffel

Reference 20

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Observation 3c194945-8026-4ce1-ab53-234328ee7e19 · outbound

This paper cites Goodfellow, Nicolas Papernot, Avital Oliver, and Colin Raffel.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Goodfellow, Nicolas Papernot, Avital Oliver, and Colin Raffel

Reference 21

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Observation 49f58851-1f47-4f28-8cd0-f06bb0480165 · outbound

This paper cites Adamatch: A unified approach to semi-supervised learning and domain adaptation.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Adamatch: A unified approach to semi-supervised learning and domain adaptation

Reference 22

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Observation 647b04ba-67f9-4605-8227-34babddd1e3c · outbound

This paper cites Shih, Yejin Choi, and Daniel Marcu.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Shih, Yejin Choi, and Daniel Marcu

Reference 23

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Observation 7695e2b0-2af6-4ec2-a284-36335d8dfe45 · outbound

This paper cites PIQA: reasoning about physical commonsense in natural language.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective PIQA: reasoning about physical commonsense in natural language

Reference 24

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Observation 1afe4fb7-f00c-4da2-bf75-566cd5d388d2 · outbound

This paper cites Bowman, Gabor Angeli, Christopher Potts, and Christopher D.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Bowman, Gabor Angeli, Christopher Potts, and Christopher D

Reference 25

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Observation 88dc5620-7baf-42b8-ba46-bec13b826d0b · outbound

This paper cites an unresolved cited work.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Unresolved cited work

Reference 26

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Observation 0ba7bc8b-8f5b-4ba0-aee8-fd2cf466cfaf · outbound

This paper cites Semi-supervised semantic role labeling with cross-view training.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Semi-supervised semantic role labeling with cross-view training

Reference 27

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Observation e9b5ec18-87be-4a50-aaf6-7afa6c962e28 · outbound

This paper cites Findings of the 2009 Workshop on Statistical Machine Translation.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Findings of the 2009 Workshop on Statistical Machine Translation

Reference 28

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Observation 64557a6d-0750-4240-86e8-c773f78a6539 · outbound

This paper cites Extracting training data from large language models.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Extracting training data from large language models

Reference 29

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Observation 1dd787d0-4e16-421b-b31d-acd77c5e2bf0 · outbound

This paper cites SemEval-2017 task 1: Semantic textual similarity multilingual and crosslingual focused evaluation.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective SemEval-2017 task 1: Semantic textual similarity multilingual and crosslingual focused evaluation

Reference 31

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Observation f8595921-d963-4d70-9f72-775a66f409b4 · outbound

This paper cites Importance of semantic representation: dataless classification.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Importance of semantic representation: dataless classification

Reference 32

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Observation afb4a726-e2b8-409d-8cf9-fafd94f1b3db · outbound

This paper cites Semi-supervised BIBLIOGRAPHY 121 learning (chapelle, o.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Semi-supervised BIBLIOGRAPHY 121 learning (chapelle, o

Reference 33

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Observation a6fe9601-807c-43b7-8236-ab98669c0721 · outbound

This paper cites Code alpaca: An instruction-following llama model for code generation.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Code alpaca: An instruction-following llama model for code generation

Reference 34

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Observation 514b73ed-c09b-4d89-b55f-282201ca692b · outbound

This paper cites Debiased self-training for semi-supervised learning.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Debiased self-training for semi-supervised learning

Reference 35

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Observation 98443fdc-0446-422f-b965-4ec3a5b44748 · outbound

This paper cites Unseen filler generalization in attention-based natural language reasoning models.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Unseen filler generalization in attention-based natural language reasoning models

Reference 36

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Observation 386a0536-a5ec-470d-b323-6eda5ce5e618 · outbound

This paper cites TOUCHDOWN: natural language navigation and spatial reasoning in visual street environments.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective TOUCHDOWN: natural language navigation and spatial reasoning in visual street environments

Reference 37

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Observation b962e938-27ce-4e8f-aa7c-9faf82ffd4e7 · outbound

This paper cites MixText: Linguistically-informed interpolation of hidden space for semi-supervised text classification.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective MixText: Linguistically-informed interpolation of hidden space for semi-supervised text classification

Reference 38

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Observation f9e4a0c8-d035-4a5e-88cc-d8cf8c5af729 · outbound

This paper cites Alpagasus: Training a better alpaca model with fewer data.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Alpagasus: Training a better alpaca model with fewer data

Reference 39

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Observation 9c8fff1d-b1a5-4387-87e1-cd0d4ad17d28 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Evaluating Large Language Models Trained on Code

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Observation 7f06129c-3df5-41a6-b17c-859f4f8c4064 · outbound

This paper cites Microsoft COCO Captions: Data Collection and Evaluation Server.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Microsoft COCO Captions: Data Collection and Evaluation Server

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Observation 4ceffca8-0791-4934-b47d-4c6d2435dd28 · outbound

This paper cites Adapt- ing language models to compress contexts.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Adapt- ing language models to compress contexts

Reference 42

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Observation fa025a4d-3938-4dc7-ae30-02f10aee1c4f · outbound

This paper cites Gonza- lez, Ion Stoica, and Eric P.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Gonza- lez, Ion Stoica, and Eric P

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source=pdf_text observed=2026-08-06T22:44:43.845371Z digest=sha256:7fe5bca3d8975e5182a86c856fd8fe0f0ede3e63cd72dcded93eb41b458aec1e

Observation 9c40c8b1-87b4-48d4-af4d-f8c49374f510 · outbound

This paper cites PaLM: Scaling Language Modeling with Pathways.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective PaLM: Scaling Language Modeling with Pathways

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Observation 45174e3b-1722-46a6-a373-2527451532a5 · outbound

This paper cites Deep reinforcement learning from human preferences.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Deep reinforcement learning from human preferences

Reference 45

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source=pdf_text observed=2026-08-06T22:44:43.854454Z digest=sha256:fdafb06e14a120f3ace4ca34e7866e333a061434a38072b36c925c1521f58e4c

Observation d17e3aa2-e9d1-4863-9736-8477ecfe52a5 · outbound

This paper cites Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V

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source=pdf_text observed=2026-08-06T22:44:43.858540Z digest=sha256:d6cb7b728b4bd86756f7563047eee7adbdb1eaffcabc8c01f3310cefd3927946

Observation 8acdc684-4fc2-4f76-b0dc-e2f9b1611de0 · outbound

This paper cites BoolQ: Exploring the surprising difficulty of natural yes/no questions.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective BoolQ: Exploring the surprising difficulty of natural yes/no questions

Reference 47

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Observation 6e83cee6-2d08-4b0f-ba24-1a8539d6dc57 · outbound

This paper cites Manning, and Quoc Le.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Manning, and Quoc Le

Reference 48

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source=pdf_text observed=2026-08-06T22:44:43.867863Z digest=sha256:5a88b90b4357a73671b86c09f15848e3069f43304ecb30185011cac1c8b66b3e

Observation b4666c98-7120-4c9d-be79-d49331aab7b6 · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 49

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source=pdf_text observed=2026-08-06T22:44:43.871934Z digest=sha256:b5f30c8a8c778175f1dae5f726066c04894cf48854809b91f695f930f8d96196

Observation 6cacfbce-4e24-404a-956c-d73ec416b0ad · outbound

This paper cites Training verifiers to solve math word problems, 2021.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Training verifiers to solve math word problems, 2021

Reference 50

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source=pdf_text observed=2026-08-06T22:44:43.876140Z digest=sha256:bd1f0d471e9b1cd971c4d4361caaec3ec574e4dfcace25b342af05d20abead0f

Observation 3dc26713-97e4-4cf2-bb77-6646961dcab3 · outbound

This paper cites Free dolly: Introducing the world’s first truly open instruction-tuned llm, 2023.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Free dolly: Introducing the world’s first truly open instruction-tuned llm, 2023

Reference 51

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source=pdf_text observed=2026-08-06T22:44:43.880797Z digest=sha256:6c9b39f2bda50bdedb6eaf8cda6d983b9211f6e35391708d00bd5545df34095b

Observation 4cd63697-8582-43e4-b949-2fed3f54d4b6 · outbound

This paper cites The PASCAL recog- nising textual entailment challenge.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective The PASCAL recog- nising textual entailment challenge

Reference 52

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source=pdf_text observed=2026-08-06T22:44:43.884647Z digest=sha256:9b1c35e7656debb79437f453fc27e81978330f1ab67e62e0b862f11e7a948a54

Observation 03f88398-73a0-4d54-85f4-752cafe668ed · outbound

This paper cites Flashattention-2: Faster attention with better parallelism and work partitioning, 2023.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Flashattention-2: Faster attention with better parallelism and work partitioning, 2023

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Observation 51c23cfc-d3dd-4812-8719-9751fa392809 · outbound

This paper cites The commitmentbank: Investigating projection in naturally occurring discourse.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective The commitmentbank: Investigating projection in naturally occurring discourse

Reference 54

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source=pdf_text observed=2026-08-06T22:44:43.892250Z digest=sha256:085ea5281dcd710b5a2f24e9d4c247adfe0a055172ddce77a806c6e4e32093b2

Observation 8cd2fcdd-14b9-44cc-bb5c-ffb64dc62880 · outbound

This paper cites Universal transformers.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Universal transformers

Reference 55

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source=pdf_text observed=2026-08-06T22:44:43.896950Z digest=sha256:62901d3f7c4417c053d9a2a89d216fb495b710fadca40398f4dd7d05a4ce5de7

Observation 78874513-6b27-49cc-b158-b38ccc5f947a · outbound

This paper cites BERT: Pre-training of deep bidirectional transformers for language understanding.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective BERT: Pre-training of deep bidirectional transformers for language understanding

Reference 56

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source=pdf_text observed=2026-08-06T22:44:43.905116Z digest=sha256:28fd069cb187a04b682fa85714717677d3a1b435fd64eaba379556bd9cfb6d23

Observation 0f4a975a-d3e1-4246-83c7-65ffeddc1ea9 · outbound

This paper cites Attention over learned object embeddings enables complex visual reasoning.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Attention over learned object embeddings enables complex visual reasoning

Reference 57

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Observation 485d0deb-e583-40a7-b3aa-d221baf29a60 · outbound

This paper cites Dolan and Chris Brockett.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Dolan and Chris Brockett

Reference 59

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Observation c54d89f2-42da-4a6f-bc85-75bd718958c9 · outbound

This paper cites A robust self-learning framework for cross- lingual text classification.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective A robust self-learning framework for cross- lingual text classification

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Observation b1cf96da-04a9-4bfd-82c9-07bc2ef89990 · outbound

This paper cites The Llama 3 Herd of Models.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective The Llama 3 Herd of Models

Reference 61

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source=pdf_text observed=2026-08-06T22:44:43.925514Z digest=sha256:8ba5b3cfda9aaa4106d74afa698dec35ae939aa715523e64a3133111a4f783e5

Observation 454af4f4-62f4-4af8-bcea-b3ab0bf330a0 · outbound

This paper cites SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine

Reference 62

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source=pdf_text observed=2026-08-06T22:44:43.929753Z digest=sha256:0f8fd84aee3e41aa0d53ce8fe364d8c445c2aee04404b0e59e2ff4f387c35286

Observation ca3c2e8d-9098-4958-a20d-fb04887dc98f · outbound

This paper cites MRQA 2019 shared task: Evaluating generalization in reading com- prehension.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective MRQA 2019 shared task: Evaluating generalization in reading com- prehension

Reference 63

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source=pdf_text observed=2026-08-06T22:44:43.934143Z digest=sha256:ec83306653e7cadd99fa21a6853d75fedc64f07aa0423b5843e8d5497e761b0d

Observation 51d56377-32f5-4d7f-9ec8-b748e575d177 · outbound

This paper cites Making pre-trained language models better few-shot learners.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Making pre-trained language models better few-shot learners

Reference 64

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Observation fd02e4cf-c60f-4c36-9207-7682117dccb2 · outbound

This paper cites Zero-shot text classification with self-training.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Zero-shot text classification with self-training

Reference 65

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source=pdf_text observed=2026-08-06T22:44:43.943172Z digest=sha256:7da92647a80d750142d9e9e57682fe49323ec6add401c285a0e0057aabf6ce61

Observation f6a792ca-a239-401e-8b3e-020e0c206176 · outbound

This paper cites The third PASCAL recognizing textual entailment challenge.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective The third PASCAL recognizing textual entailment challenge

Reference 67

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source=pdf_text observed=2026-08-06T22:44:43.951585Z digest=sha256:c67de60a8be9bd90dd7e9f391c6b9973b62a6336b7c13f88420f1652f405b093

Observation dc6b9d1b-277a-4708-9c8f-73a7cb4f0847 · outbound

This paper cites PARS: Pseudo-Label Aware Robust Sample Selection for Learning with Noisy Labels.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective PARS: Pseudo-Label Aware Robust Sample Selection for Learning with Noisy Labels

Reference 68

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source=pdf_text observed=2026-08-06T22:44:43.955463Z digest=sha256:c937ce2fa9c532087e973553fdb6ef7be21c61c0bf03dffa14f9419524566f8e

Observation a6e7cd32-27b4-4356-92b5-47c15418a18c · outbound

This paper cites Making the V in VQA matter: Elevating the role of image under- standing in visual question answering.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Making the V in VQA matter: Elevating the role of image under- standing in visual question answering

Reference 69

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source=pdf_text observed=2026-08-06T22:44:43.960188Z digest=sha256:b7aa10a64d5a315dfd06f75e10c052af011de7c54ce1319a9e49e5082a503c8d

Observation 5f8850c1-6ad4-47e1-80d6-487256dba4e4 · outbound

This paper cites Semi-supervised learning by en- tropy minimization.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Semi-supervised learning by en- tropy minimization

Reference 70

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Observation ef3006f6-2adf-47dd-bd74-2b0d2a27595b · outbound

This paper cites Projected language models: A large model pre-segmented into smaller ones.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Projected language models: A large model pre-segmented into smaller ones

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Observation c06839e4-2587-4c38-9d9d-19c58db54669 · outbound

This paper cites PPT: Pre-trained prompt tuning for few-shot learning.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective PPT: Pre-trained prompt tuning for few-shot learning

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Observation bde65ae0-cc71-4d47-93ad-2d76f9fad05d · outbound

This paper cites Textbooks Are All You Need.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Textbooks Are All You Need

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Observation 8535b9b1-5e36-4d82-a403-de8ddcb09be5 · outbound

This paper cites Parameter-efficient transfer learning with diff pruning.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Parameter-efficient transfer learning with diff pruning

Reference 74

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source=pdf_text observed=2026-08-06T22:44:43.982011Z digest=sha256:bdda1469b17ccc98bdfc3dd7f7eef2171db09ca01bf655db40e4f18e6d41190b

Observation 24285ebd-c1ae-4116-b986-77a679a384a9 · outbound

This paper cites an unresolved cited work.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Unresolved cited work

Reference 75

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source=pdf_text observed=2026-08-06T22:44:43.986757Z digest=sha256:31a60952df7f5f1f2321179bcf23d32727b63ea11406f3b51fa0b654001308a3

Observation 15de1586-1f21-4d33-b0fe-9ee0b4185d74 · outbound

This paper cites an unresolved cited work.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Unresolved cited work

Reference 76

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source=pdf_text observed=2026-08-06T22:44:43.990606Z digest=sha256:bea8edf3b500526cb093f5ed039424aff9ef05da5e345814d2042c4e0d3bc390

Observation f05badb1-233e-40c7-a630-7b8038610c34 · outbound

This paper cites W ARP: Word-level Adversarial ReProgramming.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective W ARP: Word-level Adversarial ReProgramming

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source=pdf_text observed=2026-08-06T22:44:43.994748Z digest=sha256:b4feacdb3863fccbde4e06d718efe407f10d21b0c3ec1f286f57a3d536b9a7bf

Observation 8a42452f-7e20-4234-834c-93aef0b19e51 · outbound

This paper cites ToxiGen: A large-scale machine-generated dataset for adversarial and implicit hate speech detection.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective ToxiGen: A large-scale machine-generated dataset for adversarial and implicit hate speech detection

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source=pdf_text observed=2026-08-06T22:44:43.998737Z digest=sha256:a0cf6a34f5889f98fdd93de22d8ba84172bbe965a567b9aa0d81543d649798c2

Observation 508e78f9-fa73-48d0-9ba2-bb3920c27b89 · outbound

This paper cites Preserving Pre-trained Features Helps Calibrate Fine-tuned Language Models.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Preserving Pre-trained Features Helps Calibrate Fine-tuned Language Models

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source=pdf_text observed=2026-08-06T22:44:44.002754Z digest=sha256:25329600906207581a5089ed73928ab0c42305cbf61ca04aaea7b80c0f69a7a0

Observation 96c7696e-858c-4660-95cc-c48eda6dea5a · outbound

This paper cites Extending clip for category-to-image re- trieval in e-commerce.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Extending clip for category-to-image re- trieval in e-commerce

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Observation 1d7566e4-c94a-455d-bd00-0ed6b9d6eacb · outbound

This paper cites Aligning AI with shared human values.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Aligning AI with shared human values

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Observation 2c76dce9-fa76-4d1c-be3a-ab352787a12d · outbound

This paper cites Measuring massive multitask language BIBLIOGRAPHY 129 understanding.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Measuring massive multitask language BIBLIOGRAPHY 129 understanding

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source=pdf_text observed=2026-08-06T22:44:44.015165Z digest=sha256:5a76cf8a04d86a818bf5eeca22c8e830348c804f42595365a064e83f5e5ac644

Observation 1212c0c6-a34c-4ad7-af8c-050bec2fc8b1 · outbound

This paper cites Training Compute-Optimal Large Language Models.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Training Compute-Optimal Large Language Models

Reference 83

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Observation 89b04e8d-6a7f-4972-bd88-348b7ab06d90 · outbound

This paper cites ORPO: Monolithic Preference Optimization without Reference Model.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective ORPO: Monolithic Preference Optimization without Reference Model

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source=pdf_text observed=2026-08-06T22:44:44.024618Z digest=sha256:5c1986deba160f21524761e8933c2464b6afb38535f7dbce7fdd451620c0769c

Observation 387cd108-173b-488f-896a-efb847a5ec91 · outbound

This paper cites Unnatural in- structions: Tuning language models with (almost) no human labor.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Unnatural in- structions: Tuning language models with (almost) no human labor

Reference 85

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source=pdf_text observed=2026-08-06T22:44:44.028585Z digest=sha256:299da0cce2d644ab97ef7f4970030f3efd18103b6c34ea31ec121ee019c6de68

Observation 6591317c-b6fe-468d-bda6-ec2fe5272144 · outbound

This paper cites Human feedback is not gold standard.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Human feedback is not gold standard

Reference 86

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Observation d5c18acf-5c37-48fc-80c6-da3486230df3 · outbound

This paper cites Meta-learning the differ- ence: Preparing large language models for efficient adaptation.Transactions of the Association for Computational Linguistics, 10:1249–1265, 2022.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Meta-learning the differ- ence: Preparing large language models for efficient adaptation.Transactions of the Association for Computational Linguistics, 10:1249–1265, 2022

Reference 87

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Observation 972d2f27-166c-4da7-8008-730a3bac1ee1 · outbound

This paper cites Parameter-efficient transfer learning for NLP.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Parameter-efficient transfer learning for NLP

Reference 88

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Observation 87491cae-c3a0-4940-a3b9-7ac4efb15e71 · outbound

This paper cites Universal language model fine-tuning for text classification.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Universal language model fine-tuning for text classification

Reference 89

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source=pdf_text observed=2026-08-06T22:44:44.044640Z digest=sha256:1eeb3910823c801a1429c4ef8930ef0adf519f2a3dd74bca954f2982fd38cf30

Observation c159ef43-dec7-4a8d-a25c-16cd0d11e52c · outbound

This paper cites Can llms learn from a single exam- ple?, 2023.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Can llms learn from a single exam- ple?, 2023

Reference 90

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source=pdf_text observed=2026-08-06T22:44:44.049025Z digest=sha256:cb8f1b602f866416d19b0e3208b2df8cb9f3368134277b2fac66c92d0278e1ac

Observation 070e5a04-10ca-489b-9f45-84d6e79b27f5 · outbound

This paper cites Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen

Reference 91

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source=pdf_text observed=2026-08-06T22:44:44.053093Z digest=sha256:bf7ec8a28558fd05a7996ef2990acc635713691c44847aaac8c5833647bb96f6

Observation 0fe4b3bd-abe6-48bc-8d52-5a324ea9b841 · outbound

This paper cites Mining and summarizing customer reviews.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Mining and summarizing customer reviews

Reference 92

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source=pdf_text observed=2026-08-06T22:44:44.057136Z digest=sha256:1d2c3c7963ae6a8f02a3787f99a3692e84d8832f733a397a2ef6b315341595b3

Observation f5bda0a5-dc72-4678-a121-4455a329f013 · outbound

This paper cites Instruction fine-tuning: Does prompt loss mat- ter?, 2024.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Instruction fine-tuning: Does prompt loss mat- ter?, 2024

Reference 93

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source=pdf_text observed=2026-08-06T22:44:44.061742Z digest=sha256:83d07b812694e6ec3bfc9d80d4224d3652e653ba9f6d8739788fe5a14bf282a9

Observation 8bb9cdef-5d9f-4a89-bffc-0f18e730a2b8 · outbound

This paper cites Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations

Reference 94

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source=pdf_text observed=2026-08-06T22:44:44.066010Z digest=sha256:d903d348493ffee33c72fb39e2feadaebd19751ca4018f0b2754f8ad09c6b462

Observation 8fc32d23-017b-4d41-aa13-4f3505d20fe2 · outbound

This paper cites Hyperdecoders: Instance-specific de- coders for multi-task NLP.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Hyperdecoders: Instance-specific de- coders for multi-task NLP

Reference 95

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source=pdf_text observed=2026-08-06T22:44:44.070392Z digest=sha256:dea45716e333a9c4763140b0065bc6f5ce7259f8f3401aa79efc1b209f452b75

Observation 03ee083b-576c-4b2a-9424-5ee6b357137d · outbound

This paper cites Camels in a changing climate: Enhancing lm adaptation with tulu 2, 2023.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Camels in a changing climate: Enhancing lm adaptation with tulu 2, 2023

Reference 96

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source=pdf_text observed=2026-08-06T22:44:44.074811Z digest=sha256:8c21c97195a725c55bc0428a1884fa624b73e81bd6c132e543a4e2c660986237

Observation d50d8371-1ccb-4c20-b3e7-6f0c7d166ac6 · outbound

This paper cites Bartoldson, Bhavya Kailkhura, Avi Schwarzschild, Aniruddha Saha, Micah Goldblum, Jonas Geiping, and Tom Goldstein.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Bartoldson, Bhavya Kailkhura, Avi Schwarzschild, Aniruddha Saha, Micah Goldblum, Jonas Geiping, and Tom Goldstein

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source=pdf_text observed=2026-08-06T22:44:44.079269Z digest=sha256:575aea93ebd09218d557c1675935e0fa62ff0b82b04a1951b74141d1ce2e9c43

Observation fa6bd133-d5a4-4ccc-ac10-fbff2c2ca7c9 · outbound

This paper cites Representation learning for grounded spatial reasoning.Transactions of the Association for Computational Linguistics, 6:49–61, 2018.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Representation learning for grounded spatial reasoning.Transactions of the Association for Computational Linguistics, 6:49–61, 2018

Reference 98

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source=pdf_text observed=2026-08-06T22:44:44.083308Z digest=sha256:fe1c9cf6eeee449532cd0d8bbabcd4afc98a8023d1801ed2b05baf9738a4b3e5

Observation 452c341f-57e9-48ef-aed8-cb4f10f2dc73 · outbound

This paper cites LIMIT: Less Is More for Instruction Tuning Across Evaluation Paradigms.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective LIMIT: Less Is More for Instruction Tuning Across Evaluation Paradigms

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verified exact
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T22:44:44.087462Z digest=sha256:1e4a2e446d6a90156d9e7846f6d5ae6aa11162dc666ccbb89cd9890fe8752a79

Observation 013171b2-3b55-4642-b22c-d29254e49371 · outbound

This paper cites Scaling Laws for Neural Language Models.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Scaling Laws for Neural Language Models

Reference 100

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source=pdf_text observed=2026-08-06T22:44:44.091445Z digest=sha256:bf1156b707424390b3845be7cf48ce68232f189044673fddc1f4b471cf8d7732

Observation 58e56958-bd20-4f8c-aa66-aac08c5718fb · outbound

This paper cites Parameter-efficient multi-task fine-tuning for transformers via shared hypernetworks.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Parameter-efficient multi-task fine-tuning for transformers via shared hypernetworks

Reference 101

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source=pdf_text observed=2026-08-06T22:44:44.095688Z digest=sha256:92c9cb09eb39094654aef1cb657e989ed470d98c1ab08a6c2f635bb344ed22a0

Observation df9ceb9b-f737-4228-b364-74eb76ee197b · outbound

This paper cites Looking beyond the surface: A challenge set for reading compre- hension over multiple sentences.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Looking beyond the surface: A challenge set for reading compre- hension over multiple sentences

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source=pdf_text observed=2026-08-06T22:44:44.099652Z digest=sha256:7532a8b59ea22ef1a37ec39d732197b6ea182d90ccc1131af149e5e077e28dc3

Observation 418a7124-be8a-43ae-a98a-c33d2d53fdeb · outbound

This paper cites UNIFIEDQA: Crossing for- mat boundaries with a single QA system.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective UNIFIEDQA: Crossing for- mat boundaries with a single QA system

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source=pdf_text observed=2026-08-06T22:44:44.103686Z digest=sha256:3eb640e09dc20aea0c66cc327543e7b7e1e0e1b24762314d6629c73afe36b4ff

Observation e69bab42-9f29-4524-b3f9-ec54819353d3 · outbound

This paper cites Scitail: A textual entail- ment dataset from science question answering.Proceedings of the AAAI Conference on Artificial Intelligence, 32(1), Apr.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Scitail: A textual entail- ment dataset from science question answering.Proceedings of the AAAI Conference on Artificial Intelligence, 32(1), Apr

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source=pdf_text observed=2026-08-06T22:44:44.107619Z digest=sha256:3e4002a7c90c908db980c47b0521339cad099827dc84ab06104cbfc760df46bb

Pith citing papers

No inbound Pith citation observations are available.