Who Cited It

Affordance-Compiled Intelligence: Observable-Only Cognitive Impedance Matching for No-Meta LLM-Integrated Systems

2026 · arXiv (Cornell University) · 3,059 citations · 2 from inside this corpus

Patrick Lewis, Ethan Perez low, Aleksandara Piktus low, Fabio Petroni, Vladimir Karpukhin low, Naman Goyal, Heinrich Küttler low, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, Douwe Kiela

The source holds an abstract for this work, but its best open-access copy is under no open licence, which does not permit us to republish the text. Read it at the source below.

Affordance-Compiled Intelligence: Observable-Only Cognitive Impedance Matching for No-Met… (2026)Affordance-Compiled Intellige…[No title in the source record — DROPS (Schloss Dagstuhl – Leibniz Center for Informatics… (2015)[No title in the source recor…BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019)BERT: Pre-training of Deep Bi…Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer (2019)Exploring the Limits of Trans…HuggingFace's Transformers: State-of-the-art Natural Language Processing (2019)HuggingFace's Transformers: S…fairseq: A Fast, Extensible Toolkit for Sequence Modeling (2019)fairseq: A Fast, Extensible T…Natural Questions: A Benchmark for Question Answering Research (2019)Natural Questions: A Benchmar…A Diversity-Promoting Objective Function for Neural Conversation Models (2016)A Diversity-Promoting Objecti…GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding (2018)GLUE: A Multi-Task Benchmark …Language Models as Knowledge Bases? (2019)Language Models as Knowledge …Semantic Parsing on Freebase from Question-Answer Pairs (2013)Semantic Parsing on Freebase …Reading Wikipedia to Answer Open-Domain Questions (2017)Reading Wikipedia to Answer O…Language Models are Few-Shot Learners (2020)Language Models are Few-Shot …Text Data Augmentation for Deep Learning (2021)Text Data Augmentation for De…
13 of 13 neighbouring works in this corpus. Blue is what this paper cites; orange is what cites it, and a dashed line is one neighbour citing another. Only the largest labels are drawn — every node carries its full title on hover.
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What this paper cites, inside the corpus

PaperYearCited
[No title in the source record — DROPS (Schloss Dagstuhl – Leibniz Center for Informatics…201550,396
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding201933,416
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer20193,698
HuggingFace's Transformers: State-of-the-art Natural Language Processing20193,147
fairseq: A Fast, Extensible Toolkit for Sequence Modeling20192,511
Natural Questions: A Benchmark for Question Answering Research20192,095
A Diversity-Promoting Objective Function for Neural Conversation Models20162,061
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding20181,861
Language Models as Knowledge Bases?20191,798
Semantic Parsing on Freebase from Question-Answer Pairs20131,618
Reading Wikipedia to Answer Open-Domain Questions20171,454

What cites it, inside the corpus

PaperYearCited
Language Models are Few-Shot Learners20203,020
Text Data Augmentation for Deep Learning20211,701

Links

DOI · Open access copy · OpenAlex record

Topics

Topic ModelingComputer Science
Natural Language Processing TechniquesComputer Science
Multimodal Machine Learning ApplicationsComputer Science

Is this record sound?

complete

Nothing in this record contradicts itself and no field we check is missing.

  • supports12 author record(s) attached.
  • supports59 reference(s) recorded.
  • neutralThe DOI carries no year to check against.
  • supportsA title is present.

Provenance

Everything above was read from one stored OpenAlex payload, fetched 2026-09-04T03:58:47+00:00.

sha256 a08467ae9504f237…