Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference | Paper Citation Record

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Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference Unresolved cited work

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Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference Layer Normalization

Reference 2

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Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference 1, 364–374

Reference 3

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This paper cites Beggs and Dietmar Plenz,Neuronal avalanches in neocortical circuits, Journal of Neuroscience23 (2003), no.

Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference Beggs and Dietmar Plenz,Neuronal avalanches in neocortical circuits, Journal of Neuroscience23 (2003), no

Reference 4

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Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference Unresolved cited work

Reference 5

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This paper cites Muñoz, Alessandro Vespignani, and Stefano Zapperi,Paths to self-organized criticality, Brazilian Journal of Physics30(2000), no.

Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference Muñoz, Alessandro Vespignani, and Stefano Zapperi,Paths to self-organized criticality, Brazilian Journal of Physics30(2000), no

Reference 6

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This paper cites 2793–2803.

Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference 2793–2803

Reference 7

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This paper cites XCiT: Cross-Covariance Image Transformers.

Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference XCiT: Cross-Covariance Image Transformers

Reference 8

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This paper cites On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning.

Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning

Reference 9

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This paper cites CoulGAT: An Experiment on Interpretability of Graph Attention Networks.

Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference CoulGAT: An Experiment on Interpretability of Graph Attention Networks

Reference 10

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This paper cites Power Law Graph Transformer for Machine Translation and Representation Learning.

Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference Power Law Graph Transformer for Machine Translation and Representation Learning

Reference 11

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This paper cites PLDR-LLM: Large Language Model from Power Law Decoder Representations.

Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference PLDR-LLM: Large Language Model from Power Law Decoder Representations

Reference 12

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This paper cites PLDR-LLMs Learn A Generalizable Tensor Operator That Can Replace Its Own Deep Neural Net At Inference.

Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference PLDR-LLMs Learn A Generalizable Tensor Operator That Can Replace Its Own Deep Neural Net At Inference

Reference 13

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Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference Unresolved cited work

Reference 14

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Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference Unresolved cited work

Reference 15

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This paper cites HyperNetworks.

Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference HyperNetworks

Reference 16

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Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference Unresolved cited work

Reference 17

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This paper cites Horn and Charles R.

Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference Horn and Charles R

Reference 18

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This paper cites Going Beyond Linear Transformers with Recurrent Fast Weight Programmers.

Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference Going Beyond Linear Transformers with Recurrent Fast Weight Programmers

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This paper cites Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention.

Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Reference 20

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This paper cites Bilinear Attention Networks.

Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference Bilinear Attention Networks

Reference 21

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This paper cites Don't Read Everything: A Curvature-Conditioned Query for Linear Attention.

Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference Don't Read Everything: A Curvature-Conditioned Query for Linear Attention

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This paper cites TruthfulQA: Measuring How Models Mimic Human Falsehoods.

Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference TruthfulQA: Measuring How Models Mimic Human Falsehoods

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Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference 2, 41–74

Reference 24

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This paper cites Pointer Sentinel Mixture Models.

Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference Pointer Sentinel Mixture Models

Reference 25

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This paper cites Misner, Kip S.

Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference Misner, Kip S

Reference 26

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Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference Unresolved cited work

Reference 27

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Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference LieRE: Lie Rotational Positional Encodings

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Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference DBA: Efficient Transformer with Dynamic Bilinear Low-Rank Attention

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Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference Sinkformers: Transformers with Doubly Stochastic Attention

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Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference Linear Transformers Are Secretly Fast Weight Programmers

Reference 31

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This paper cites Attention as a Hypernetwork.

Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference Attention as a Hypernetwork

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Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference Unresolved cited work

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This paper cites GLU Variants Improve Transformer.

Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference GLU Variants Improve Transformer

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Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference Talking-Heads Attention

Reference 35

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Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference Unresolved cited work

Reference 36

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This paper cites Eugene Stanley,Scaling, universality, and renormalization: Three pillars of modern critical phenomena, Reviews of Modern Physics71(1999), no.

Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference Eugene Stanley,Scaling, universality, and renormalization: Three pillars of modern critical phenomena, Reviews of Modern Physics71(1999), no

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This paper cites RoFormer: Enhanced Transformer with Rotary Position Embedding.

Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference RoFormer: Enhanced Transformer with Rotary Position Embedding

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This paper cites Synthesizer: Rethinking Self-Attention in Transformer Models.

Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference Synthesizer: Rethinking Self-Attention in Transformer Models

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This paper cites Functional Equivalence in Attention: A Comprehensive Study with Applications to Linear Mode Connectivity.

Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference Functional Equivalence in Attention: A Comprehensive Study with Applications to Linear Mode Connectivity

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This paper cites Tropp,An introduction to matrix concentration inequalities, Foundations and Trends in Machine Learning8(2015), no.

Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference Tropp,An introduction to matrix concentration inequalities, Foundations and Trends in Machine Learning8(2015), no

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This paper cites Faster Query-Key Learning Sharpens Attention in Self-Attention Models.

Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference Faster Query-Key Learning Sharpens Attention in Self-Attention Models

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Observation f15e901b-6740-4366-be38-6c7fd5f15fe5 · outbound

This paper cites Gomez, Łukasz Kaiser, and Illia Polosukhin,Attention is all you need, Advances in Neural Information Processing Systems 30 (NIPS), 2017, pp.

Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference Gomez, Łukasz Kaiser, and Illia Polosukhin,Attention is all you need, Advances in Neural Information Processing Systems 30 (NIPS), 2017, pp

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Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference Unresolved cited work

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Observation f57aed38-1c73-4a1a-a258-b328824a9e9c · outbound

This paper cites Wilson and John Kogut,The renormalization group and theϵ expansion, Physics Reports12 (1974), no.

Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference Wilson and John Kogut,The renormalization group and theϵ expansion, Physics Reports12 (1974), no

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Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference Unresolved cited work

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This paper cites Autonomy-of-Heads: Data-Free Sparse Attention from Frozen Query-Key Geometry.

Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference Autonomy-of-Heads: Data-Free Sparse Attention from Frozen Query-Key Geometry

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Resolution: verified exact; local_arxiv, observed 2026-08-14T04:15:49.105871Z

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Observation 27e241a0-8af7-4d23-b8bd-08bdff1db90c · outbound

This paper cites ComRoPE: Scalable and Robust Rotary Position Embedding Parameterized by Trainable Commuting Angle Matrices.

Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference ComRoPE: Scalable and Robust Rotary Position Embedding Parameterized by Trainable Commuting Angle Matrices

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This paper cites I, Proceedings of the Physico-Mathematical Society of Japan17(1935), 48–57.

Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference I, Proceedings of the Physico-Mathematical Society of Japan17(1935), 48–57

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Observation 7ae40847-c8e2-46bc-a6ee-10f093b09b97 · outbound

This paper cites Xing,DAGs with NO TEARS: Continuous optimization for structure learning, Advances in Neural Information Processing Systems 31 (NeurIPS), 2018, pp.

Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference Xing,DAGs with NO TEARS: Continuous optimization for structure learning, Advances in Neural Information Processing Systems 31 (NeurIPS), 2018, pp

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Observation 0715a2e3-414d-4c7f-97d4-9c33dc689a42 · outbound

This paper cites Zipf,Human behavior and the principle of least effort, Addison-Wesley, 1949.

Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference Zipf,Human behavior and the principle of least effort, Addison-Wesley, 1949

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