Paper Citation Record · LEDGER

Universal Approximation of Visual Autoregressive Transformers

As of 9 August 2026, this Paper Citation Record lists 63 of 63 outbound references and 3 inbound Pith citation observations for arXiv:2502.06167.

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

pith.paper-citation-record.v1
2502.06167 v1

Coverage vector

measured 63 of 63 reference resolution

Typed states for the displayed outbound observations.

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measured 66 of 66 standing notices

One-hop event checks from named stored sources.

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measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T10:20:15.278868Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-06T23:04:16.374748Z

Reference resolution

63 of 63 outbound references displayed

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External citation measurements

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Outbound references

Observation 44cc05e8-0514-405d-8d27-d2bd8c75f280 · outbound

This paper cites RichSpace: Enriching Text-to-Video Prompt Space via Text Embedding Interpolation.

Universal Approximation of Visual Autoregressive Transformers RichSpace: Enriching Text-to-Video Prompt Space via Text Embedding Interpolation

Reference 3

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Observation fc652b63-2a36-45fb-b3ca-3b7615ad3b15 · outbound

This paper cites Fast gradient computation for rope attention in almost linear time.

Universal Approximation of Visual Autoregressive Transformers Fast gradient computation for rope attention in almost linear time

Reference 5

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Observation 476ef222-2196-4878-8eec-db59a372e755 · outbound

This paper cites Bypassing the Exponential Dependency: Looped Transformers Efficiently Learn In-context by Multi-step Gradient Descent.

Universal Approximation of Visual Autoregressive Transformers Bypassing the Exponential Dependency: Looped Transformers Efficiently Learn In-context by Multi-step Gradient Descent

Reference 7

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Observation 0b2ff850-b1c5-4df3-9ceb-d36b54960654 · outbound

This paper cites The Computational Limits of State-Space Models and Mamba via the Lens of Circuit Complexity.

Universal Approximation of Visual Autoregressive Transformers The Computational Limits of State-Space Models and Mamba via the Lens of Circuit Complexity

Reference 8

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Observation 92b3210b-cac9-4e6d-9844-9e637ef80506 · outbound

This paper cites Dissecting Submission Limit in Desk-Rejections: A Mathematical Analysis of Fairness in AI Conference Policies.

Universal Approximation of Visual Autoregressive Transformers Dissecting Submission Limit in Desk-Rejections: A Mathematical Analysis of Fairness in AI Conference Policies

Reference 9

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Observation 1cd1b651-399a-431f-86d5-f3f0e1ff5463 · outbound

This paper cites HSR-Enhanced Sparse Attention Acceleration.

Universal Approximation of Visual Autoregressive Transformers HSR-Enhanced Sparse Attention Acceleration

Reference 10

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Observation a70f39a2-f4db-4a78-a1b3-fadf8c156e54 · outbound

This paper cites Zero-th order al- gorithm for softmax attention optimization.

Universal Approximation of Visual Autoregressive Transformers Zero-th order al- gorithm for softmax attention optimization

Reference 12

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Observation 33fa3999-0ae4-4d1f-908c-86b291b3d7fe · outbound

This paper cites Randomized and Deterministic Attention Sparsification Algorithms for Over-parameterized Feature Dimension.

Universal Approximation of Visual Autoregressive Transformers Randomized and Deterministic Attention Sparsification Algorithms for Over-parameterized Feature Dimension

Reference 13

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Observation f829d95a-b79e-4995-9e19-6ef4d4489e0b · outbound

This paper cites A Nearly Optimal Size Coreset Algorithm with Nearly Linear Time.

Universal Approximation of Visual Autoregressive Transformers A Nearly Optimal Size Coreset Algorithm with Nearly Linear Time

Reference 14

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Observation 45a08f1f-0a95-4241-afe6-76e2d069d4cb · outbound

This paper cites Faster Robust Tensor Power Method for Arbitrary Order.

Universal Approximation of Visual Autoregressive Transformers Faster Robust Tensor Power Method for Arbitrary Order

Reference 15

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Observation b608b22e-dbe5-4319-80d7-cc00511d4b8e · outbound

This paper cites A Fast Optimization View: Reformulating Single Layer Attention in LLM Based on Tensor and SVM Trick, and Solving It in Matrix Multiplication Time.

Universal Approximation of Visual Autoregressive Transformers A Fast Optimization View: Reformulating Single Layer Attention in LLM Based on Tensor and SVM Trick, and Solving It in Matrix Multiplication Time

Reference 17

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Observation 72cedb37-6c8c-48d4-b318-4d843522144a · outbound

This paper cites In-Context Learning for Attention Scheme: from Single Softmax Regression to Multiple Softmax Regression via a Tensor Trick.

Universal Approximation of Visual Autoregressive Transformers In-Context Learning for Attention Scheme: from Single Softmax Regression to Multiple Softmax Regression via a Tensor Trick

Reference 18

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Observation af2b8ea2-78cb-40df-8b10-1c0024be4b05 · outbound

This paper cites GradientCoin: A Peer-to-Peer Decentralized Large Language Models.

Universal Approximation of Visual Autoregressive Transformers GradientCoin: A Peer-to-Peer Decentralized Large Language Models

Reference 19

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Observation 0b3a8da4-4b8c-4c64-8347-a1d7e46f6b47 · outbound

This paper cites An Iterative Algorithm for Rescaled Hyperbolic Functions Regression.

Universal Approximation of Visual Autoregressive Transformers An Iterative Algorithm for Rescaled Hyperbolic Functions Regression

Reference 20

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Observation 20ee9adc-18df-482c-818c-d553dd484eb8 · outbound

This paper cites Computational Limits of Low-Rank Adaptation (LoRA) Fine-Tuning for Transformer Models.

Universal Approximation of Visual Autoregressive Transformers Computational Limits of Low-Rank Adaptation (LoRA) Fine-Tuning for Transformer Models

Reference 21

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Observation 78f10811-1925-4147-a2b8-418f88578697 · outbound

This paper cites InstaHide's Sample Complexity When Mixing Two Private Images.

Universal Approximation of Visual Autoregressive Transformers InstaHide's Sample Complexity When Mixing Two Private Images

Reference 22

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Observation b938ae5e-9130-41a7-82ca-138bc0e9be88 · outbound

This paper cites A Dynamic Low-Rank Fast Gaussian Transform.

Universal Approximation of Visual Autoregressive Transformers A Dynamic Low-Rank Fast Gaussian Transform

Reference 24

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Observation f4b2a72f-3a03-4ee5-b9ed-06a00ebbe1dc · outbound

This paper cites Fundamental Limits of Prompt Tuning Transformers: Universality, Capacity and Efficiency.

Universal Approximation of Visual Autoregressive Transformers Fundamental Limits of Prompt Tuning Transformers: Universality, Capacity and Efficiency

Reference 25

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Observation 0c04f6a3-8bb7-4efe-aefc-5b39dcb53049 · outbound

This paper cites On Statistical Rates of Conditional Diffusion Transformers: Approximation, Estimation and Minimax Optimality.

Universal Approximation of Visual Autoregressive Transformers On Statistical Rates of Conditional Diffusion Transformers: Approximation, Estimation and Minimax Optimality

Reference 26

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Observation 6351d552-0607-431c-a452-a21b490704e0 · outbound

This paper cites On Statistical Rates and Provably Efficient Criteria of Latent Diffusion Transformers (DiTs).

Universal Approximation of Visual Autoregressive Transformers On Statistical Rates and Provably Efficient Criteria of Latent Diffusion Transformers (DiTs)

Reference 27

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Observation b36bf190-766c-49b6-ab38-15d0126110a4 · outbound

This paper cites Approximation Rate of the Transformer Architecture for Sequence Modeling.

Universal Approximation of Visual Autoregressive Transformers Approximation Rate of the Transformer Architecture for Sequence Modeling

Reference 28

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Observation 9bbd61db-9cfe-4124-aedd-fae90abb8a3f · outbound

This paper cites On Computational Limits and Provably Efficient Criteria of Visual Autoregressive Models: A Fine-Grained Complexity Analysis.

Universal Approximation of Visual Autoregressive Transformers On Computational Limits and Provably Efficient Criteria of Visual Autoregressive Models: A Fine-Grained Complexity Analysis

Reference 29

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Observation 38e644f0-c408-416f-b060-c5eb52bde7e6 · outbound

This paper cites Circuit Complexity Bounds for Visual Autoregressive Model.

Universal Approximation of Visual Autoregressive Transformers Circuit Complexity Bounds for Visual Autoregressive Model

Reference 30

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Observation 7fc97e72-02bc-4130-8811-99e165941d5a · outbound

This paper cites Faster sampling algorithms for polytopes with small treewidth.

Universal Approximation of Visual Autoregressive Transformers Faster sampling algorithms for polytopes with small treewidth

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:39:15.153607Z

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Observation 2d83a417-b17f-4d5a-aa1f-bd41ac960945 · outbound

This paper cites Neural algorithmic reasoning for hypergraphs with looped transformers.

Universal Approximation of Visual Autoregressive Transformers Neural algorithmic reasoning for hypergraphs with looped transformers

Reference 32

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Observation 94be64d9-d901-45e9-86a1-a3485c324626 · outbound

This paper cites Theoretical Constraints on the Expressive Power of $\mathsf{RoPE}$-based Tensor Attention Transformers.

Universal Approximation of Visual Autoregressive Transformers Theoretical Constraints on the Expressive Power of $\mathsf{RoPE}$-based Tensor Attention Transformers

Reference 33

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Observation 395c5ba0-28fd-4287-8bdf-1e4fdb2c4124 · outbound

This paper cites Conv-Basis: A New Paradigm for Efficient Attention Inference and Gradient Computation in Transformers.

Universal Approximation of Visual Autoregressive Transformers Conv-Basis: A New Paradigm for Efficient Attention Inference and Gradient Computation in Transformers

Reference 34

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Observation b202b886-7f38-4158-a517-0a9045b4ddff · outbound

This paper cites Beyond Linear Approximations: A Novel Pruning Approach for Attention Matrix.

Universal Approximation of Visual Autoregressive Transformers Beyond Linear Approximations: A Novel Pruning Approach for Attention Matrix

Reference 35

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Observation 1ebc143a-4690-4b2d-8d98-764c8624b273 · outbound

This paper cites On the Computational Capability of Graph Neural Networks: A Circuit Complexity Bound Perspective.

Universal Approximation of Visual Autoregressive Transformers On the Computational Capability of Graph Neural Networks: A Circuit Complexity Bound Perspective

Reference 36

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Observation f816a0f7-fdc7-481b-8c16-48e05711d445 · outbound

This paper cites A Tighter Complexity Analysis of SparseGPT.

Universal Approximation of Visual Autoregressive Transformers A Tighter Complexity Analysis of SparseGPT

Reference 37

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This paper cites Fast second-order method for neural networks under small treewidth setting.

Universal Approximation of Visual Autoregressive Transformers Fast second-order method for neural networks under small treewidth setting

Reference 38

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This paper cites Uniform Last-Iterate Guarantee for Bandits and Reinforcement Learning.

Universal Approximation of Visual Autoregressive Transformers Uniform Last-Iterate Guarantee for Bandits and Reinforcement Learning

Reference 39

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Universal Approximation of Visual Autoregressive Transformers A Faster $k$-means++ Algorithm

Reference 40

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Universal Approximation of Visual Autoregressive Transformers Looped ReLU MLPs May Be All You Need as Practical Programmable Computers

Reference 41

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Universal Approximation of Visual Autoregressive Transformers Multi-Layer Transformers Gradient Can be Approximated in Almost Linear Time

Reference 42

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Universal Approximation of Visual Autoregressive Transformers Differential Privacy Mechanisms in Neural Tangent Kernel Regression

Reference 43

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Universal Approximation of Visual Autoregressive Transformers Differential privacy of cross-attention with provable guarantee

Reference 44

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Universal Approximation of Visual Autoregressive Transformers Tensor attention train- ing: Provably efficient learning of higher-order transformers

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This paper cites How to Inverting the Leverage Score Distribution?.

Universal Approximation of Visual Autoregressive Transformers How to Inverting the Leverage Score Distribution?

Reference 46

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This paper cites Local Convergence of Approximate Newton Method for Two Layer Nonlinear Regression.

Universal Approximation of Visual Autoregressive Transformers Local Convergence of Approximate Newton Method for Two Layer Nonlinear Regression

Reference 47

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Universal Approximation of Visual Autoregressive Transformers Dynamic Maintenance of Kernel Density Estimation Data Structure: From Practice to Theory

Reference 48

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Universal Approximation of Visual Autoregressive Transformers Inverting the Leverage Score Gradient: An Efficient Approximate Newton Method

Reference 49

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Universal Approximation of Visual Autoregressive Transformers Solving Regularized Exp, Cosh and Sinh Regression Problems

Reference 50

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Universal Approximation of Visual Autoregressive Transformers The Llama 3 Herd of Models

Reference 51

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Universal Approximation of Visual Autoregressive Transformers Score-based Generative Diffusion Models for Social Recommendations

Reference 52

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Universal Approximation of Visual Autoregressive Transformers DreamFusion: Text-to-3D using 2D Diffusion

Reference 53

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Universal Approximation of Visual Autoregressive Transformers Denoising Diffusion Implicit Models

Reference 55

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This paper cites A Mathematical Abstraction for Balancing the Trade-off Between Creativity and Reality in Large Language Models.

Universal Approximation of Visual Autoregressive Transformers A Mathematical Abstraction for Balancing the Trade-off Between Creativity and Reality in Large Language Models

Reference 56

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Universal Approximation of Visual Autoregressive Transformers A Unified Scheme of ResNet and Softmax

Reference 57

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Universal Approximation of Visual Autoregressive Transformers Fast and Efficient Matching Algorithm with Deadline Instances

Reference 58

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Observation fdeb068f-94ae-4a6a-a7b8-6d005e50a9e6 · outbound

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Universal Approximation of Visual Autoregressive Transformers The Expressibility of Polynomial based Attention Scheme

Reference 59

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Observation c624e87b-2e6c-479e-bb8c-0d3e4fcc3111 · outbound

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Universal Approximation of Visual Autoregressive Transformers An Automatic Learning Rate Schedule Algorithm for Achieving Faster Convergence and Steeper Descent

Reference 60

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Observation 0a46d974-4bd5-431c-8e67-8287bc123a12 · outbound

This paper cites Revisiting Quantum Algorithms for Linear Regressions: Quadratic Speedups without Data-Dependent Parameters.

Universal Approximation of Visual Autoregressive Transformers Revisiting Quantum Algorithms for Linear Regressions: Quadratic Speedups without Data-Dependent Parameters

Reference 61

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This paper cites Adaptive Liquidity Provision in Uniswap V3 with Deep Reinforcement Learning.

Universal Approximation of Visual Autoregressive Transformers Adaptive Liquidity Provision in Uniswap V3 with Deep Reinforcement Learning

Reference 63

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Observation e961d5ce-0da8-4194-af36-ddf9fcd0bf03 · outbound

This paper cites Dolfin: Diffusion Layout Transformers without Autoencoder.

Universal Approximation of Visual Autoregressive Transformers Dolfin: Diffusion Layout Transformers without Autoencoder

Reference 2017

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Observation 21dfec4a-04df-418f-9091-1df9e4a3912b · outbound

This paper cites Query Complexity of Active Learning for Function Family With Nearly Orthogonal Basis.

Universal Approximation of Visual Autoregressive Transformers Query Complexity of Active Learning for Function Family With Nearly Orthogonal Basis

Reference 2018

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Observation 721eed07-cbeb-420a-9235-2755d57df207 · outbound

This paper cites FlowAR: Scale-wise Autoregressive Image Generation Meets Flow Matching.

Universal Approximation of Visual Autoregressive Transformers FlowAR: Scale-wise Autoregressive Image Generation Meets Flow Matching

Reference 2019

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Observation 58c054dc-fbba-4548-af90-6d87b228b9ca · outbound

This paper cites Sublinear Time Algorithm for Online Weighted Bipartite Matching.

Universal Approximation of Visual Autoregressive Transformers Sublinear Time Algorithm for Online Weighted Bipartite Matching

Reference 2020

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Observation 90606bf6-ba39-428e-999f-cdf49bd7a395 · outbound

This paper cites An Over-parameterized Exponential Regression.

Universal Approximation of Visual Autoregressive Transformers An Over-parameterized Exponential Regression

Reference 2021

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This paper cites Sumformer: Univer- sal approximation for efficient transformers.

Universal Approximation of Visual Autoregressive Transformers Sumformer: Univer- sal approximation for efficient transformers

Reference 2022

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Observation 884c3c3b-d416-430b-902c-e11c3c998f2a · outbound

This paper cites Federated Empirical Risk Minimization via Second-Order Method.

Universal Approximation of Visual Autoregressive Transformers Federated Empirical Risk Minimization via Second-Order Method

Reference 2023

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Observation 423c9e52-f987-4305-b56b-0571d9908fdc · outbound

This paper cites Circuit Complexity Bounds for RoPE-based Transformer Architecture.

Universal Approximation of Visual Autoregressive Transformers Circuit Complexity Bounds for RoPE-based Transformer Architecture

Reference 2024

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Observation 87802fc2-e708-444b-bf09-461ce0a1ea86 · outbound

This paper cites High-Order Matching for One-Step Shortcut Diffusion Models.

Universal Approximation of Visual Autoregressive Transformers High-Order Matching for One-Step Shortcut Diffusion Models

Reference 2025

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Pith citing papers

Observation eb76f657-67b6-45ff-9970-12ae899ad648 · inbound

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling cites this paper.

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling Universal Approximation of Visual Autoregressive Transformers

Reference 5

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Observation 23407aeb-deec-4e61-8efd-6ab0c3408842 · inbound

Reasoning Like an Economist: Post-Training on Economic Problems Induces Strategic Generalization in LLMs cites this paper.

Reasoning Like an Economist: Post-Training on Economic Problems Induces Strategic Generalization in LLMs Universal Approximation of Visual Autoregressive Transformers

Reference 9

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Observation eee48c05-6f22-43cb-be7f-df588d52bafb · inbound

CoVE: Compressed Vocabulary Expansion Makes Better LLM-based Recommender Systems cites this paper.

CoVE: Compressed Vocabulary Expansion Makes Better LLM-based Recommender Systems Universal Approximation of Visual Autoregressive Transformers

Reference 15

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