From Shortcut to Induction Head: How Data Diversity Shapes Algorithm Selection in Transformers
Fuente:
arXiv
Saved in:
| Main Authors: | Kawata, Ryotaro, Song, Yujin, Bietti, Alberto, Nishikawa, Naoki, Suzuki, Taiji, Vaiter, Samuel, Wu, Denny |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Transformers as Measure-Theoretic Associative Memory: A Statistical Perspective and Minimax Optimality
by: Kawata, Ryotaro, et al.
Published: (2026)
by: Kawata, Ryotaro, et al.
Published: (2026)
State Space Models are Provably Comparable to Transformers in Dynamic Token Selection
by: Nishikawa, Naoki, et al.
Published: (2024)
by: Nishikawa, Naoki, et al.
Published: (2024)
Mixture of Experts Provably Detect and Learn the Latent Cluster Structure in Gradient-Based Learning
by: Kawata, Ryotaro, et al.
Published: (2025)
by: Kawata, Ryotaro, et al.
Published: (2025)
Learning sum of diverse features: computational hardness and efficient gradient-based training for ridge combinations
by: Oko, Kazusato, et al.
Published: (2024)
by: Oko, Kazusato, et al.
Published: (2024)
Pretrained transformer efficiently learns low-dimensional target functions in-context
by: Oko, Kazusato, et al.
Published: (2024)
by: Oko, Kazusato, et al.
Published: (2024)
Direct Distributional Optimization for Provable Alignment of Diffusion Models
by: Kawata, Ryotaro, et al.
Published: (2025)
by: Kawata, Ryotaro, et al.
Published: (2025)
Degrees of Freedom for Linear Attention: Distilling Softmax Attention with Optimal Feature Efficiency
by: Nishikawa, Naoki, et al.
Published: (2025)
by: Nishikawa, Naoki, et al.
Published: (2025)
How Neural Reward Models Learn Features for Policy Optimization: A Single-Index Analysis
by: Higuchi, Rei, et al.
Published: (2026)
by: Higuchi, Rei, et al.
Published: (2026)
How Does Label Noise Gradient Descent Improve Generalization in the Low SNR Regime?
by: Huang, Wei, et al.
Published: (2025)
by: Huang, Wei, et al.
Published: (2025)
When Does Metadata Conditioning (NOT) Work for Language Model Pre-Training? A Study with Context-Free Grammars
by: Higuchi, Rei, et al.
Published: (2025)
by: Higuchi, Rei, et al.
Published: (2025)
Understanding the Mechanisms of Fast Hyperparameter Transfer
by: Ghosh, Nikhil, et al.
Published: (2025)
by: Ghosh, Nikhil, et al.
Published: (2025)
Learning to Recall with Transformers Beyond Orthogonal Embeddings
by: Vural, Nuri Mert, et al.
Published: (2026)
by: Vural, Nuri Mert, et al.
Published: (2026)
Selective Induction Heads: How Transformers Select Causal Structures In Context
by: D'Angelo, Francesco, et al.
Published: (2025)
by: D'Angelo, Francesco, et al.
Published: (2025)
ZOBA: An Efficient Single-loop Zeroth-order Bilevel Optimization Algorithm
by: Rando, Marco, et al.
Published: (2026)
by: Rando, Marco, et al.
Published: (2026)
Metastable Dynamics of Chain-of-Thought Reasoning: Provable Benefits of Search, RL and Distillation
by: Kim, Juno, et al.
Published: (2025)
by: Kim, Juno, et al.
Published: (2025)
Learning Compositional Functions with Transformers from Easy-to-Hard Data
by: Wang, Zixuan, et al.
Published: (2025)
by: Wang, Zixuan, et al.
Published: (2025)
Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit
by: Lee, Jason D., et al.
Published: (2024)
by: Lee, Jason D., et al.
Published: (2024)
Risk Estimate under a Time-Varying Autoregressive Model for Data-Driven Reproduction Number Estimation
by: Pascal, Barbara, et al.
Published: (2024)
by: Pascal, Barbara, et al.
Published: (2024)
Transformers Provably Solve Parity Efficiently with Chain of Thought
by: Kim, Juno, et al.
Published: (2024)
by: Kim, Juno, et al.
Published: (2024)
Approximation and Estimation Ability of Transformers for Sequence-to-Sequence Functions with Infinite Dimensional Input
by: Takakura, Shokichi, et al.
Published: (2023)
by: Takakura, Shokichi, et al.
Published: (2023)
Transformers Learn Nonlinear Features In Context: Nonconvex Mean-field Dynamics on the Attention Landscape
by: Kim, Juno, et al.
Published: (2024)
by: Kim, Juno, et al.
Published: (2024)
Deep Two-Way Matrix Reordering for Relational Data Analysis
by: Watanabe, Chihiro, et al.
Published: (2021)
by: Watanabe, Chihiro, et al.
Published: (2021)
How history shapes memories in autobiographical narratives
by: Lucas Bietti
Published: (2012)
by: Lucas Bietti
Published: (2012)
On the Hardness of Junking LLMs
by: Rando, Marco, et al.
Published: (2026)
by: Rando, Marco, et al.
Published: (2026)
Characterizations of inexact proximal operators
by: Lauga, Guillaume, et al.
Published: (2026)
by: Lauga, Guillaume, et al.
Published: (2026)
Sharp Capacity Scaling of Spectral Optimizers in Learning Associative Memory
by: Kim, Juno, et al.
Published: (2026)
by: Kim, Juno, et al.
Published: (2026)
How do Transformers perform In-Context Autoregressive Learning?
by: Sander, Michael E., et al.
Published: (2024)
by: Sander, Michael E., et al.
Published: (2024)
Transformers are Minimax Optimal Nonparametric In-Context Learners
by: Kim, Juno, et al.
Published: (2024)
by: Kim, Juno, et al.
Published: (2024)
A Lower Bound and a Near-Optimal Algorithm for Bilevel Empirical Risk Minimization
by: Dagréou, Mathieu, et al.
Published: (2023)
by: Dagréou, Mathieu, et al.
Published: (2023)
From Saddle Points Toward Global Minima: A Newton-Type Method on Wasserstein Space
by: Lascu, Razvan-Andrei, et al.
Published: (2026)
by: Lascu, Razvan-Andrei, et al.
Published: (2026)
The Politics of Social Care in Japan: How Central–Local Interactions Shaped Child Allowances and Elderly Medical Care
by: Ryotaro Takahashi
Published: (2025)
by: Ryotaro Takahashi
Published: (2025)
Understanding Contextual Recall in Transformers: How Finetuning Enables In-Context Reasoning over Pretraining Knowledge
by: Vasudeva, Bhavya, et al.
Published: (2026)
by: Vasudeva, Bhavya, et al.
Published: (2026)
Understanding Factual Recall in Transformers via Associative Memories
by: Nichani, Eshaan, et al.
Published: (2024)
by: Nichani, Eshaan, et al.
Published: (2024)
An energy landscape-based theoretical framework for understanding the emergence of functions in a living system under the dynamical component interaction
by: Suzuki, Ryunosuke, et al.
Published: (2025)
by: Suzuki, Ryunosuke, et al.
Published: (2025)
Direct Density Ratio Optimization: A Statistically Consistent Approach to Aligning Large Language Models
by: Higuchi, Rei, et al.
Published: (2025)
by: Higuchi, Rei, et al.
Published: (2025)
The Mechanism of Weak-to-Strong Generalization: Feature Elicitation from Latent Knowledge
by: Awano, Ryoya, et al.
Published: (2026)
by: Awano, Ryoya, et al.
Published: (2026)
Mean-field Analysis on Two-layer Neural Networks from a Kernel Perspective
by: Takakura, Shokichi, et al.
Published: (2024)
by: Takakura, Shokichi, et al.
Published: (2024)
AutoLL: Automatic Linear Layout of Graphs based on Deep Neural Network
by: Watanabe, Chihiro, et al.
Published: (2021)
by: Watanabe, Chihiro, et al.
Published: (2021)
Test time training enhances in-context learning of nonlinear functions
by: Kuwataka, Kento, et al.
Published: (2025)
by: Kuwataka, Kento, et al.
Published: (2025)
In-Context Learning Is Provably Bayesian Inference: A Generalization Theory for Meta-Learning
by: Wakayama, Tomoya, et al.
Published: (2025)
by: Wakayama, Tomoya, et al.
Published: (2025)
Similar Items
-
Transformers as Measure-Theoretic Associative Memory: A Statistical Perspective and Minimax Optimality
by: Kawata, Ryotaro, et al.
Published: (2026) -
State Space Models are Provably Comparable to Transformers in Dynamic Token Selection
by: Nishikawa, Naoki, et al.
Published: (2024) -
Mixture of Experts Provably Detect and Learn the Latent Cluster Structure in Gradient-Based Learning
by: Kawata, Ryotaro, et al.
Published: (2025) -
Learning sum of diverse features: computational hardness and efficient gradient-based training for ridge combinations
by: Oko, Kazusato, et al.
Published: (2024) -
Pretrained transformer efficiently learns low-dimensional target functions in-context
by: Oko, Kazusato, et al.
Published: (2024)