Saved in:
| Main Authors: | Nishi, Kento, Ramesh, Rahul, Okawa, Maya, Khona, Mikail, Tanaka, Hidenori, Lubana, Ekdeep Singh |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.17194 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Towards an Understanding of Stepwise Inference in Transformers: A Synthetic Graph Navigation Model
by: Khona, Mikail, et al.
Published: (2024)
by: Khona, Mikail, et al.
Published: (2024)
Compositional Capabilities of Autoregressive Transformers: A Study on Synthetic, Interpretable Tasks
by: Ramesh, Rahul, et al.
Published: (2023)
by: Ramesh, Rahul, et al.
Published: (2023)
Compositional Abilities Emerge Multiplicatively: Exploring Diffusion Models on a Synthetic Task
by: Okawa, Maya, et al.
Published: (2023)
by: Okawa, Maya, et al.
Published: (2023)
ICLR: In-Context Learning of Representations
by: Park, Core Francisco, et al.
Published: (2024)
by: Park, Core Francisco, et al.
Published: (2024)
Emergence of Hidden Capabilities: Exploring Learning Dynamics in Concept Space
by: Park, Core Francisco, et al.
Published: (2024)
by: Park, Core Francisco, et al.
Published: (2024)
Swing-by Dynamics in Concept Learning and Compositional Generalization
by: Yang, Yongyi, et al.
Published: (2024)
by: Yang, Yongyi, et al.
Published: (2024)
A Percolation Model of Emergence: Analyzing Transformers Trained on a Formal Language
by: Lubana, Ekdeep Singh, et al.
Published: (2024)
by: Lubana, Ekdeep Singh, et al.
Published: (2024)
Emergence of Hierarchical Emotion Organization in Large Language Models
by: Zhao, Bo, et al.
Published: (2025)
by: Zhao, Bo, et al.
Published: (2025)
Competition Dynamics Shape Algorithmic Phases of In-Context Learning
by: Park, Core Francisco, et al.
Published: (2024)
by: Park, Core Francisco, et al.
Published: (2024)
Abrupt Learning in Transformers: A Case Study on Matrix Completion
by: Gopalani, Pulkit, et al.
Published: (2024)
by: Gopalani, Pulkit, et al.
Published: (2024)
In-Context Learning Dynamics with Random Binary Sequences
by: Bigelow, Eric J., et al.
Published: (2023)
by: Bigelow, Eric J., et al.
Published: (2023)
In-Context Learning Strategies Emerge Rationally
by: Wurgaft, Daniel, et al.
Published: (2025)
by: Wurgaft, Daniel, et al.
Published: (2025)
In-Context Learning of Energy Functions
by: Schaeffer, Rylan, et al.
Published: (2024)
by: Schaeffer, Rylan, et al.
Published: (2024)
From Flat to Hierarchical: Extracting Sparse Representations with Matching Pursuit
by: Costa, Valérie, et al.
Published: (2025)
by: Costa, Valérie, et al.
Published: (2025)
How Do LLMs Persuade? Linear Probes Can Uncover Persuasion Dynamics in Multi-Turn Conversations
by: Jaipersaud, Brandon, et al.
Published: (2025)
by: Jaipersaud, Brandon, et al.
Published: (2025)
Belief Dynamics Reveal the Dual Nature of In-Context Learning and Activation Steering
by: Bigelow, Eric, et al.
Published: (2025)
by: Bigelow, Eric, et al.
Published: (2025)
Analyzing (In)Abilities of SAEs via Formal Languages
by: Menon, Abhinav, et al.
Published: (2024)
by: Menon, Abhinav, et al.
Published: (2024)
Mechanistically analyzing the effects of fine-tuning on procedurally defined tasks
by: Jain, Samyak, et al.
Published: (2023)
by: Jain, Samyak, et al.
Published: (2023)
Evaluating Sparse Autoencoders: From Shallow Design to Matching Pursuit
by: Costa, Valérie, et al.
Published: (2025)
by: Costa, Valérie, et al.
Published: (2025)
Projecting Assumptions: The Duality Between Sparse Autoencoders and Concept Geometry
by: Hindupur, Sai Sumedh R., et al.
Published: (2025)
by: Hindupur, Sai Sumedh R., et al.
Published: (2025)
Aggregated Multi-output Gaussian Processes with Knowledge Transfer Across Domains
by: Tanaka, Yusuke, et al.
Published: (2022)
by: Tanaka, Yusuke, et al.
Published: (2022)
The Impact of Off-Policy Training Data on Probe Generalisation
by: Kirch, Nathalie, et al.
Published: (2025)
by: Kirch, Nathalie, et al.
Published: (2025)
What Makes and Breaks Safety Fine-tuning? A Mechanistic Study
by: Jain, Samyak, et al.
Published: (2024)
by: Jain, Samyak, et al.
Published: (2024)
From Isolation to Entanglement: When Do Interpretability Methods Identify and Disentangle Known Concepts?
by: Mueller, Aaron, et al.
Published: (2025)
by: Mueller, Aaron, et al.
Published: (2025)
Uncovering Latent Memories: Assessing Data Leakage and Memorization Patterns in Frontier AI Models
by: Duan, Sunny, et al.
Published: (2024)
by: Duan, Sunny, et al.
Published: (2024)
Detecting High-Stakes Interactions with Activation Probes
by: McKenzie, Alex, et al.
Published: (2025)
by: McKenzie, Alex, et al.
Published: (2025)
Features as Rewards: Scalable Supervision for Open-Ended Tasks via Interpretability
by: Prasad, Aaditya Vikram, et al.
Published: (2026)
by: Prasad, Aaditya Vikram, et al.
Published: (2026)
Provable Low-Frequency Bias of In-Context Learning of Representations
by: Yang, Yongyi, et al.
Published: (2025)
by: Yang, Yongyi, et al.
Published: (2025)
Stories in Space: In-Context Learning Trajectories in Conceptual Belief Space
by: Bigelow, Eric, et al.
Published: (2026)
by: Bigelow, Eric, et al.
Published: (2026)
Why Larger Models Learn More: Effects of Capacity, Interference, and Rare-Task Retention
by: Huang, Jing, et al.
Published: (2026)
by: Huang, Jing, et al.
Published: (2026)
Meta-Learning for Neural Network-based Temporal Point Processes
by: Takimoto, Yoshiaki, et al.
Published: (2024)
by: Takimoto, Yoshiaki, et al.
Published: (2024)
Manifold Steering Reveals the Shared Geometry of Neural Network Representation and Behavior
by: Wurgaft, Daniel, et al.
Published: (2026)
by: Wurgaft, Daniel, et al.
Published: (2026)
Stable Minima of ReLU Neural Networks Suffer from the Curse of Dimensionality: The Neural Shattering Phenomenon
by: Liang, Tongtong, et al.
Published: (2025)
by: Liang, Tongtong, et al.
Published: (2025)
$\textit{New News}$: System-2 Fine-tuning for Robust Integration of New Knowledge
by: Park, Core Francisco, et al.
Published: (2025)
by: Park, Core Francisco, et al.
Published: (2025)
Continuous-Time Analysis of Adaptive Optimization and Normalization
by: Gould, Rhys, et al.
Published: (2024)
by: Gould, Rhys, et al.
Published: (2024)
Shattered Compositionality: Counterintuitive Learning Dynamics of Transformers for Arithmetic
by: Zhao, Xingyu, et al.
Published: (2026)
by: Zhao, Xingyu, et al.
Published: (2026)
Do Sparse Autoencoders Capture Concept Manifolds?
by: Bhalla, Usha, et al.
Published: (2026)
by: Bhalla, Usha, et al.
Published: (2026)
Bridging Associative Memory and Probabilistic Modeling
by: Schaeffer, Rylan, et al.
Published: (2024)
by: Schaeffer, Rylan, et al.
Published: (2024)
Towards an Improved Understanding and Utilization of Maximum Manifold Capacity Representations
by: Schaeffer, Rylan, et al.
Published: (2024)
by: Schaeffer, Rylan, et al.
Published: (2024)
Cross-patient Seizure Onset Zone Classification by Patient-Dependent Weight
by: Zhao, Xuyang, et al.
Published: (2025)
by: Zhao, Xuyang, et al.
Published: (2025)
Similar Items
-
Towards an Understanding of Stepwise Inference in Transformers: A Synthetic Graph Navigation Model
by: Khona, Mikail, et al.
Published: (2024) -
Compositional Capabilities of Autoregressive Transformers: A Study on Synthetic, Interpretable Tasks
by: Ramesh, Rahul, et al.
Published: (2023) -
Compositional Abilities Emerge Multiplicatively: Exploring Diffusion Models on a Synthetic Task
by: Okawa, Maya, et al.
Published: (2023) -
ICLR: In-Context Learning of Representations
by: Park, Core Francisco, et al.
Published: (2024) -
Emergence of Hidden Capabilities: Exploring Learning Dynamics in Concept Space
by: Park, Core Francisco, et al.
Published: (2024)