Cover Image

Measuring In-Context Computation Complexity via Hidden State Prediction

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Herrmann, Vincent, Csordás, Róbert, Schmidhuber, Jürgen
Format:	Preprint
Published:	2025
Subjects:	Machine Learning I.2.6
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Multiple Token Divergence: Measuring and Steering In-Context Computation Density
by: Herrmann, Vincent, et al.
Published: (2025)

Learning Useful Representations of Recurrent Neural Network Weight Matrices
by: Herrmann, Vincent, et al.
Published: (2024)

Interestingness as an Inductive Heuristic for Future Compression Progress
by: Herrmann, Vincent, et al.
Published: (2026)

Upside Down Reinforcement Learning with Policy Generators
by: Di Ventura, Jacopo, et al.
Published: (2025)

Fusing Rewards and Preferences in Reinforcement Learning
by: Khorasani, Sadegh, et al.
Published: (2025)

Faster Predictive Coding Networks via Better Initialization
by: Pinchetti, Luca, et al.
Published: (2026)

Scaling Value Iteration Networks to 5000 Layers for Extreme Long-Term Planning
by: Wang, Yuhui, et al.
Published: (2024)

Chunked TabPFN: Exact Training-Free In-Context Learning for Long-Context Tabular Data
by: Sergazinov, Renat, et al.
Published: (2025)

2Mamba2Furious: Linear in Complexity, Competitive in Accuracy
by: Mongaras, Gabriel, et al.
Published: (2026)

AI and Machine Learning Approaches for Predicting Nanoparticles Toxicity The Critical Role of Physiochemical Properties
by: Yousaf, Iqra
Published: (2024)

Zero-Shot Context Generalization in Reinforcement Learning from Few Training Contexts
by: Chapman, James, et al.
Published: (2025)

Efficient Morphology-Control Co-Design via Stackelberg Proximal Policy Optimization
by: Dai, Yanning, et al.
Published: (2026)

Securing Reliability: A Brief Overview on Enhancing In-Context Learning for Foundation Models
by: Huang, Yunpeng, et al.
Published: (2024)

Measuring IIA Violations in Similarity Choices with Bayesian Models
by: Corrêa, Hugo Sales, et al.
Published: (2025)

Towards a Robust Soft Baby Robot With Rich Interaction Ability for Advanced Machine Learning Algorithms
by: Alhakami, Mohannad, et al.
Published: (2024)

Predicting and improving test-time scaling laws via reward tail-guided search
by: Li, Muheng, et al.
Published: (2026)

Learning Transferable Predictability Representations
by: Goswami, Diyali, et al.
Published: (2026)

Exploring Neural Granger Causality with xLSTMs: Unveiling Temporal Dependencies in Complex Data
by: Poonia, Harsh, et al.
Published: (2025)

Potential-Based Intrinsic Motivation: Preserving Optimality With Complex, Non-Markovian Shaping Rewards
by: Forbes, Grant C., et al.
Published: (2024)

Market-Alignment Risk in Pricing Agents: Trace Diagnostics and Trace-Prior RL under Hidden Competitor State
by: Zhu, Peiying, et al.
Published: (2026)

Informed Priors for Knowledge Integration in Trajectory Prediction
by: Schlauch, Christian, et al.
Published: (2022)

Explaining Temporal Graph Predictions With Shapley Values
by: Sussek, Lea-Marie, et al.
Published: (2026)

FluidWorld: Reaction-Diffusion Dynamics as a Predictive Substrate for World Models
by: Polly, Fabien
Published: (2026)

Deep Memory Search: A Metaheuristic Approach for Optimizing Heuristic Search
by: Hedar, Abdel-Rahman, et al.
Published: (2024)

Pre-Ictal Seizure Prediction Using Personalized Deep Learning
by: Jaddu, Shriya, et al.
Published: (2024)

RobustBlack: Challenging Black-Box Adversarial Attacks on State-of-the-Art Defenses
by: Djilani, Mohamed, et al.
Published: (2024)

Automatic Album Sequencing
by: Herrmann, Vincent, et al.
Published: (2024)

Normalization Layer Per-Example Gradients are Sufficient to Predict Gradient Noise Scale in Transformers
by: Gray, Gavia, et al.
Published: (2024)

HGCN(O): A Self-Tuning GCN HyperModel Toolkit for Outcome Prediction in Event-Sequence Data
by: Wang, Fang, et al.
Published: (2025)

Black Box Model Explanations and the Human Interpretability Expectations -- An Analysis in the Context of Homicide Prediction
by: Ribeiro, José, et al.
Published: (2022)

Almost Equivariance via Lie Algebra Convolutions
by: McNeela, Daniel
Published: (2023)

Real-Time Pulsatile Flow Prediction for Realistic, Diverse Intracranial Aneurysm Morphologies using a Graph Transformer and Steady-Flow Data Augmentation
by: Sheng, Yiying, et al.
Published: (2026)

Explainable Graph Representation Learning via Graph Pattern Analysis
by: Wang, Xudong, et al.
Published: (2025)

Inference-Time Machine Unlearning via Gated Activation Redirection
by: Turani, Vinícius Conte, et al.
Published: (2026)

Predictable Gradient Manifolds in Deep Learning: Temporal Path-Length and Intrinsic Rank as a Complexity Regime
by: Calvo, Anherutowa
Published: (2026)

Low-Rank Adapters Initialization via Gradient Surgery for Continual Learning
by: Pasquali, Joana, et al.
Published: (2026)

MotherNet: Fast Training and Inference via Hyper-Network Transformers
by: Müller, Andreas, et al.
Published: (2023)

Entropy-Based Measurement of Value Drift and Alignment Work in Large Language Models
by: Fadli, Samih
Published: (2025)

xLSTM-Mixer: Multivariate Time Series Forecasting by Mixing via Scalar Memories
by: Kraus, Maurice, et al.
Published: (2024)

Human-Corrected Labels Learning: Enhancing Labels Quality via Human Correction of VLMs Discrepancies
by: Li, Zhongnian, et al.
Published: (2025)