Cover Image

The Limits of Inference Scaling Through Resampling

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Stroebl, Benedikt, Kapoor, Sayash, Narayanan, Arvind
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

AI Agents That Matter
by: Kapoor, Sayash, et al.
Published: (2024)

CORE-Bench: Fostering the Credibility of Published Research Through a Computational Reproducibility Agent Benchmark
by: Siegel, Zachary S., et al.
Published: (2024)

Towards a Science of AI Agent Reliability
by: Rabanser, Stephan, et al.
Published: (2026)

Promises and pitfalls of artificial intelligence for legal applications
by: Kapoor, Sayash, et al.
Published: (2024)

Foundation Model Transparency Reports
by: Bommasani, Rishi, et al.
Published: (2024)

The 2024 Foundation Model Transparency Index
by: Bommasani, Rishi, et al.
Published: (2024)

On Limitations of the Transformer Architecture
by: Peng, Binghui, et al.
Published: (2024)

Decoupled Prioritized Resampling for Offline RL
by: Yue, Yang, et al.
Published: (2023)

The 2025 Foundation Model Transparency Index
by: Wan, Alexander, et al.
Published: (2025)

ART: Adaptive Resampling-based Training for Imbalanced Classification
by: Basandrai, Arjun, et al.
Published: (2025)

Large Scale Constrained Clustering With Reinforcement Learning
by: Schesch, Benedikt, et al.
Published: (2024)

Reject, Resample, Repeat: Understanding Parallel Reasoning in Language Model Inference
by: Golowich, Noah, et al.
Published: (2026)

mldr.resampling: Efficient Reference Implementations of Multilabel Resampling Algorithms
by: Rivera, Antonio J., et al.
Published: (2023)

Data Balancing Strategies: A Systematic Survey of Resampling and Augmentation Methods
by: Yousefimehr, Behnam, et al.
Published: (2025)

QDeepGR4J: Quantile-based ensemble of deep learning and GR4J hybrid rainfall-runoff models for extreme flow prediction with uncertainty quantification
by: Kapoor, Arpit, et al.
Published: (2025)

Valid Property-Enhanced Contrastive Learning for Targeted Optimization & Resampling for Novel Drug Design
by: Banerjee, Amartya, et al.
Published: (2025)

Synthetic Data in Education: Empirical Insights from Traditional Resampling and Deep Generative Models
by: Chinodakufa, Tapiwa Amion, et al.
Published: (2026)

Improving ARDS Diagnosis Through Context-Aware Concept Bottleneck Models
by: Narain, Anish, et al.
Published: (2025)

Thought Branches: Interpreting LLM Reasoning Requires Resampling
by: Macar, Uzay, et al.
Published: (2025)

Data Presentation Over Architecture: Resampling Strategies for Credit Risk Prediction with Tabular Foundation Models
by: Tanna, Aditya, et al.
Published: (2026)

Geometric Scaling of Bayesian Inference in LLMs
by: Agarwal, Naman, et al.
Published: (2025)

Compute-Optimal LLMs Provably Generalize Better With Scale
by: Finzi, Marc, et al.
Published: (2025)

Fundamental Limits of Membership Inference Attacks on Machine Learning Models
by: Aubinais, Eric, et al.
Published: (2023)

The Responsible Foundation Model Development Cheatsheet: A Review of Tools & Resources
by: Longpre, Shayne, et al.
Published: (2024)

Observation Adaptation via Annealed Importance Resampling for Partially Observable Markov Decision Processes
by: Zhang, Yunuo, et al.
Published: (2025)

Sample, Scrutinize and Scale: Effective Inference-Time Search by Scaling Verification
by: Zhao, Eric, et al.
Published: (2025)

Leveraging Influence Functions for Resampling Data in Physics-Informed Neural Networks
by: Naujoks, Jonas R., et al.
Published: (2025)

Localized Cultural Knowledge is Conserved and Controllable in Large Language Models
by: Veselovsky, Veniamin, et al.
Published: (2025)

Recursive Inference Scaling: A Winning Path to Scalable Inference in Language and Multimodal Systems
by: Alabdulmohsin, Ibrahim, et al.
Published: (2025)

The Importance of Being Lazy: Scaling Limits of Continual Learning
by: Graldi, Jacopo, et al.
Published: (2025)

TRIM: Hybrid Inference via Targeted Stepwise Routing in Multi-Step Reasoning Tasks
by: Kapoor, Vansh, et al.
Published: (2026)

Deep Dense Exploration for LLM Reinforcement Learning via Pivot-Driven Resampling
by: Guo, Yiran, et al.
Published: (2026)

Diversified Scaling Inference in Time Series Foundation Models
by: Hua, Ruijin, et al.
Published: (2026)

Scaling On-Device GPU Inference for Large Generative Models
by: Tang, Jiuqiang, et al.
Published: (2025)

A Methodology for Thermal Limit Bias Predictability Through Artificial Intelligence
by: Tunga, Anirudh, et al.
Published: (2026)

PLADIS: Pushing the Limits of Attention in Diffusion Models at Inference Time by Leveraging Sparsity
by: Kim, Kwanyoung, et al.
Published: (2025)

CATS: Cascaded Adaptive Tree Speculation for Memory-Limited LLM Inference Acceleration
by: Han, Yuning, et al.
Published: (2026)

On the Societal Impact of Open Foundation Models
by: Kapoor, Sayash, et al.
Published: (2024)

DeepACTIF: Efficient Feature Attribution via Activation Traces in Neural Sequence Models
by: Hosp, Benedikt W.
Published: (2025)

Formal Logic Enabled Personalized Federated Learning Through Property Inference
by: An, Ziyan, et al.
Published: (2024)