:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Wallace, Eric, Watkins, Olivia, Wang, Miles, Chen, Kai, Koch, Chris
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	https://arxiv.org/abs/2508.03153
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Differentially Private Worst-group Risk Minimization
by: Zhou, Xinyu, et al.
Published: (2024)

Tamper-Resistant Safeguards for Open-Weight LLMs
by: Tamirisa, Rishub, et al.
Published: (2024)

How Worst-Case Are Adversarial Attacks? Linking Adversarial and Perturbation Robustness
by: Rossolini, Giulio
Published: (2026)

Open Problems in Frontier AI Risk Management
by: Ziosi, Marta, et al.
Published: (2026)

IH-Challenge: A Training Dataset to Improve Instruction Hierarchy on Frontier LLMs
by: Guo, Chuan, et al.
Published: (2026)

Infra-Bayesian Reinforcement Learning Agents Outperform Classical RL For Worst-Case Robustness
by: Aryal, Manish, et al.
Published: (2026)

NVLM: Open Frontier-Class Multimodal LLMs
by: Dai, Wenliang, et al.
Published: (2024)

Provably Efficient Exploration in Quantum Reinforcement Learning with Logarithmic Worst-Case Regret
by: Zhong, Han, et al.
Published: (2023)

Risk Profiling and Modulation for LLMs
by: Wang, Yikai, et al.
Published: (2025)

Drag-and-Drop LLMs: Zero-Shot Prompt-to-Weights
by: Liang, Zhiyuan, et al.
Published: (2025)

NNsight and NDIF: Democratizing Access to Open-Weight Foundation Model Internals
by: Fiotto-Kaufman, Jaden, et al.
Published: (2024)

Worst-Case Regret Bounds for Exploration via Randomized Value Functions
by: Russo, Daniel
Published: (2019)

The Path to Open Innovation: Peer-Review Under Fire (PRUF)
by: Billions, Ava, et al.
Published: (2025)

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses
by: Miao, Changhao, et al.
Published: (2026)

Exploring Memorization and Copyright Violation in Frontier LLMs: A Study of the New York Times v. OpenAI 2023 Lawsuit
by: Freeman, Joshua, et al.
Published: (2024)

Deep Ignorance: Filtering Pretraining Data Builds Tamper-Resistant Safeguards into Open-Weight LLMs
by: O'Brien, Kyle, et al.
Published: (2025)

Worst-case low-rank approximations
by: Fries, Anya, et al.
Published: (2026)

The Devil in the Details: Emergent Misalignment, Format and Coherence in Open-Weights LLMs
by: Dickson, Craig
Published: (2025)

Goedel-Prover: A Frontier Model for Open-Source Automated Theorem Proving
by: Lin, Yong, et al.
Published: (2025)

FrontierScience: Evaluating AI's Ability to Perform Expert-Level Scientific Tasks
by: Wang, Miles, et al.
Published: (2026)

Exploring the Frontiers of LLMs in Psychological Applications: A Comprehensive Review
by: Ke, Luoma, et al.
Published: (2024)

Bounding the Worst-class Error: A Boosting Approach
by: Saito, Yuya, et al.
Published: (2023)

OpenEstimate: Evaluating LLMs on Reasoning Under Uncertainty with Real-World Data
by: Renda, Alana, et al.
Published: (2025)

AlphaLab: Autonomous Multi-Agent Research Across Optimization Domains with Frontier LLMs
by: Hogan, Brendan R., et al.
Published: (2026)

To Compress or Not? Pushing the Frontier of Lossless GenAI Model Weights Compression with Exponent Concentration
by: Yang, Zeyu, et al.
Published: (2025)

Spurious Correlation-Aware Embedding Regularization for Worst-Group Robustness
by: Park, Subeen, et al.
Published: (2025)

Challenging Forgets: Unveiling the Worst-Case Forget Sets in Machine Unlearning
by: Fan, Chongyu, et al.
Published: (2024)

DAQ: Density-Aware Post-Training Weight-Only Quantization For LLMs
by: Luo, Yingsong, et al.
Published: (2024)

Foundations and Frontiers of Graph Learning Theory
by: Huang, Yu, et al.
Published: (2024)

Position: Weight Space Should Be a First-Class Generative AI Modality
by: Wang, Zhangyang, et al.
Published: (2026)

Extrapolative Weight Averaging Reveals Correctness-Efficiency Frontiers in Code RL
by: Zheng, Kunhao, et al.
Published: (2026)

Compiling Agentic Workflows into LLM Weights: Near-Frontier Quality at Two Orders of Magnitude Less Cost
by: Dennis, Simon, et al.
Published: (2026)

Frontier LLMs Still Struggle with Simple Reasoning Tasks
by: Malek, Alan, et al.
Published: (2025)

Distributionally Robust Safety Verification of Neural Networks via Worst-Case CVaR
by: Kishida, Masako
Published: (2025)

Calibration and Transformation-Free Weight-Only LLMs Quantization via Dynamic Grouping
by: Zheng, Xinzhe, et al.
Published: (2025)

Feature Subset Weighting for Distance-based Supervised Learning through Choquet Integration
by: Theerens, Adnan, et al.
Published: (2025)

Physics-model-guided Worst-case Sampling for Safe Reinforcement Learning
by: Cao, Hongpeng, et al.
Published: (2024)

Improving Rule-based Reasoning in LLMs using Neurosymbolic Representations
by: Dhanraj, Varun, et al.
Published: (2025)

Worst-Case Convergence Time of ML Algorithms via Extreme Value Theory
by: Tizpaz-Niari, Saeid, et al.
Published: (2024)

Towards Worst-Case Guarantees with Scale-Aware Interpretability
by: Greenspan, Lauren, et al.
Published: (2026)