Cover Image

Unlearning as Ablation: Toward a Falsifiable Benchmark for Generative Scientific Discovery

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Yang, Robert
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges
by: Reddy, Chandan K, et al.
Published: (2024)

Deep Unlearn: Benchmarking Machine Unlearning for Image Classification
by: Cadet, Xavier F., et al.
Published: (2024)

Prior-Guided Symbolic Regression: Towards Scientific Consistency in Equation Discovery
by: Xiao, Jing, et al.
Published: (2026)

The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery
by: Lu, Chris, et al.
Published: (2024)

Evaluation-driven Scaling for Scientific Discovery
by: Ye, Haotian, et al.
Published: (2026)

Automated Scientific Discovery: From Equation Discovery to Autonomous Discovery Systems
by: Kramer, Stefan, et al.
Published: (2023)

Design Principles for Falsifiable, Replicable and Reproducible Empirical ML Research
by: Vranješ, Daniel, et al.
Published: (2024)

Grokking as a Falsifiable Finite-Size Transition
by: Bi, Yuda, et al.
Published: (2026)

Towards Reliable Testing of Machine Unlearning
by: Mazhar, Anna, et al.
Published: (2026)

Simulation as Supervision: Mechanistic Pretraining for Scientific Discovery
by: Dudley, Carson, et al.
Published: (2025)

Toward a Team of AI-made Scientists for Scientific Discovery from Gene Expression Data
by: Liu, Haoyang, et al.
Published: (2024)

The Use of AI-Robotic Systems for Scientific Discovery
by: Gower, Alexander H., et al.
Published: (2024)

OpenGU: A Comprehensive Benchmark for Graph Unlearning
by: Fan, Bowen, et al.
Published: (2025)

Artificial Scientific Discovery
by: Norelli, Antonio
Published: (2024)

Nature Language Model: Deciphering the Language of Nature for Scientific Discovery
by: Xia, Yingce, et al.
Published: (2025)

Post-hoc Interpretability Illumination for Scientific Interaction Discovery
by: Zhang, Ling, et al.
Published: (2024)

Principle-Evolvable Scientific Discovery via Uncertainty Minimization
by: Pu, Yingming, et al.
Published: (2026)

MU-Bench: A Multitask Multimodal Benchmark for Machine Unlearning
by: Cheng, Jiali, et al.
Published: (2024)

LLM-SRBench: A New Benchmark for Scientific Equation Discovery with Large Language Models
by: Shojaee, Parshin, et al.
Published: (2025)

Towards Federated Domain Unlearning: Verification Methodologies and Challenges
by: Tam, Kahou, et al.
Published: (2024)

Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery
by: Matsubara, Yoshitomo, et al.
Published: (2022)

BLUR: A Benchmark for LLM Unlearning Robust to Forget-Retain Overlap
by: Hu, Shengyuan, et al.
Published: (2025)

Agentic Unlearning: When LLM Agent Meets Machine Unlearning
by: Wang, Bin, et al.
Published: (2026)

Towards Aligned Data Removal via Twin Machine Unlearning
by: Ji, Haoxuan, et al.
Published: (2024)

UNO: Unlearning via Orthogonalization in Generative models
by: Mandal, Pinak, et al.
Published: (2025)

On the Limitations and Prospects of Machine Unlearning for Generative AI
by: Zhou, Shiji, et al.
Published: (2024)

PiFlow: Principle-Aware Scientific Discovery with Multi-Agent Collaboration
by: Pu, Yingming, et al.
Published: (2025)

DeltaEvolve: Accelerating Scientific Discovery through Momentum-Driven Evolution
by: Jiang, Jiachen, et al.
Published: (2026)

Towards Lifecycle Unlearning Commitment Management: Measuring Sample-level Unlearning Completeness
by: Wang, Cheng-Long, et al.
Published: (2025)

OPC: One-Point-Contraction Unlearning Toward Deep Feature Forgetting
by: Jung, Jaeheun, et al.
Published: (2025)

ScienceAgentBench: Toward Rigorous Assessment of Language Agents for Data-Driven Scientific Discovery
by: Chen, Ziru, et al.
Published: (2024)

Ready2Unlearn: A Learning-Time Approach for Preparing Models with Future Unlearning Readiness
by: Duan, Hanyu, et al.
Published: (2025)

AutoDiscovery: Open-ended Scientific Discovery via Bayesian Surprise
by: Agarwal, Dhruv, et al.
Published: (2025)

Curie: Toward Rigorous and Automated Scientific Experimentation with AI Agents
by: Kon, Patrick Tser Jern, et al.
Published: (2025)

Ablation Based Counterfactuals
by: Dai, Zheng, et al.
Published: (2024)

Orthogonal Soft Pruning for Efficient Class Unlearning
by: Gong, Qinghui, et al.
Published: (2025)

AutoSciDACT: Automated Scientific Discovery through Contrastive Embedding and Hypothesis Testing
by: Bright-Thonney, Samuel, et al.
Published: (2025)

DP2Unlearning: An Efficient and Guaranteed Unlearning Framework for LLMs
by: Mahmud, Tamim Al, et al.
Published: (2025)

Unlearning Information Bottleneck: Machine Unlearning of Systematic Patterns and Biases
by: Han, Ling, et al.
Published: (2024)

Zero-Shot Machine Unlearning with Proxy Adversarial Data Generation
by: Chen, Huiqiang, et al.
Published: (2025)