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Cursor makes its biggest India push yet ahead of SpaceX acquisition with localized pricing4h◆Reverso: Efficient Time Series Foundation Models for Zero-shot Forecasting5h◆Multinex: Lightweight Low-light Image Enhancement via Multi-prior Retinex5h◆Market Design for AI: Beyond the Copyright Binary5h◆Who Pays the Price? Stakeholder-Centric Prompt Injection Benchmarking for Real-world Web Agents5h◆TextRich: A Multi-Domain Benchmark for Detecting AI-Generated Text-Rich Images from GPT-Image-25h◆DiscoLoop: Looping Discrete Embeddings and Continuous Hidden States for Multi-hop Reasoning5h◆From World Models to World Action Models: A Concise Tutorial for Robotics5h◆QuantFlow: A Federated Mamba-Based Post-Transformer Foundation Model for Time-Series Forecasting5h◆Multi-Turn On-Policy Distillation with Prefix Replay5h◆Operational Proto-Introspection in Looped Language Models: Process-Quality Taps, Executable Branching, and the Readout-Control Boundary5h◆Skillware: A Software Ontology and Engineering Lifecycle for Persistent Behavioral Artifacts5h◆MedDDC-Eval: Diagnosis-Decoupled Evaluation of Multi-Turn Medical Consultation Agents5h◆PhantomFill: When the Form Demands an Answer, Language Models Invent One5h◆Error Certificates for KV-Cache Eviction via Randomized Design5h◆Explaining GAND: A Resource on Gender-Ambiguous Natural Data & Contrastive Attribution5h◆MioFFAn: an Annotation Software for Formula Formalization with LLM Automation Capabilities5h◆LA-RL: Label-Aware Self-Reflection for Reinforcement Learning in Information Extraction5h◆Mwando: Leveraging AI to Preserve and Teach shiKomori5h◆The JEPA Paradox in Language: The Geometry of Linguistic Alternatives5h◆Cursor makes its biggest India push yet ahead of SpaceX acquisition with localized pricing4h◆Reverso: Efficient Time Series Foundation Models for Zero-shot Forecasting5h◆Multinex: Lightweight Low-light Image Enhancement via Multi-prior Retinex5h◆Market Design for AI: Beyond the Copyright Binary5h◆Who Pays the Price? Stakeholder-Centric Prompt Injection Benchmarking for Real-world Web Agents5h◆TextRich: A Multi-Domain Benchmark for Detecting AI-Generated Text-Rich Images from GPT-Image-25h◆DiscoLoop: Looping Discrete Embeddings and Continuous Hidden States for Multi-hop Reasoning5h◆From World Models to World Action Models: A Concise Tutorial for Robotics5h◆QuantFlow: A Federated Mamba-Based Post-Transformer Foundation Model for Time-Series Forecasting5h◆Multi-Turn On-Policy Distillation with Prefix Replay5h◆Operational Proto-Introspection in Looped Language Models: Process-Quality Taps, Executable Branching, and the Readout-Control Boundary5h◆Skillware: A Software Ontology and Engineering Lifecycle for Persistent Behavioral Artifacts5h◆MedDDC-Eval: Diagnosis-Decoupled Evaluation of Multi-Turn Medical Consultation Agents5h◆PhantomFill: When the Form Demands an Answer, Language Models Invent One5h◆Error Certificates for KV-Cache Eviction via Randomized Design5h◆Explaining GAND: A Resource on Gender-Ambiguous Natural Data & Contrastive Attribution5h◆MioFFAn: an Annotation Software for Formula Formalization with LLM Automation Capabilities5h◆LA-RL: Label-Aware Self-Reflection for Reinforcement Learning in Information Extraction5h◆Mwando: Leveraging AI to Preserve and Teach shiKomori5h◆The JEPA Paradox in Language: The Geometry of Linguistic Alternatives5h◆
Tag

#unlearning

3 articles tagged #unlearning

arxivMay 28

Less is More: Geometric Unlearning for LLMs with Minimal Data Disclosure

arXiv:2605.01735v2 Announce Type: replace Abstract: As large language models (LLMs) are increasingly deployed in real-world systems, they must support post-hoc removal of specific content to meet privacy and governance requirements. This motivates selective unlearning, which suppresses information a

#unlearning#large-language-models#privacyRead on arxiv →
arxivMay 8

SMI: Statistical Membership Inference for Reliable Unlearned Model Auditing

HomeModelsNews

arXiv:2602.01150v2 Announce Type: replace-cross Abstract: Machine unlearning (MU) is essential for enforcing the right to be forgotten in machine learning systems. A key challenge of MU is how to reliably audit whether a model has truly forgotten specified training data. Membership Inference Attacks

#machine-learning#unlearning#auditingRead on arxiv →
arxivApr 30bullish

Asynchronous Federated Unlearning with Invariance Calibration for Medical Imaging

arXiv:2604.26809v1 Announce Type: new Abstract: Federated Unlearning (FU) is an emerging paradigm in Federated Learning (FL) that enables participating clients to fully remove their contributions from a trained global model, driven by data protection regulations that mandate the right to be forgotte

#federated-learning#unlearning#medical-imagingRead on arxiv →