Adaptive-lambda Subtracted Importance Sampled Scores in Machine Unlearning for DDPMs and VAEscs.AI updates on arXiv.org arXiv:2512.01054v2 Announce Type: replace-cross
Abstract: Machine Unlearning is essential for large generative models (VAEs, DDPMs) to comply with the right to be forgotten and prevent undesired content generation without costly retraining. Existing approaches, such as Static-lambda SISS for diffusion models, rely on a fixed mixing weight lambda, which is suboptimal because the required unlearning strength varies across samples and training stages.
We propose Adaptive-lambda SISS, a principled extension that turns lambda into a latent variable dynamically inferred at each training step. A lightweight inference network parameterizes an adaptive posterior over lambda, conditioned on contextual features derived from the instantaneous SISS loss terms (retain/forget losses and their gradients). This enables joint optimization of the diffusion model and the lambda-inference mechanism via a variational objective, yielding significantly better trade-offs.
We further extend the adaptive-lambda principle to score-based unlearning and introduce a multi-class variant of Score Forgetting Distillation. In addition, we present two new directions: (i) a hybrid objective combining the data-free efficiency of Score Forgetting Distillation with the direct gradient control of SISS, and (ii) a Reinforcement Learning formulation that treats unlearning as a sequential decision process, learning an optimal policy over a state space defined by the model’s current memory of the forget set.
Experiments on an augmented MNIST benchmark show that Adaptive-lambda SISS substantially outperforms the original static-lambda SISS, achieving stronger removal of forgotten classes while better preserving generation quality on the retain set.
arXiv:2512.01054v2 Announce Type: replace-cross
Abstract: Machine Unlearning is essential for large generative models (VAEs, DDPMs) to comply with the right to be forgotten and prevent undesired content generation without costly retraining. Existing approaches, such as Static-lambda SISS for diffusion models, rely on a fixed mixing weight lambda, which is suboptimal because the required unlearning strength varies across samples and training stages.
We propose Adaptive-lambda SISS, a principled extension that turns lambda into a latent variable dynamically inferred at each training step. A lightweight inference network parameterizes an adaptive posterior over lambda, conditioned on contextual features derived from the instantaneous SISS loss terms (retain/forget losses and their gradients). This enables joint optimization of the diffusion model and the lambda-inference mechanism via a variational objective, yielding significantly better trade-offs.
We further extend the adaptive-lambda principle to score-based unlearning and introduce a multi-class variant of Score Forgetting Distillation. In addition, we present two new directions: (i) a hybrid objective combining the data-free efficiency of Score Forgetting Distillation with the direct gradient control of SISS, and (ii) a Reinforcement Learning formulation that treats unlearning as a sequential decision process, learning an optimal policy over a state space defined by the model’s current memory of the forget set.
Experiments on an augmented MNIST benchmark show that Adaptive-lambda SISS substantially outperforms the original static-lambda SISS, achieving stronger removal of forgotten classes while better preserving generation quality on the retain set. Read More
Multimodal DeepResearcher: Generating Text-Chart Interleaved Reports From Scratch with Agentic Frameworkcs.AI updates on arXiv.org arXiv:2506.02454v3 Announce Type: replace-cross
Abstract: Visualizations play a crucial part in effective communication of concepts and information. Recent advances in reasoning and retrieval augmented generation have enabled Large Language Models (LLMs) to perform deep research and generate comprehensive reports. Despite its progress, existing deep research frameworks primarily focus on generating text-only content, leaving the automated generation of interleaved texts and visualizations underexplored. This novel task poses key challenges in designing informative visualizations and effectively integrating them with text reports. To address these challenges, we propose Formal Description of Visualization (FDV), a structured textual representation of charts that enables LLMs to learn from and generate diverse, high-quality visualizations. Building on this representation, we introduce Multimodal DeepResearcher, an agentic framework that decomposes the task into four stages: (1) researching, (2) exemplar report textualization, (3) planning, and (4) multimodal report generation. For the evaluation of generated multimodal reports, we develop MultimodalReportBench, which contains 100 diverse topics served as inputs along with 5 dedicated metrics. Extensive experiments across models and evaluation methods demonstrate the effectiveness of Multimodal DeepResearcher. Notably, utilizing the same Claude 3.7 Sonnet model, Multimodal DeepResearcher achieves an 82% overall win rate over the baseline method.
arXiv:2506.02454v3 Announce Type: replace-cross
Abstract: Visualizations play a crucial part in effective communication of concepts and information. Recent advances in reasoning and retrieval augmented generation have enabled Large Language Models (LLMs) to perform deep research and generate comprehensive reports. Despite its progress, existing deep research frameworks primarily focus on generating text-only content, leaving the automated generation of interleaved texts and visualizations underexplored. This novel task poses key challenges in designing informative visualizations and effectively integrating them with text reports. To address these challenges, we propose Formal Description of Visualization (FDV), a structured textual representation of charts that enables LLMs to learn from and generate diverse, high-quality visualizations. Building on this representation, we introduce Multimodal DeepResearcher, an agentic framework that decomposes the task into four stages: (1) researching, (2) exemplar report textualization, (3) planning, and (4) multimodal report generation. For the evaluation of generated multimodal reports, we develop MultimodalReportBench, which contains 100 diverse topics served as inputs along with 5 dedicated metrics. Extensive experiments across models and evaluation methods demonstrate the effectiveness of Multimodal DeepResearcher. Notably, utilizing the same Claude 3.7 Sonnet model, Multimodal DeepResearcher achieves an 82% overall win rate over the baseline method. Read More
Audio streaming platform SoundCloud has confirmed that outages and VPN connection issues over the past few days were caused by a security breach in which threat actors stole a database containing user information. […] Read More
The key elements in a security operations center’s strategy map very closely to the swim/bike/run events in a triathlon. SOCs, like triathletes, perform well when their “inputs” are strong. Read More
5 Workflow Automation Tools for All ProfessionalsKDnuggets Five powerful automation tools to help you streamline repetitive digital tasks, increase productivity, and create smarter workflows without requiring deep technical skills.
Five powerful automation tools to help you streamline repetitive digital tasks, increase productivity, and create smarter workflows without requiring deep technical skills. Read More
Mining business learnings for AI deploymentAI News Mining conglomerate BHP describes AI as the way it’s turning operational data into better day-to-day decisions. A blog post from the company highlights the analysis of data from sensors and monitoring systems to spot patterns and flag issues for plant machinery, giving choices to decision-makers that can improve efficiency and safety – plus reduce environmental
The post Mining business learnings for AI deployment appeared first on AI News.
Mining conglomerate BHP describes AI as the way it’s turning operational data into better day-to-day decisions. A blog post from the company highlights the analysis of data from sensors and monitoring systems to spot patterns and flag issues for plant machinery, giving choices to decision-makers that can improve efficiency and safety – plus reduce environmental
The post Mining business learnings for AI deployment appeared first on AI News. Read More
Google has announced that it’s discontinuing its dark web report tool in February 2026, less than two years after it was launched as a way for users to monitor if their personal information is found on the dark web. To that end, scans for new dark web breaches will be stopped on January 15, 2026, […]
AWS’s legacy will be in AI successAI News As the company that kick-started the cloud computing revolution, Amazon is one of the world’s biggest companies whose practices in all things technological can be regarded as a blueprint for implementing new technology. This article looks at some of the ways that the company is deploying AI in its operations. Amazon’s latest AI strategy has
The post AWS’s legacy will be in AI success appeared first on AI News.
As the company that kick-started the cloud computing revolution, Amazon is one of the world’s biggest companies whose practices in all things technological can be regarded as a blueprint for implementing new technology. This article looks at some of the ways that the company is deploying AI in its operations. Amazon’s latest AI strategy has
The post AWS’s legacy will be in AI success appeared first on AI News. Read More
Geospatial exploratory data analysis with GeoPandas and DuckDBTowards Data Science In this article, I’ll show you how to use two popular Python libraries to carry out some geospatial analysis of traffic accident data within the UK. I was a relatively early adopter of DuckDB, the fast OLAP database, after it became available, but only recently realised that, through an extension, it offered a large number
The post Geospatial exploratory data analysis with GeoPandas and DuckDB appeared first on Towards Data Science.
In this article, I’ll show you how to use two popular Python libraries to carry out some geospatial analysis of traffic accident data within the UK. I was a relatively early adopter of DuckDB, the fast OLAP database, after it became available, but only recently realised that, through an extension, it offered a large number
The post Geospatial exploratory data analysis with GeoPandas and DuckDB appeared first on Towards Data Science. Read More
Adaptive infrastructure for foundation model training with elastic training on SageMaker HyperPodArtificial Intelligence Amazon SageMaker HyperPod now supports elastic training, enabling your machine learning (ML) workloads to automatically scale based on resource availability. In this post, we demonstrate how elastic training helps you maximize GPU utilization, reduce costs, and accelerate model development through dynamic resource adaptation, while maintain training quality and minimizing manual intervention.
Amazon SageMaker HyperPod now supports elastic training, enabling your machine learning (ML) workloads to automatically scale based on resource availability. In this post, we demonstrate how elastic training helps you maximize GPU utilization, reduce costs, and accelerate model development through dynamic resource adaptation, while maintain training quality and minimizing manual intervention. Read More
