4 Ways to Supercharge Your Data Science Workflow with Google AI StudioTowards Data Science With concrete examples of using AI Studio Build mode to learn faster, prototype smarter, communicate clearer, and automate quicker.
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With concrete examples of using AI Studio Build mode to learn faster, prototype smarter, communicate clearer, and automate quicker.
The post 4 Ways to Supercharge Your Data Science Workflow with Google AI Studio appeared first on Towards Data Science. Read More
Hosting Language Models on a BudgetKDnuggets Learn how to run your own language model for free using lightweight models and Hugging Face Spaces.
Learn how to run your own language model for free using lightweight models and Hugging Face Spaces. Read More
The Subset Sum Problem Solved in Linear Time for Dense Enough InputsTowards Data Science An optimal solution to the well-known NP-complete problem, when the input values are close enough to each other.
The post The Subset Sum Problem Solved in Linear Time for Dense Enough Inputs appeared first on Towards Data Science.
An optimal solution to the well-known NP-complete problem, when the input values are close enough to each other.
The post The Subset Sum Problem Solved in Linear Time for Dense Enough Inputs appeared first on Towards Data Science. Read More
5 Top AI-Powered App BuildersKDnuggets Take a tour of 5 of the most popular AI-powered app builders out there to leverage automation in the process of building software.
Take a tour of 5 of the most popular AI-powered app builders out there to leverage automation in the process of building software. Read More
Evaluating chain-of-thought monitorabilityOpenAI News OpenAI introduces a new framework and evaluation suite for chain-of-thought monitorability, covering 13 evaluations across 24 environments. Our findings show that monitoring a model’s internal reasoning is far more effective than monitoring outputs alone, offering a promising path toward scalable control as AI systems grow more capable.
OpenAI introduces a new framework and evaluation suite for chain-of-thought monitorability, covering 13 evaluations across 24 environments. Our findings show that monitoring a model’s internal reasoning is far more effective than monitoring outputs alone, offering a promising path toward scalable control as AI systems grow more capable. Read More
RePo: Language Models with Context Re-Positioningcs.AI updates on arXiv.org arXiv:2512.14391v1 Announce Type: cross
Abstract: In-context learning is fundamental to modern Large Language Models (LLMs); however, prevailing architectures impose a rigid and fixed contextual structure by assigning linear or constant positional indices. Drawing on Cognitive Load Theory (CLT), we argue that this uninformative structure increases extraneous cognitive load, consuming finite working memory capacity that should be allocated to deep reasoning and attention allocation. To address this, we propose RePo, a novel mechanism that reduces extraneous load via context re-positioning. Unlike standard approaches, RePo utilizes a differentiable module, $f_phi$, to assign token positions that capture contextual dependencies, rather than replying on pre-defined integer range. By continually pre-training on the OLMo-2 1B backbone, we demonstrate that RePo significantly enhances performance on tasks involving noisy contexts, structured data, and longer context length, while maintaining competitive performance on general short-context tasks. Detailed analysis reveals that RePo successfully allocate higher attention to distant but relevant information, assign positions in dense and non-linear space, and capture the intrinsic structure of the input context. Our code is available at https://github.com/SakanaAI/repo.
arXiv:2512.14391v1 Announce Type: cross
Abstract: In-context learning is fundamental to modern Large Language Models (LLMs); however, prevailing architectures impose a rigid and fixed contextual structure by assigning linear or constant positional indices. Drawing on Cognitive Load Theory (CLT), we argue that this uninformative structure increases extraneous cognitive load, consuming finite working memory capacity that should be allocated to deep reasoning and attention allocation. To address this, we propose RePo, a novel mechanism that reduces extraneous load via context re-positioning. Unlike standard approaches, RePo utilizes a differentiable module, $f_phi$, to assign token positions that capture contextual dependencies, rather than replying on pre-defined integer range. By continually pre-training on the OLMo-2 1B backbone, we demonstrate that RePo significantly enhances performance on tasks involving noisy contexts, structured data, and longer context length, while maintaining competitive performance on general short-context tasks. Detailed analysis reveals that RePo successfully allocate higher attention to distant but relevant information, assign positions in dense and non-linear space, and capture the intrinsic structure of the input context. Our code is available at https://github.com/SakanaAI/repo. Read More
Native and Compact Structured Latents for 3D Generationcs.AI updates on arXiv.org arXiv:2512.14692v1 Announce Type: cross
Abstract: Recent advancements in 3D generative modeling have significantly improved the generation realism, yet the field is still hampered by existing representations, which struggle to capture assets with complex topologies and detailed appearance. This paper present an approach for learning a structured latent representation from native 3D data to address this challenge. At its core is a new sparse voxel structure called O-Voxel, an omni-voxel representation that encodes both geometry and appearance. O-Voxel can robustly model arbitrary topology, including open, non-manifold, and fully-enclosed surfaces, while capturing comprehensive surface attributes beyond texture color, such as physically-based rendering parameters. Based on O-Voxel, we design a Sparse Compression VAE which provides a high spatial compression rate and a compact latent space. We train large-scale flow-matching models comprising 4B parameters for 3D generation using diverse public 3D asset datasets. Despite their scale, inference remains highly efficient. Meanwhile, the geometry and material quality of our generated assets far exceed those of existing models. We believe our approach offers a significant advancement in 3D generative modeling.
arXiv:2512.14692v1 Announce Type: cross
Abstract: Recent advancements in 3D generative modeling have significantly improved the generation realism, yet the field is still hampered by existing representations, which struggle to capture assets with complex topologies and detailed appearance. This paper present an approach for learning a structured latent representation from native 3D data to address this challenge. At its core is a new sparse voxel structure called O-Voxel, an omni-voxel representation that encodes both geometry and appearance. O-Voxel can robustly model arbitrary topology, including open, non-manifold, and fully-enclosed surfaces, while capturing comprehensive surface attributes beyond texture color, such as physically-based rendering parameters. Based on O-Voxel, we design a Sparse Compression VAE which provides a high spatial compression rate and a compact latent space. We train large-scale flow-matching models comprising 4B parameters for 3D generation using diverse public 3D asset datasets. Despite their scale, inference remains highly efficient. Meanwhile, the geometry and material quality of our generated assets far exceed those of existing models. We believe our approach offers a significant advancement in 3D generative modeling. Read More
AI in Human Resources: the real operational impactAI News Human Resources is an area in many organisations where AI can have significant operational impact. The technology is now being embedded into day-to-day operations, in activities like answering employees’ questions and supporting training. The clearest impact appears where organisations can measure the tech’s outcomes, typically in time saved and the numbers of queries successfully resolved.
The post AI in Human Resources: the real operational impact appeared first on AI News.
Human Resources is an area in many organisations where AI can have significant operational impact. The technology is now being embedded into day-to-day operations, in activities like answering employees’ questions and supporting training. The clearest impact appears where organisations can measure the tech’s outcomes, typically in time saved and the numbers of queries successfully resolved.
The post AI in Human Resources: the real operational impact appeared first on AI News. Read More
Wall Street’s AI gains are here — banks plan for fewer peopleAI News By December 2025, AI adoption on Wall Street had moved past experiments inside large US banks and into everyday operations. Speaking at a Goldman Sachs financial-services conference in New York on 9 December, bank executives described AI—particularly generative AI—as an operational upgrade already lifting productivity across engineering, operations, and customer service. The same discussion also
The post Wall Street’s AI gains are here — banks plan for fewer people appeared first on AI News.
By December 2025, AI adoption on Wall Street had moved past experiments inside large US banks and into everyday operations. Speaking at a Goldman Sachs financial-services conference in New York on 9 December, bank executives described AI—particularly generative AI—as an operational upgrade already lifting productivity across engineering, operations, and customer service. The same discussion also
The post Wall Street’s AI gains are here — banks plan for fewer people appeared first on AI News. Read More
A fine-grained look at causal effects in causal spacescs.AI updates on arXiv.org arXiv:2512.11919v2 Announce Type: replace-cross
Abstract: The notion of causal effect is fundamental across many scientific disciplines. Traditionally, quantitative researchers have studied causal effects at the level of variables; for example, how a certain drug dose (W) causally affects a patient’s blood pressure (Y). However, in many modern data domains, the raw variables-such as pixels in an image or tokens in a language model-do not have the semantic structure needed to formulate meaningful causal questions. In this paper, we offer a more fine-grained perspective by studying causal effects at the level of events, drawing inspiration from probability theory, where core notions such as independence are first given for events and sigma-algebras, before random variables enter the picture. Within the measure-theoretic framework of causal spaces, a recently introduced axiomatisation of causality, we first introduce several binary definitions that determine whether a causal effect is present, as well as proving some properties of them linking causal effect to (in)dependence under an intervention measure. Further, we provide quantifying measures that capture the strength and nature of causal effects on events, and show that we can recover the common measures of treatment effect as special cases.
arXiv:2512.11919v2 Announce Type: replace-cross
Abstract: The notion of causal effect is fundamental across many scientific disciplines. Traditionally, quantitative researchers have studied causal effects at the level of variables; for example, how a certain drug dose (W) causally affects a patient’s blood pressure (Y). However, in many modern data domains, the raw variables-such as pixels in an image or tokens in a language model-do not have the semantic structure needed to formulate meaningful causal questions. In this paper, we offer a more fine-grained perspective by studying causal effects at the level of events, drawing inspiration from probability theory, where core notions such as independence are first given for events and sigma-algebras, before random variables enter the picture. Within the measure-theoretic framework of causal spaces, a recently introduced axiomatisation of causality, we first introduce several binary definitions that determine whether a causal effect is present, as well as proving some properties of them linking causal effect to (in)dependence under an intervention measure. Further, we provide quantifying measures that capture the strength and nature of causal effects on events, and show that we can recover the common measures of treatment effect as special cases. Read More