Causal Masking on Spatial Data: An Information-Theoretic Case for Learning Spatial Datasets with Unimodal Language Modelscs.AI updates on arXiv.org arXiv:2510.27009v1 Announce Type: new
Abstract: Language models are traditionally designed around causal masking. In domains with spatial or relational structure, causal masking is often viewed as inappropriate, and sequential linearizations are instead used. Yet the question of whether it is viable to accept the information loss introduced by causal masking on nonsequential data has received little direct study, in part because few domains offer both spatial and sequential representations of the same dataset. In this work, we investigate this issue in the domain of chess, which naturally supports both representations. We train language models with bidirectional and causal self-attention mechanisms on both spatial (board-based) and sequential (move-based) data. Our results show that models trained on spatial board states – textit{even with causal masking} – consistently achieve stronger playing strength than models trained on sequential data. While our experiments are conducted on chess, our results are methodological and may have broader implications: applying causal masking to spatial data is a viable procedure for training unimodal LLMs on spatial data, and in some domains is even preferable to sequentialization.
arXiv:2510.27009v1 Announce Type: new
Abstract: Language models are traditionally designed around causal masking. In domains with spatial or relational structure, causal masking is often viewed as inappropriate, and sequential linearizations are instead used. Yet the question of whether it is viable to accept the information loss introduced by causal masking on nonsequential data has received little direct study, in part because few domains offer both spatial and sequential representations of the same dataset. In this work, we investigate this issue in the domain of chess, which naturally supports both representations. We train language models with bidirectional and causal self-attention mechanisms on both spatial (board-based) and sequential (move-based) data. Our results show that models trained on spatial board states – textit{even with causal masking} – consistently achieve stronger playing strength than models trained on sequential data. While our experiments are conducted on chess, our results are methodological and may have broader implications: applying causal masking to spatial data is a viable procedure for training unimodal LLMs on spatial data, and in some domains is even preferable to sequentialization. Read More
Anyscale and NovaSky Team Releases SkyRL tx v0.1.0: Bringing Tinker Compatible Reinforcement Learning RL Engine To Local GPU ClustersMarkTechPost How can AI teams run Tinker style reinforcement learning on large language models using their own infrastructure with a single unified engine? Anyscale and NovaSky (UC Berkeley) Team releases SkyRL tx v0.1.0 that gives developers a way to run a Tinker compatible training and inference engine directly on their own hardware, while keeping the same
The post Anyscale and NovaSky Team Releases SkyRL tx v0.1.0: Bringing Tinker Compatible Reinforcement Learning RL Engine To Local GPU Clusters appeared first on MarkTechPost.
How can AI teams run Tinker style reinforcement learning on large language models using their own infrastructure with a single unified engine? Anyscale and NovaSky (UC Berkeley) Team releases SkyRL tx v0.1.0 that gives developers a way to run a Tinker compatible training and inference engine directly on their own hardware, while keeping the same
The post Anyscale and NovaSky Team Releases SkyRL tx v0.1.0: Bringing Tinker Compatible Reinforcement Learning RL Engine To Local GPU Clusters appeared first on MarkTechPost. Read More
How to Build Supervised AI Models When You Don’t Have Annotated DataMarkTechPost One of the biggest challenges in real-world machine learning is that supervised models require labeled data—yet in many practical scenarios, the data you start with is almost always unlabeled. Manually annotating thousands of samples isn’t just slow; it’s expensive, tedious, and often impractical. This is where active learning becomes a game-changer. Active learning is a
The post How to Build Supervised AI Models When You Don’t Have Annotated Data appeared first on MarkTechPost.
One of the biggest challenges in real-world machine learning is that supervised models require labeled data—yet in many practical scenarios, the data you start with is almost always unlabeled. Manually annotating thousands of samples isn’t just slow; it’s expensive, tedious, and often impractical. This is where active learning becomes a game-changer. Active learning is a
The post How to Build Supervised AI Models When You Don’t Have Annotated Data appeared first on MarkTechPost. Read More
AWS and OpenAI announce multi-year strategic partnershipOpenAI News OpenAI and AWS have entered a multi-year, $38 billion partnership to scale advanced AI workloads. AWS will provide world-class infrastructure and compute capacity to power OpenAI’s next generation of models.
OpenAI and AWS have entered a multi-year, $38 billion partnership to scale advanced AI workloads. AWS will provide world-class infrastructure and compute capacity to power OpenAI’s next generation of models. Read More
The Complete Guide to Using Google AI StudioKDnuggets Google AI Studio offers an intuitive, web-based platform for prototyping and deploying AI solutions with the latest Gemini models. It streamlines the development process, allowing users to experiment with prompts, analyze outputs, and export production-ready code effortlessly.
Google AI Studio offers an intuitive, web-based platform for prototyping and deploying AI solutions with the latest Gemini models. It streamlines the development process, allowing users to experiment with prompts, analyze outputs, and export production-ready code effortlessly. Read More
AI browsers are a significant security threatAI News Among the explosion of AI systems, AI web browsers such as Fellou and Comet from Perplexity have begun to make appearances on the corporate desktop. Such applications are described as the next evolution of the humble browser, and come with AI features built in; they can read and summarise web pages – and, at their
The post AI browsers are a significant security threat appeared first on AI News.
Among the explosion of AI systems, AI web browsers such as Fellou and Comet from Perplexity have begun to make appearances on the corporate desktop. Such applications are described as the next evolution of the humble browser, and come with AI features built in; they can read and summarise web pages – and, at their
The post AI browsers are a significant security threat appeared first on AI News. Read More
OpenAI spreads $600B cloud AI bet across AWS, Oracle, MicrosoftAI News OpenAI is on a spending spree to secure its AI compute supply chain, signing a new deal with AWS as part of its multi-cloud strategy. The company recently ended its exclusive cloud-computing partnership with Microsoft. It has since allocated a reported $250 billion back to Microsoft, $300 billion to Oracle, and now, $38 billion to
The post OpenAI spreads $600B cloud AI bet across AWS, Oracle, Microsoft appeared first on AI News.
OpenAI is on a spending spree to secure its AI compute supply chain, signing a new deal with AWS as part of its multi-cloud strategy. The company recently ended its exclusive cloud-computing partnership with Microsoft. It has since allocated a reported $250 billion back to Microsoft, $300 billion to Oracle, and now, $38 billion to
The post OpenAI spreads $600B cloud AI bet across AWS, Oracle, Microsoft appeared first on AI News. Read More
What’s on My Bookmarks Bar: Data Science EditionKDnuggets Save time by keeping top resources and tools at your fingertips.
Save time by keeping top resources and tools at your fingertips. Read More
How Switchboard, MD automates real-time call transcription in clinical contact centers with Amazon Nova SonicArtificial Intelligence In this post, we examine the specific challenges Switchboard, MD faced with scaling transcription accuracy and cost-effectiveness in clinical environments, their evaluation process for selecting the right transcription solution, and the technical architecture they implemented using Amazon Connect and Amazon Kinesis Video Streams. This post details the impressive results achieved and demonstrates how they were able to use this foundation to automate EMR matching and give healthcare staff more time to focus on patient care.
In this post, we examine the specific challenges Switchboard, MD faced with scaling transcription accuracy and cost-effectiveness in clinical environments, their evaluation process for selecting the right transcription solution, and the technical architecture they implemented using Amazon Connect and Amazon Kinesis Video Streams. This post details the impressive results achieved and demonstrates how they were able to use this foundation to automate EMR matching and give healthcare staff more time to focus on patient care. Read More
Building a Multimodal RAG That Responds with Text, Images, and Tables from SourcesTowards Data Science Why do few chatbots return figures from source documents in their responses?
The post Building a Multimodal RAG That Responds with Text, Images, and Tables from Sources appeared first on Towards Data Science.
Why do few chatbots return figures from source documents in their responses?
The post Building a Multimodal RAG That Responds with Text, Images, and Tables from Sources appeared first on Towards Data Science. Read More
