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Collective Monte Carlo Tree Search (CoMCTS): A New Learning-to-Reason Method for Multimodal Large Language Models
In today’s world, Multimodal large language models (MLLMs) are advanced systems that process and understand multiple input forms, such as text and images. By interpreting these diverse inputs, they aim to reason through tasks and generate accurate outputs. However, MLLMs often fail at complex tasks because they lack structured processes to break problems into smaller…
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Camel-AI Open Sourced OASIS: A Next Generation Simulator for Realistic Social Media Dynamics with One Million Agents
Social media platforms have revolutionized human interaction, creating dynamic environments where millions of users exchange information, form communities, and influence one another. These platforms, including X and Reddit, are not just tools for communication but have become critical ecosystems for understanding modern societal behaviors. Simulating such intricate interactions is vital for studying misinformation, group polarization,…
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Unveiling Privacy Risks in Machine Unlearning: Reconstruction Attacks on Deleted Data
Machine unlearning is driven by the need for data autonomy, allowing individuals to request the removal of their data’s influence on machine learning models. This field complements data privacy efforts, which focus on preventing models from revealing sensitive information about the training data through attacks like membership inference or reconstruction. While differential privacy methods limit…
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Quasar-1: A Rigorous Mathematical Framework for Temperature-Guided Reasoning in Language Models
Large language models (LLMs) encounter significant difficulties in performing efficient and logically consistent reasoning. Existing methods, such as CoT prompting, are extremely computationally intensive, not scalable, and unsuitable for real-time applications or limited resources. These limitations restrict their applicability in financial analysis and decision-making, which require speed and accuracy. State-of-the-art reasoning approaches, like CoT, build…
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YuLan-Mini: A 2.42B Parameter Open Data-efficient Language Model with Long-Context Capabilities and Advanced Training Techniques
Large language models (LLMs) built using transformer architectures heavily depend on pre-training with large-scale data to predict sequential tokens. This complex and resource-intensive process requires enormous computational infrastructure and well-constructed data pipelines. The growing demand for efficient and accessible LLMs has led researchers to explore techniques that balance resource use and performance, emphasizing achieving competitive…
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Critical warning from Microsoft: .NET install domains changing
Users who expected to be able to use the resources of the Azure content delivery network (CDN) from Edgio until November 2025, as previously announced, had a not-so-festive “gift” this week – the CDN is now shutting down on Jan. 15, 2025. In a post on Github, Rich Lander, principal program manager, .NET core at…
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Google DeepMind Introduces Differentiable Cache Augmentation: A Coprocessor-Enhanced Approach to Boost LLM Reasoning and Efficiency
Large language models (LLMs) are integral to solving complex problems across language processing, mathematics, and reasoning domains. Enhancements in computational techniques focus on enabling LLMs to process data more effectively, generating more accurate and contextually relevant responses. As these models become complex, researchers strive to develop methods to operate within fixed computational budgets without sacrificing…
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Meet SemiKong: The World’s First Open-Source Semiconductor-Focused LLM
The semiconductor industry enables advancements in consumer electronics, automotive systems, and cutting-edge computing technologies. The production of semiconductors involves sophisticated processes that demand unparalleled precision and expertise. These processes include chip design, manufacturing, testing, and optimization, each stage requiring deep domain knowledge. The field has traditionally depended on seasoned engineers whose experience has been built…
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AI data centers are becoming ‘mind-blowingly large’
Clusters of GPU chips in coming years will have to connect over distances longer than a mile, says the CEO of this fiber-optics firm.
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OpenAI’s o3 isn’t AGI yet but it just did something no other AI has done
The new AI model ‘is doing something completely different from the GPT series.’