Research Papers

Agentic Entropy-Balanced Policy Optimization (AEPO): Balancing Exploration and Stability in Reinforcement Learning for Web Agents

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Agentic Entropy-Balanced Policy Optimization (AEPO): Balancing Exploration and Stability in Reinforcement Learning for Web Agents

AEPO (Agentic Entropy-Balanced Policy Optimization) represents a major advancement in the evolution of Agentic Reinforcement Learning (RL). As large language models (LLMs) increasingly act as autonomous web agents – searching, reasoning and interacting with tools – the need for balanced exploration and stability has become crucial. Traditional RL methods often rely heavily on entropy to … Read more

NVIDIA, MIT, HKU and Tsinghua University Introduce QeRL: A Powerful Quantum Leap in Reinforcement Learning for LLMs

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NVIDIA, MIT, HKU and Tsinghua University Introduce QeRL: A Powerful Quantum Leap in Reinforcement Learning for LLMs

The rise of large language models (LLMs) has redefined artificial intelligence powering everything from conversational AI to autonomous reasoning systems. However, training these models especially through reinforcement learning (RL) is computationally expensive requiring massive GPU resources and long training cycles. To address this, a team of researchers from NVIDIA, Massachusetts Institute of Technology (MIT), The … Read more

MinerU2.5 by Shanghai AI Lab, Peking University & Shanghai Jiao Tong University Sets New Standard for AI-Powered Document Parsing

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MinerU2.5 by Shanghai AI Lab, Peking University & Shanghai Jiao Tong University Sets New Standard for AI-Powered Document Parsing

In the world of digital transformation, the ability to accurately extract and interpret information from complex documents is becoming increasingly essential. Whether for academic research, financial analysis or enterprise automation, document parsing – the process of converting structured and unstructured document data into machine-readable formats plays a vital role. Enter MinerU2.5, a groundbreaking vision-language model … Read more

Diffusion Transformers with Representation Autoencoders (RAE): The Next Leap in Generative AI

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Diffusion Transformers with Representation Autoencoders (RAE): The Next Leap in Generative AI

Diffusion Transformers (DiTs) have revolutionized image and video generation enabling stunningly realistic outputs in systems like Stable Diffusion and Imagen. However, despite innovations in transformer architectures and training methods, one crucial element of the diffusion pipeline has remained largely stagnant- the autoencoder that defines the latent space. Most current diffusion models still depend on Variational … Read more

LLaMAX2 by Nanjing University, HKU, CMU & Shanghai AI Lab: A Breakthrough in Translation-Enhanced Reasoning Models

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LLaMAX2 by Nanjing University, HKU, CMU & Shanghai AI Lab: A Breakthrough in Translation-Enhanced Reasoning Models

The world of large language models (LLMs) has evolved rapidly, producing advanced systems capable of reasoning, problem-solving, and creative text generation. However, a persistent challenge has been balancing translation quality with reasoning ability. Most translation-enhanced models excel in linguistic diversity but falter in logical reasoning or coding tasks. Addressing this crucial gap, the research paper … Read more

Granite-Speech-3.3-8B: IBM’s Next-Gen Speech-Language Model for Enterprise AI

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Granite-Speech-3.3-8B: IBM’s Next-Gen Speech-Language Model for Enterprise AI

In the fast-growing field of speech and language AI, IBM continues to make strides with its Granite model family , a suite of open enterprise-grade AI models that combine accuracy, safety and efficiency. The latest addition to this ecosystem, Granite-Speech-3.3-8B marks a significant milestone in automatic speech recognition (ASR) and speech translation (AST) technology. Released … Read more

Thinking with Camera 2.0: A Powerful Multimodal Model for Camera-Centric Understanding and Generation

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Thinking with Camera 2.0: A Powerful Multimodal Model for Camera-Centric Understanding and Generation

In the rapidly evolving field of multimodal AI, bridging gaps between vision, language and geometry is one of the frontier challenges. Traditional vision-language models excel at describing what is in an image “a cat on a sofa” “a red car on the road” but struggle to reason about how the image was captured: the camera’s … Read more