Author: Divyesh Vitthal Jawkhede

Divyesh Vitthal Jawkhede
66 POSTS0 COMMENTS
Divyesh is a consulting intern at Marktechpost. He is pursuing a BTech in Agricultural and Food Engineering from the Indian Institute of Technology, Kharagpur. He is a Data Science and Machine learning enthusiast who wants to integrate these leading technologies into the agricultural domain and solve challenges.

Google DeepMind Research Introduces WebLI-100B: Scaling Vision-Language Pretraining to 100 Billion Examples for Cultural Diversity and Multilingualit

Machines learn to connect images and text by training on large datasets, where more data helps models recognize patterns and improve accuracy. Vision-language models...

Vintix: Scaling In-Context Reinforcement Learning for Generalist AI Agents

Developing AI systems that learn from their surroundings during execution involves creating models that adapt dynamically based on new information. In-Context Reinforcement Learning (ICRL)...

This AI Paper Introduces MaAS (Multi-agent Architecture Search): A New Machine Learning Framework that Optimizes Multi-Agent Systems

Large language models (LLMs) are the foundation for multi-agent systems, allowing multiple AI agents to collaborate, communicate, and solve problems. These agents use LLMs...

Unraveling Direct Alignment Algorithms: A Comparative Study on Optimization Strategies for LLM Alignment

Aligning large language models (LLMs) with human values remains difficult due to unclear goals, weak training signals, and the complexity of human intent. Direct...

π0 Released and Open Sourced: A General-Purpose Robotic Foundation Model that could be Fine-Tuned to a Diverse Range of Tasks

Robots are usually unsuitable for altering different tasks and environments. General-purpose models of robots are devised to circumvent this problem. They allow fine-tuning these...

From Softmax to SSMax: Enhancing Attention and Key Information Retrieval in Transformers

Transformer-based language models process text by analyzing word relationships rather than reading in order. They use attention mechanisms to focus on keywords, but handling...

This AI Paper from the Tsinghua University Propose T1 to Scale Reinforcement Learning by Encouraging Exploration and Understand Inference Scaling

Large language models (LLMs) are developed specifically for math, programming, and general autonomous agents and require improvement in reasoning at test time. Various approaches...

Meet RAGEN Framework: The First Open-Source Reproduction of DeepSeek-R1 for Training Agentic Models via Reinforcement Learning

Developing AI agents capable of independent decision-making, especially for multi-step tasks, is a significant challenge. DeepSeekAI, a leader in advancing large language models and...

Light3R-SfM: A Scalable and Efficient Feed-Forward Approach to Structure-from-Motion

Structure-from-motion (SfM) focuses on recovering camera positions and building 3D scenes from multiple images. This process is important for tasks like 3D reconstruction and...

Quantization Space Utilization Rate (QSUR): A Novel Post-Training Quantization Method Designed to Enhance the Efficiency of Large Language Models (LLMs)

Post-training quantization (PTQ) focuses on reducing the size and improving the speed of large language models (LLMs) to make them more practical for real-world...

ByteDance Introduces UI-TARS: A Native GUI Agent Model that Integrates Perception, Action, Reasoning, and Memory into a Scalable and Adaptive Framework

GUI agents seek to perform real tasks in digital environments by understanding and interacting with graphical interfaces such as buttons and text boxes. The...

Alibaba Researchers Propose VideoLLaMA 3: An Advanced Multimodal Foundation Model for Image and Video Understanding

Advancements in multimodal intelligence depend on processing and understanding images and videos. Images can reveal static scenes by providing information regarding details such as...