Sajjad Ansari is a final year undergraduate from IIT Kharagpur. As a Tech enthusiast, he delves into the practical applications of AI with a focus on understanding the impact of AI technologies and their real-world implications. He aims to articulate complex AI concepts in a clear and accessible manner.
Density Functional Theory (DFT) serves as the foundation of modern computational chemistry and materials science. However, its high computational cost severely limits its usage....
Reward models are fundamental components for aligning LLMs with human feedback, yet they face the challenge of reward hacking issues. These models focus on...
Introduction: Reinforcement Learning Progress through Chain-of-Thought Prompting
LLMs have shown excellent progress in complex reasoning tasks through CoT prompting combined with large-scale reinforcement learning (RL)....
Introduction to Generalization in Mathematical Reasoning
Large-scale language models with long CoT reasoning, such as DeepSeek-R1, have shown good results on Olympiad-level mathematics. However, models...
LLMs and the Need for Scientific Code Control
LLMs have rapidly evolved into complex natural language processors, enabling the development of agentic systems that...
Introduction: Personal LLM Agents and Privacy Risks
LLMs are deployed as personal assistants, gaining access to sensitive user data through Personal LLM agents. This deployment...
Understanding the Limitations of Current Omni-Modal Architectures
Large multimodal models (LMMs) have shown outstanding omni-capabilities across text, vision, and speech modalities, creating vast potential...
LLM-Based Code Generation Faces a Verification Gap
LLMs have shown strong performance in programming and are widely adopted in tools like Cursor and GitHub Copilot...
Introduction: Understanding Generalization in Deep Generative Models
Deep generative models, including diffusion and flow matching, have shown outstanding performance in synthesizing realistic multi-modal content across...
AI institutions develop heterogeneous models for specific tasks but face data scarcity challenges during training. Traditional Federated Learning (FL) supports only homogeneous model collaboration,...
The Challenge of Updating LLM Knowledge
LLMs have shown outstanding performance for various tasks through extensive pre-training on vast datasets. However, these models frequently generate...
Post-training methods for pre-trained language models (LMs) depend on human supervision through demonstrations or preference feedback to specify desired behaviors. However, this approach faces...
