Vision-Powered RAG Agents for Organizational Software and Web Operations
Author(s): Varun Malhotra, Gaurav Adke, Ameya Divekar
LLM Based Agentic Framework to Assist with IT Incidents
Author(s): Chandan Kumar Agarwal, Aditi Raghuvanshi, Suresh S K, Sovan Gosh
Kimi K1.5 for Advancing LLMs with Scaling RL
Kimi K1.5 revolutionizes LLM scaling by leveraging RL for long-context reasoning, policy optimization, and multimodal integration.
Mastering Multimodal Understanding and Generation with Janus-Pro
Janus-Pro advances multimodal AI by decoupling visual understanding and generation, optimizing training strategies for superior performance.
Mastering Self-Adaptive LLMs with Transformer2
Transformer2 is a revolutionary framework enhancing LLMs with self-adaptive capabilities through Singular Value Fine-Tuning and reinforcement learning, enabling real-time task adaptation with low computational cost.
A Low Code Approach to Build Powerful AI Agents with Smolagents
Smolagents enable large language models (LLMs) to handle dynamic workflows with ease. Learn how its code-first, minimalistic design powers intelligent, flexible AI solutions for real-world tasks.
Mastering the Art of Mitigating AI Hallucinations
AI hallucinations challenge generative models’ reliability in critical applications. Learn about advanced mitigation techniques, including RLHF, RAG, and real-time fact-checking, to enhance accuracy and trustworthiness.
Mastering LLMs Reasoning Capability with DeepSeek-R1
DeepSeek-R1 harnesses reinforcement learning to achieve cutting-edge reasoning capabilities, outperforming traditional SFT approaches. Discover its architecture, training methods, and real-world applications in AI advancements.
Google’s Titans for Redefining Neural Memory with Persistent Learning at Test Time
Titans redefine neural memory by integrating short- and long-term components for efficient retention and retrieval. This article explores its architecture, innovations, and transformative potential across AI applications.
A Deep Dive into Cache Augmented Generation (CAG)
CAG eliminates retrieval latency and simplifies knowledge workflows by preloading and caching context. Learn how this innovative paradigm improves accuracy and efficiency in language generation tasks.