Writings
Function-Calling-in-AI-Agents
Function calling is a powerful feature in modern AI models that enables them to interact with external tools or APIs.
"Towards Better Generalization and Interpretability in Unsupervised Concept-Based Models"
(often linked with Concept Bottleneck Models and interpretability in deep learning)
Evaluating AI Agents & Agentic AI : Framework
(My thoughts as a Visionary for Evaluating AI Agents & Agentic AI)
"Better Pseudo-labeling with Multi-ASR Fusion and Error Correction by SpeechLLM"
Technical Use Case for : Better Pseudo-labeling with Multi-ASR Fusion and Error Correction by SpeechLLM
Winner-Takes-All Strategies for Multivariate Probabilistic Time Series Forecasting
A Visionary Perspective & Advanced Architectures and Battle-Tested Techniques
LRMs vs LLMs: AI Reasoning Efficiency
Breaking down complexity problems. LRMs use explicit reasoning steps—chain-of-thought, tree-of-thought
Custom Annotation Interfaces and how to use for AI agent
what is the custom annotation interfaces and how to use for AI agent ?
Building Foundation Models to Predict and Capture Human Cognition: A Roadmap for Advancing Agentic AI
Interact Your AI models Like Human Behaviors
Benefits and Applications of Dense+MoE LLMs for AI Agents
Merge dense and mixture of experts (MoE) architectures offer significant advantages for AI agents
A Philosophy & Efficiency Between GPT-oss vs. DeepSeek- vs. Llama 4
Comparative Between GPT-oss vs. DeepSeek- vs. Llama 4
The Key Components That Create Intelligence and Enable Good Decisions
Think of an AI agent as a person. The base knowledge (the LLM) is their education and raw intelligence. But to be effective in the real world, they need more
Supercharging Small AI With REFRAG
A Small Article: Supercharging Small AI with REFRAG