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Test‑Time Self‑Training to Boost LLM Reasoning

Watch: START: Self-taught Reasoner with Tools (Mar 2025) by AI Paper Slop Test-time self-training addresses critical gaps in large language model (LLM) performance by dynamically refining reasoning during inference. Industry benchmarks show that even top-tier LLMs struggle with complex tasks,…
Thumbnail Image of Tutorial Test‑Time Self‑Training to Boost LLM Reasoning
Image of Author Dr. Dipen
Dr. Dipen
I am an AI/ML researcher with 150+ citations and 16 published research papers. I have three tier-1 publications, including Internet of Things (Elsevier), Biomedical Signal Processing and Control (Elsevier), and IEEE Access. In my research journey, I have collaborated with NASA Glenn Research Center, Cleveland Clinic, and the U.S. Department of Energy for various research projects. I am also an official reviewer and have reviewed over 100 research papers for Elsevier, IEEE Transactions, ICRA, MDPI, and other top journals and conferences. I hold a PhD from Cleveland State University with a focus on large language models (LLMs) in cybersecurity, and I also earned a master’s degree in informatics from Northeastern University.
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Token‑Size‑Aware Compression Reduces LLM Memory Footprint

As large language models (LLMs) grow in complexity, their memory demands have become a critical bottleneck. Modern models with hundreds of billions of parameters require extreme computational resources to store and process token data during inference. For example, a single long-context generation…
Thumbnail Image of Tutorial Token‑Size‑Aware Compression Reduces LLM Memory Footprint
Image of Author Dr. Dipen
Dr. Dipen
I am an AI/ML researcher with 150+ citations and 16 published research papers. I have three tier-1 publications, including Internet of Things (Elsevier), Biomedical Signal Processing and Control (Elsevier), and IEEE Access. In my research journey, I have collaborated with NASA Glenn Research Center, Cleveland Clinic, and the U.S. Department of Energy for various research projects. I am also an official reviewer and have reviewed over 100 research papers for Elsevier, IEEE Transactions, ICRA, MDPI, and other top journals and conferences. I hold a PhD from Cleveland State University with a focus on large language models (LLMs) in cybersecurity, and I also earned a master’s degree in informatics from Northeastern University.
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Using Latent Reasoning for Autonomous Driving

Latent reasoning, as detailed in the Fundamentals of Latent Reasoning for Autonomous Driving section, is transforming autonomous driving by enabling systems to process complex, real-time decisions with human-like adaptability. Traditional modular pipelines often struggle with unpredictable…
Thumbnail Image of Tutorial Using Latent Reasoning for Autonomous Driving
Image of Author Dr. Dipen
Dr. Dipen
I am an AI/ML researcher with 150+ citations and 16 published research papers. I have three tier-1 publications, including Internet of Things (Elsevier), Biomedical Signal Processing and Control (Elsevier), and IEEE Access. In my research journey, I have collaborated with NASA Glenn Research Center, Cleveland Clinic, and the U.S. Department of Energy for various research projects. I am also an official reviewer and have reviewed over 100 research papers for Elsevier, IEEE Transactions, ICRA, MDPI, and other top journals and conferences. I hold a PhD from Cleveland State University with a focus on large language models (LLMs) in cybersecurity, and I also earned a master’s degree in informatics from Northeastern University.
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Using Meme Theory to Evaluate Large Language Models

The rise of large language models (LLMs) has transformed industries, but evaluating their capabilities remains a complex challenge. Over 70% of organizations now use LLMs for tasks like customer support, content creation, and data analysis, yet traditional evaluation methods often fail to capture…
Thumbnail Image of Tutorial Using Meme Theory to Evaluate Large Language Models
Image of Author Dr. Dipen
Dr. Dipen
I am an AI/ML researcher with 150+ citations and 16 published research papers. I have three tier-1 publications, including Internet of Things (Elsevier), Biomedical Signal Processing and Control (Elsevier), and IEEE Access. In my research journey, I have collaborated with NASA Glenn Research Center, Cleveland Clinic, and the U.S. Department of Energy for various research projects. I am also an official reviewer and have reviewed over 100 research papers for Elsevier, IEEE Transactions, ICRA, MDPI, and other top journals and conferences. I hold a PhD from Cleveland State University with a focus on large language models (LLMs) in cybersecurity, and I also earned a master’s degree in informatics from Northeastern University.
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Why AI-Generated Code Becomes Hard to Maintain and How to Fix It

AI-generated code is reshaping software development, but its long-term value depends on how well teams maintain it. Industry data shows that 70-90% of software costs over a project’s lifespan go toward maintenance, modification, and bug fixes. With AI tools now generating vast portions of code,…
Thumbnail Image of Tutorial Why AI-Generated Code Becomes Hard to Maintain and How to Fix It
Image of Author Dr. Dipen
Dr. Dipen
I am an AI/ML researcher with 150+ citations and 16 published research papers. I have three tier-1 publications, including Internet of Things (Elsevier), Biomedical Signal Processing and Control (Elsevier), and IEEE Access. In my research journey, I have collaborated with NASA Glenn Research Center, Cleveland Clinic, and the U.S. Department of Energy for various research projects. I am also an official reviewer and have reviewed over 100 research papers for Elsevier, IEEE Transactions, ICRA, MDPI, and other top journals and conferences. I hold a PhD from Cleveland State University with a focus on large language models (LLMs) in cybersecurity, and I also earned a master’s degree in informatics from Northeastern University.
•Last Updated:
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