AI

Modern AI systems have made significant progress, yet many are still struggling with complex reasoning tasks. Questions such as inconsistent problem solving, limited chain-to-thought capabilities and occasional factual inaccuracies are left. These challenges prevent practical uses in research and software development, where nuanced understanding and precision are crucial. Driven to overcome these limitations have led … Read more

Multimodal Large Language Models (MLLMS) have received considerable attention for their ability to handle complex tasks involving vision, language and audio integration. However, they lack the extensive adjustment in addition to fundamentally monitored fine tuning (SFT). Current advanced models often circumvent strict adaptation stages, leaving important aspects such as truth, security and human preference adaptation … Read more

Understanding of financial information means analysis of numbers, financial terms and organized data as tables for useful insight. It requires math calculations and knowledge of financial concepts, rules and relationships between economic terms. Although sophisticated AI Models have shown excellent general reasoning, their suitability for financial tasks is questionable. Such tasks require more than simple … Read more

Transforming language models into effective red holders is not without its challenges. Modern large language models have transformed the way we interact with technology, yet they are still struggling to prevent the generation of harmful content. Efforts such as rejection training help these models refuse risky requests, but even these protective measures can be bypassed … Read more

Adaptation of large language models to specialized domains remains challenging, especially in fields that require spatial reasoning and structured problem solving, although specialized in complex reasoning. Semiconductor Layout Design is a good example where AI tools have to interpret geometric restrictions and ensure accurate component location. Researchers develop advanced AI architectures to improve LLMS ‘ability … Read more

Quantization is a crucial technique for deep learning for reducing calculation costs and improving model efficiency. Large language models require significant treatment power, making quantization important to minimize memory consumption and improve the rate of inference. By converting high precision weights to lower bit formats, such as Int8, Int4 or InT2, quantization reduces storage requirements. … Read more

In recent years, the rapid scaling of large language models (LLMs) has led to extraordinary improvements in natural language understanding and reasoning. However, this progress comes with a significant warning: the inference process – generating response one token at a time – supports a calculation bottleneck. As LLMS grows in size and complexity, latency and … Read more

Artificial intelligence is increasingly integrated into different sectors, yet there is limited empirical evidence of its real world application across industries. Traditional research methods – such as predictable modeling and user surveys – struggle to capture AI’s evolving role in workplaces. This makes it difficult to assess its influence on productivity, labor markets and economic … Read more

Transformer-based models have significantly advanced Natural Language Processing (NLP), which is distinguished in different tasks. However, they are struggling with reasoning for long contexts, multi-stage inference and numeric reasoning. These challenges arise from their square complexity in self -perception, making them ineffective for expanded sequences and their lack of explicit memory, limiting their ability to … Read more

Mathematical reasoning is still one of the most complex challenges in AI. While AI is advanced in NLP and pattern recognition, its ability to solve complex mathematical problems with human -like logic and reasoning still hangs. Many AI models struggle with structured problem solving, symbolic reasoning and understand the deep relations between mathematical concepts. Tackling … Read more