Research agents and tools represent a burgeoning field within artificial intelligence, where autonomous systems leverage large language models (LLMs) and modular architectures to facilitate scientific inquiry and innovation. These agents operate by integrating perception, reasoning, planning, and action capabilities, enabling them to perform tasks such as literature review, hypothesis generation,
Artificial intelligence (AI) agents are broadly defined as computational entities that perceive their environment and act autonomously to achieve specific goals (Russell & Norvig 2020). Foundational work in the field, dating back to its early formalisation, characterises intelligent agents by key properties such as autonomy, reactivity, proactiveness, and social ability,
In the European Union, the General Data Protection Regulation (GDPR) serves as a foundational instrument, mandating stringent controls on personal data processing within AI systems (Novelli et al. 2024). Complementary legislation, such as the EU AI Act, introduces risk-based classifications to ensure ethical deployment (European Data Protection Board 2024). In
The proliferation of various generative AI tools raises profound questions about authorship in scientific publications, particularly whether GenAI systems can be considered authors and how responsibility for their output should be shared. Traditional notions of authorship emphasise human agency, accountability, and intellectual contribution, as outlined in established guidelines from bodies
Example-driven prompts leverage demonstrations to guide large language models (LLMs) in producing precise and contextually relevant outputs, forming a critical component of prompt engineering. This category includes zero-shot prompting, one-shot prompting, and few-shot prompting, each offering varying degrees of guidance for research tasks. These techniques enable researchers to tailor LLM
The most fundamental component of any prompt is the instruction, which explicitly defines the task the model is expected to perform. A simple query might pose a question, but a well-crafted instruction provides a clear, actionable directive. This distinction is crucial; an instruction such as, “Summarise the following academic article
Generative Artificial Intelligence (AI) has revolutionised digital media through its ability to synthesise highly realistic content, with deepfake technology standing as one of its most prominent and contentious applications. The term “deepfake,” derived from “deep learning” and “fake,” refers to synthetic media—typically videos or audio—that convincingly depict individuals
Detecting hallucinations involves distinguishing accurate outputs from those that deviate from factual or contextual grounding. One approach is consistency checking, where LLM outputs are evaluated against external knowledge bases to identify discrepancies. Manakul et al. (2023) propose SelfCheckGPT, a zero-resource method that uses the model’s internal consistency to detect
Contemporary researchers face unprecedented financial barriers when engaging with state-of-the-art language models, particularly through API-based services where costs are directly proportional to token consumption and computational resource utilisation. The challenge is compounded by increasing complexity of research tasks requiring extensive prompt engineering, iterative model interactions, and large-scale data processing operations.
A defining characteristic of LLMs is their scale, measured by the number of parameters, which has grown exponentially in recent years. Models such as GPT-3, with 175 billion parameters, and its successors have demonstrated remarkable capabilities, raising questions about the relationship between model size and performance (Brown et al. 2020)
The concept of generative artificial intelligence (GenAI) has long been employed in academic and technological discourse; however, it only began to garner widespread recognition in the mid-to-late 2010s, a period marked by pivotal breakthroughs in AI development that fundamentally transformed the landscape of its potential applications. Early in the decade,
The evolution of machine learning has led to the development of powerful general models, such as BERT, GPT-3, and Vision Transformers (ViT), which have transformed artificial intelligence applications across diverse domains. These models, pre-trained on extensive datasets like Common Crawl for natural language processing or ImageNet for computer vision, demonstrate
