AI and language model research that explores the architecture of how language models are being integrated into business workflows and software products. We analyze the technical aspects of current LLM architectures, and the gains found in automation. Our research is a technical framework for understanding how AI is altering the structure ofย businesses.
An LLM research paper, titled “Artificial or Just Artful? explores the tension between pretraining objectives and alignment constraints in Large Language Models (LLMs). The researchers specifically investigated how models adapt their strategies when exposed to test cases from the BigCodeBench (Hard) dataset.
Do LLMs Bend the Rules in Programming When They Have Access to Test Cases? Read Article ยป
RAGRecon, a system to improve Cyber Threat Intelligence through the integration of Large Language Models and Retrieval-Augmented Generation.
Is Your AI Target Defensible? How RAGRecon Solves the Trust Gap in Cybersecurity Read Article ยป
DRAFT-RL is a evaluation framework fort LLMs designed to address critical limitations in LLM-based reasoning systems by integrating Chain-of-Draft (CoD) reasoning with multi-agent reinforcement learning.
The Language Model Council research suggests that the top spot on any given leaderboard might be an artifact of evaluation design rather than a reflection of superior, generalized capability.
How Did 20 LLMs Dethroned GPT-4o and Reveal the Flaws in AI Leaderboards Read Article ยป
Humanity’s Last Exam is a multi-modal case study designed to measure the capabilities of large language models.
Is This Humanity’s Last Exam… For Language Models? Read Article ยป
A new research paper from Humains-Junior language model reportedly matches the factual accuracy of GPT-4o on a specific public subset. According to the paper the Humains-Junior language model achieves this performance through an innovative method called “Exoskeleton Reasoning.”
Humains-Junior Language Model Challenges GPT-4o on Factual Accuracy Read Article ยป
