Research Papers

An analysis of the current state of AGI research, identifying key challenges in achieving human-level artificial intelligence and proposing potential research directions. We examine scaling laws, emergent capabilities, and the gap between narrow AI and general intelligence.

A comprehensive review of ethical frameworks for developing safe and beneficial artificial intelligence systems, with focus on alignment problems, value specification, and maintaining human oversight in increasingly autonomous systems.

Examining how transfer learning techniques can help bridge the gap between task-specific AI and more general artificial intelligence systems. We propose novel architectures that demonstrate improved knowledge transfer across domains.

An in-depth analysis of modern LLMs, their emergent capabilities, fundamental limitations, and what they tell us about the path to AGI. We examine GPT-4, Claude, and other frontier models through the lens of general intelligence criteria.

A detailed study of RLHF techniques and their effectiveness in aligning AI systems with human preferences. We propose improvements to reward modeling and discuss limitations of current approaches.

Exploring architectures that combine vision, language, and reasoning in unified models. We analyze how multimodal capabilities bring us closer to general intelligence and identify remaining challenges.