LLM temporal and causal reasoning research (github.com)

Krellix has launched a curated and annotated research repository that addresses the cognitive limitations of large language models (LLMs), focusing specifically on gaps in temporal and causal reasoning. This collection serves as a centralized resource for developers, researchers, and product teams engaged with LLMs, highlighting the significant challenges these models face regarding understanding sequences of events and cause-and-effect relationships. By collating essential literature from various sources, the repository aims to facilitate informed decision-making about the potential and limitations of LLMs, directly benefiting those building and deploying these technologies. The significance of this initiative lies in the organized approach it provides to an area of active research, helping stakeholders grasp the complexities involved in LLM reasoning. Each topic area includes foundational papers, recent research, benchmarks, and practical implications, ensuring users can quickly find relevant information. This curated resource not only fosters collaboration within the AI community but also promotes a deeper understanding of fundamental reasoning gaps critical for future LLM advancements. With plans for ongoing expansion into additional reasoning topics, Krellix invites community contributions to refine and enhance the repository, making it a valuable tool for navigating the evolving landscape of AI research.