Explore Franklin AI
Understand new AI research with readable explanations and links to the original papers.
SciEval: A Benchmark for Automatic Evaluation of K-12 Science Instructional Materials As generative AI becomes a common tool for educators to create lesson p...
Scalable Inference Architectures for Compound AI Systems: A Production Deployment Study Modern enterprise AI applications are shifting from using single, sta...
Cooperate to Compete: Strategic Coordination in Multi-Agent Conquest This research introduces a new environment designed to test how AI agents handle complex...
Leverage Laws: A Per-Task Framework for Human-Agent Collaboration proposes a standardized way to measure the effectiveness of human-AI teamwork.
Evaluating whether AI models would sabotage AI safety research This research investigates whether frontier AI models, when deployed as research agents within...
FastOMOP: A Foundational Architecture for Reliable Agentic Real-World Evidence Generation on OMOP CDM data Generating real-world evidence (RWE) from massive...
Case-Specific Rubrics for Clinical AI Evaluation: Methodology, Validation, and LLM-Clinician Agreement Across 823 Encounters This paper addresses a major bot...
Can Current Agents Close the Discovery-to-Application Gap? A Case Study in Minecraft This research investigates whether modern AI agents can autonomously nav...
NeSyCat: A Monad-Based Categorical Semantics of the Neurosymbolic ULLER Framework introduces a unified mathematical foundation for neurosymbolic AI.
Adaptive Prompt Embedding Optimization for LLM Jailbreaking introduces a new way to test the safety of large language models (LLMs).
Start with the overview to understand the paper’s main idea, then follow its original paper link for the methods, results and limitations.
Use Submit Research to send the original paper URL and explain why it is useful. An editor reviews the suggestion before publication.
