Explore Franklin AI
Understand new AI research with readable explanations and links to the original papers.
Schema-Guided Hierarchical Information Extraction and Semantic Evaluation Using Generative AI presents a framework for extracting complex, nested data from u...
ECHO (Enhanced Care & Health Observer) is a locally-deployable conversational assistant designed to support long-term chronic care management.
Integrating Implicit and Explicit Relational Biases through Graph-Based Multiple Instance Learning: A Case Study in Skin Lesion Diagnosis explores a dual-lev...
EnvACE is a reinforcement learning method designed to train large language model (LLM) agents without relying on external environments or simulators.
PaDoc is a document parsing system designed to accelerate the conversion of page images into structured data—such as text, tables, and formulas—by enabling p...
Argus is a general-purpose agentic runtime designed to handle long-horizon research tasks that require persistent reasoning, evidence-backed pivoting, and se...
Short-term load forecasting (STLF) is a critical component of the electric power industry, essential for balancing energy supply and demand.
When Shared Rollouts Fail in Defensive Driving Evaluation: A NAVSIM Score Basis Audit investigates a structural validity risk in the NAVSIM v2.2 benchmarking...
Item Response Theory for AI Safety proposes using a statistical framework from psychometrics to improve how language models are evaluated for safety.
From Score Matrices to Football-Aware Match-State Simulation: An Auditable LLM Harness for Exact-Score Reranking explores a hybrid architecture designed to i...
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.
