Explore Franklin AI
Understand new AI research with readable explanations and links to the original papers.
LEMUR (Learning to Align with Multi-Objective Reinforcement Learning from Preference Feedback) is a framework designed to train autonomous agents to balance...
ModelEquivBench is a certifying evaluation system designed to assess the correctness of optimization models generated by large language models (LLMs).
Beyond Retrieval: Analytic Memory for Multimodal Agents introduces a framework called AdaMM that allows AI agents to perform structured calculations on long-...
The paper "Development of FDD-ON: an Ontology for VAV HVAC System Fault Detection and Diagnostics" introduces a structured framework designed to standardize...
Self-Evolving Skill-Augmented Agent (SESA) is a framework designed to improve how AI agents learn from their own experience.
AgentHPOBench is a new benchmark designed to evaluate how well LLM-based autonomous agents can perform sequential hyperparameter optimization (HPO) in machin...
AMTFV (Agentic Mathematical Tool-Flow Verification) is a framework designed to improve the reliability of mathematical problem-solving in large language mode...
WIDE is a framework designed to improve the efficiency of Large Language Models (LLMs) by enabling token-level dynamic width pruning.
Towards Autonomous Aircraft Surveillance from Nanosatellites through On-Board Inference and Generative Data Augmentation proposes a method to enable nanosate...
PathView-Bench is a new benchmark designed to evaluate whether Multimodal Large Language Models (MLLMs) truly understand the fine-grained, multiscale visual...
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.
