Explore Franklin AI
Understand new AI research with readable explanations and links to the original papers.
"Should We Type or Talk to LLM Agents? A Comprehensive Study of Voice and Keyboard Input Perturbations" investigates how common human input errors—such as ty...
ReflectRL is a framework designed to improve the reasoning capabilities of large language models by repurposing failed reasoning attempts from expert models....
LatentGuard is a framework designed to make Large Language Model (LLM) safety moderation more efficient by replacing the generation of long, explicit text ra...
AgenticECO is an automated framework designed to perform post-route engineering change orders (ECO) on 3D integrated circuits.
Failure-Informed Image Self-Augmentation (FISA) is a framework designed to improve Multimodal Large Language Models (MLLMs) by generating synthetic training...
MAFIA is a framework designed to test the security of memory-augmented LLM agents.
TARL (Transaction-Aware Reliable Ledgers) is a memory management framework designed to improve how long-term AI agents update their stored knowledge.
GDPevo is a benchmark designed to evaluate how well AI agents can "self-evolve"—the process of updating their internal state (such as skills, memory, or prom...
Vision-Language Models (VLMs) can memorize sensitive or harmful information during pretraining.
SAT-Edge-Agent is a hardware-in-the-loop (HIL) system designed to manage onboard satellite intelligence by translating mission intent into actionable tool ca...
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