Explore Franklin AI
Understand new AI research with readable explanations and links to the original papers.
How Fast Should a Model Commit to Supervision? Training Reasoning Models on the Tsallis Loss Continuum explores why reasoning models often struggle to learn...
Three Models of RLHF Annotation: Extension, Evidence, and Authority Reinforcement Learning with Human Feedback (RLHF) is the primary method used to align lar...
At the Edge of the Heart: ULP FPGA-Based CNN for On-Device Cardiac Feature Extraction in Smart Health Sensors for Astronauts As space exploration missions to...
No Pedestrian Left Behind: Real-Time Detection and Tracking of Vulnerable Road Users for Adaptive Traffic Signal Control Current traffic signals often rely o...
PSI-Bench: Towards Clinically Grounded and Interpretable Evaluation of Depression Patient Simulators Patient simulators are becoming essential tools for trai...
This research investigates the mechanics of in-context learning (ICL) in transformer models, specifically focusing on how these models solve binary classific...
Conditional misalignment: common interventions can hide emergent misalignment behind contextual triggers This paper investigates a phenomenon called "conditi...
Security analysts are currently overwhelmed by the sheer volume of security alerts and the lack of context provided by detection systems.
MAIC-UI: Making Interactive Courseware with Generative UI MAIC-UI is a zero-code authoring system designed to help educators transform static teaching materi...
When Errors Can Be Beneficial: A Categorization of Imperfect Rewards for Policy Gradient Training language models with reinforcement learning often requires...
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.
