Explore Franklin AI
Understand new AI research with readable explanations and links to the original papers.
Do LLMs Exhibit Coherent Knowledge Structures in Mathematical Reasoning? A Perspective from Knowledge Space Theory This research investigates whether Large L...
Testing Interchangeability in LLM Agent Teams This research investigates a fundamental assumption in the design of multi-agent systems: that any agent capabl...
Technical Manual for a Toolkit for Measuring Contextual Individuation in Transformer Language Models This paper introduces a specialized toolkit designed to...
GUT: Quantifying and Optimizing the Reasoning Uncertainty of LLMs via Graph Complexity Large Language Models (LLMs) often struggle with "reasoning uncertaint...
Quantum circuits are essential for running quantum algorithms, but they require Boolean functions to be implemented in a reversible way.
RISE: Recursive Improvement via Self-Extrapolating Policy Distillation RISE is a new training method designed to help language models improve themselves recu...
Beyond Aggregate Scores: Behavioral Correctness Assumptions for Assessing Reference-Based Automatic Evaluation Methods This paper introduces a new diagnostic...
Large Language Models for HVAC Operations in Building Energy Systems: A Critical Review of Methods, Applications, and Deployment Readiness This paper provide...
Necessary or Sufficient? Evaluating LLM Explanations With Behavioural Evidence This research investigates whether the explanations provided by Large Language...
Does Your Agent's Memory Survive a Model Upgrade? A Controlled Study of Memory Portability investigates the hidden risks of upgrading the AI models that powe...
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.
