Explore Franklin AI
Understand new AI research with readable explanations and links to the original papers.
A Collective Variational Principle Unifying Bayesian Inference, Game Theory, and Thermodynamics This paper introduces the "Game-Theoretic Free Energy Princip...
Scientific spectra are dense, complex images that present a significant hurdle for current Multimodal Large Language Models (MLLMs).
This paper introduces a new framework called LAPITHS (Language model Analysis through Paradigm grounded Interpretations of Theses about Human likenesS) to cr...
Collaborative Agent Reasoning Engineering (CARE): A Three-Party Design Methodology for Systematically Engineering AI Agents with Subject Matter Experts, Deve...
From Unstructured Recall to Schema-Grounded Memory: Reliable AI Memory via Iterative, Schema-Aware Extraction This paper addresses a fundamental limitation i...
LLM as Clinical Graph Structure Refiner: Enhancing Representation Learning in EEG Seizure Diagnosis Electroencephalogram (EEG) signals are essential for diag...
Intern-Atlas: A Methodological Evolution Graph as Research Infrastructure for AI Scientists Current research tools treat scientific papers as isolated docume...
LLMs as ASP Programmers: Self-Correction Enables Task-Agnostic Nonmonotonic Reasoning Recent large language models (LLMs) are powerful, but they often strugg...
What Makes a Good Terminal-Agent Benchmark Task: A Guideline for Adversarial, Difficult, and Legible Evaluation Design This paper provides a set of design pr...
In-Context Prompting Obsoletes Agent Orchestration for Procedural Tasks This paper investigates whether the complex frameworks currently used to manage AI ag...
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.
