Explore Franklin AI
Understand new AI research with readable explanations and links to the original papers.
Large language models (LLMs) often struggle to maintain coherence over long conversations because they treat all personal information as equally important.
What Should an Agent Forget? Separating What Is Stored from What Is Used Persistent language agents often struggle to manage memory as they accumulate inform...
Beyond Surface Imitation: Contrastive Modeling for Reasoning Path Alignment in Multimodal In-Context Learning Multimodal Large Language Models (MLLMs) often...
MeClear: Cooperative Game-Theoretic Attribution and Risk-Aware Memory Clearance for Long-Horizon LLM Agents Long-horizon Large Language Model (LLM) agents re...
Procedural Graphs: Self-Evolving Execution Structures for LLM Agents Large language models (LLMs) are increasingly used as autonomous agents to plan and exec...
Everything in Moderation: Per-Domain Coverage Optima and Alignment-Resistant Domain Gaps in Multi-Domain Mid-Training investigates how the data composition d...
ExecCritic: Learn to Test, Test to Improve for Coding Agents Coding agents often struggle to fix bugs in large software repositories because they lack a reli...
Time-Varying Data as Sheaves: an Invitation to Narratives Modern science and engineering rely heavily on data that changes over time, yet the mathematical me...
Co-Evolving Harnesses and Models: On-Policy Correction Helps Weaker Models Catch Up Where Imitation Fails This research explores how to effectively combine t...
A Data-Driven Framework for Identifying and Prioritizing RPA Opportunities in Healthcare Processes Hospitals are increasingly using Robotic Process Automatio...
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.
