Explore Franklin AI
Understand new AI research with readable explanations and links to the original papers.
SpatialWorld: Benchmarking Interactive Spatial Reasoning of Multimodal Agents in Real-World Tasks SpatialWorld is a new benchmark designed to evaluate how we...
Multi-Turn Evaluation of Deep Research Agents Under Process-Level Feedback This research investigates whether Deep Research Agents (DRAs)—AI systems designed...
Proxy Reward Internalization and Mechanistic Exploitation: A Learned Precursor to Reward Hacking and Its Generalization This research investigates the hidden...
AI evaluation results are currently reported in a fragmented way across blogs, leaderboards, and research papers, making it difficult for readers to compare...
SIGA: Self-Evolving Coding-Agent Adapters for Scientific Simulation Scientific simulators are essential for modern research, but they often require complex,...
SearchSwarm addresses a fundamental challenge in artificial intelligence: how to enable large language models (LLMs) to perform complex, long-horizon researc...
The Problem with Current Biomedical Encoders The paper "Correlation Is Not Enough: Embedding Human Metadata for Individual Causal Discovery" addresses a crit...
Collaborative Human-Agent Protocol (CHAP) addresses the growing challenge of managing work where humans and AI agents collaborate in operational roles.
Beyond Probabilistic Similarity: Structural, Temporal, and Causal Limitations of Retrieval-Augmented Generation in the Legal Domain argues that the frequent...
Off-Policy Evaluation with Strategic Agents via Local Disclosure addresses a fundamental challenge in data-driven decision-making: how to accurately evaluate...
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.
