Scaling Observation-aware Planning in Uncertain Domains
Scaling Observation-aware Planning in Uncertain Domains When designing autonomous agents for uncertain environments, engineers face a difficult trade-off: th...
Explore Franklin AI
Understand new AI research with readable explanations and links to the original papers.
Scaling Observation-aware Planning in Uncertain Domains When designing autonomous agents for uncertain environments, engineers face a difficult trade-off: th...
Beyond Acoustic Emotion Recognition: Multimodal Pathos Analysis in Political Speech Using LLM-Based and Acoustic Emotion Models This research investigates ho...
Collaborative driving aims to improve road safety and efficiency by allowing connected vehicles to share information and coordinate their actions.
Gated DeltaNet-2 is a new recurrent attention layer designed to improve how large language models manage their internal memory.
The Challenge of Evaluating Agent Optimizers Automated agent creation relies on "harness optimization," where an optimizer agent iteratively updates the soft...
Is Capability a Liability? More Capable Language Models Make Worse Forecasts When It Matters Most This research investigates a counterintuitive phenomenon in...
Parametric Modular Answer Set Programs Made Declarative introduces a new formal framework designed to bring modularity and declarative clarity to Answer Set...
WorkstreamBench is a new evaluation framework designed to test how well AI agents can build complex, professional-grade financial spreadsheets from scratch.
HarnessAPI is a Python framework designed to solve the problem of "dual-maintenance" for developers building AI tools.
AiraXiv: An AI-Driven Open-Access Platform for Human and AI Scientists introduces a new publishing paradigm designed to handle the rapid growth of research i...
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.