Explore Franklin AI
Understand new AI research with readable explanations and links to the original papers.
SovereignPA-Bench: Evaluating User-Owned Personal Agents under Evolving Intent, Platform Mediation, and Consent Constraints introduces a new way to measure h...
Graph Sparse Sampling (GSS) is a new approach to online planning for autonomous systems operating in continuous environments.
OptiAgent is a multi-agent framework designed to bridge the gap between natural language descriptions of Operations Research problems and the precise, execut...
EvoAgentBench: Benchmarking Agent Self-Evolution via Ability Transfer AI agents are increasingly capable of solving complex, long-horizon tasks, but they oft...
ClassicLogic: A Knowledge-Driven Benchmark of Classic Puzzle Games for Evaluating Compositional Generalization Modern artificial intelligence excels at patte...
MoP-JEPA: Hard-Assigned Predictor Mixtures for Stochastic JEPA World Models JEPA (Joint-Embedding Predictive Architecture) world models are designed to help...
MetaSkill-Evolve: Recursive Self-Improvement of LLM Agents via Two-Timescale Meta-Skill Evolution Recent LLM agents often use "skills"—reusable procedural fi...
Reason, Reward, Refine: Step-Level Errors Corrections with Structured Feedback for Physics Reasoning in Small Language Models Physics reasoning is notoriousl...
LLM-as-a-Verifier is a new framework designed to improve how we evaluate the performance of AI agents.
A-TMA: Decoupling State-Aware Memory Failures in Long-Term Agent Memory Long-term memory allows AI agents to act as persistent assistants, but this creates a...
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.
