Jul 9, 2026
InductWave: Inductive Multi-Hop Logical Query Answering on Knowledge Graphs
InductWave: Inductive Multi-Hop Logical Query Answering on Knowledge Graphs Knowledge Graphs (KGs) are essential for extracting information across diverse fi...
Explore Franklin AI
AI Research Papers
Understand new AI research with readable explanations and links to the original papers.
Jul 9, 2026
Do LLM-Generated Skills Make Better AI Data Scientists? A Component Ablation Across Data-Science Workflows
Do LLM-Generated Skills Make Better AI Data Scientists? A Component Ablation Across Data-Science Workflows This paper investigates whether "reusable skills"—...
Jul 9, 2026
SpaCellAgent: A Self-Evolving LLM-Based Multi-Agent Framework for Trajectory Analysis
SpaCellAgent: A Self-Evolving LLM-Based Multi-Agent Framework for Trajectory Analysis Trajectory inference (TI) is a vital process in computational biology u...
Jul 9, 2026
Institutional Red-Teaming: Deployment Rules, Not Just Models, Causally Shape Multi-Agent AI Safety
Institutional Red-Teaming: Deployment Rules, Not Just Models, Causally Shape Multi-Agent AI Safety This paper introduces "institutional red-teaming," a new m...
Jul 9, 2026
RL Post-Training Builds Compositional Reasoning Strategies
RL Post-Training Builds Compositional Reasoning Strategies This research investigates whether reinforcement learning (RL) post-training simply amplifies skil...
Jul 9, 2026
The Blind Curator: How a Biased Judge Silently Disables Skill Retirement in Self-Evolving Agents
The Blind Curator: How a Biased Judge Silently Disables Skill Retirement in Self-Evolving Agents Self-evolving AI agents improve by creating new skills based...
Jul 9, 2026
Recursive Self-Improvement in AI: From Bounded Self-Refinement to Autonomous Research Loops
Recursive Self-Improvement in AI: From Bounded Self-Refinement to Autonomous Research Loops explores the rapidly expanding field of AI systems that participa...
Jul 9, 2026
Search, Fail, Recover: A Training Framework for Correction-Aware Reasoning
Search, Fail, Recover: A Training Framework for Correction-Aware Reasoning introduces Pyligent, a framework designed to improve how AI models handle complex...
Jul 9, 2026
Reason Less, Verify More: Deterministic Gates Recover a Silent Policy-Violation Failure Mode in Tool-Using LLM Agents
Reason Less, Verify More: Deterministic Gates Recover a Silent Policy-Violation Failure Mode in Tool-Using LLM Agents This paper addresses a critical trust i...
Jul 8, 2026
RMISC: A Large-scale Real-world Multivariate Corpus for Time Series Foundation Models
RMISC: A Large-scale Real-world Multivariate Corpus for Time Series Foundation Models Time Series Foundation Models (TSFMs) have become a powerful tool for f...
Good to know
How do I use these AI research explainers?
Start with the overview to understand the paper’s main idea, then follow its original paper link for the methods, results and limitations.
Can I suggest a research paper?
Use Submit Research to send the original paper URL and explain why it is useful. An editor reviews the suggestion before publication.