·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Music streamer Deezer says more than 50% of daily uploads are AI-generated43m◆Halliday’s latest smart glasses feature a much-improved display1h◆America needs to stop getting shocked by Chinese AI3h◆Advancing next-gen AI with materials science innovation3h◆Gritt exits stealth with $32 million for robots to build solar plants — then, everything else4h◆Capacity and Redundancy Trade-offs in Multi-Task Learning10h◆Predictive Training with Latent Imagination for Visual Quadruped Navigation10h◆Where Not to Learn: Prior-Aligned Training with Subset-based Attribution Constraints for Reliable Decision-Making10h◆Did We Actually Fix It? An Independent Adversarial Stress-Test of Post-Point-Adjustment Evaluation Metrics for Time-Series Anomaly Detection10h◆Supervised Reward Inference10h◆PPO-HSC: An Exploratory Reinforcement Learning Framework Based on Wide-Area Policy Coverage Optimization10h◆Is Progressive Disclosure All You Need for Long-Context Agents?10h◆It Depends on the Dataset: When a Brain-Encoding Model's Predicted Responses Beat Their Visual Backbone for Video Memorability10h◆DMFNet: Dual-Backbone Multiscale Fusion Network for Urban Scene Classification10h◆Oracle Gap and Signal Fidelity: A Fixed-Pool Diagnostic for Test-Time Collaboration10h◆Scientific reasoning does not reliably translate into scientific forecasting in frontier AI10h◆Language Triggers Hijack Language Circuits: A Mechanistic Analysis of Backdoor Behaviors in Large Language Models10h◆AI-Augmented Human Resource Management? Insights from German companies10h◆When to Use Extra Context: Evidence-Grounded Terminology Adaptation for Simultaneous Speech Translation10h◆PoLoRA: A Preconditioned Orthogonalized LoRA Optimizer10h◆Music streamer Deezer says more than 50% of daily uploads are AI-generated43m◆Halliday’s latest smart glasses feature a much-improved display1h◆America needs to stop getting shocked by Chinese AI3h◆Advancing next-gen AI with materials science innovation3h◆Gritt exits stealth with $32 million for robots to build solar plants — then, everything else4h◆Capacity and Redundancy Trade-offs in Multi-Task Learning10h◆Predictive Training with Latent Imagination for Visual Quadruped Navigation10h◆Where Not to Learn: Prior-Aligned Training with Subset-based Attribution Constraints for Reliable Decision-Making10h◆Did We Actually Fix It? An Independent Adversarial Stress-Test of Post-Point-Adjustment Evaluation Metrics for Time-Series Anomaly Detection10h◆Supervised Reward Inference10h◆PPO-HSC: An Exploratory Reinforcement Learning Framework Based on Wide-Area Policy Coverage Optimization10h◆Is Progressive Disclosure All You Need for Long-Context Agents?10h◆It Depends on the Dataset: When a Brain-Encoding Model's Predicted Responses Beat Their Visual Backbone for Video Memorability10h◆DMFNet: Dual-Backbone Multiscale Fusion Network for Urban Scene Classification10h◆Oracle Gap and Signal Fidelity: A Fixed-Pool Diagnostic for Test-Time Collaboration10h◆Scientific reasoning does not reliably translate into scientific forecasting in frontier AI10h◆Language Triggers Hijack Language Circuits: A Mechanistic Analysis of Backdoor Behaviors in Large Language Models10h◆AI-Augmented Human Resource Management? Insights from German companies10h◆When to Use Extra Context: Evidence-Grounded Terminology Adaptation for Simultaneous Speech Translation10h◆PoLoRA: A Preconditioned Orthogonalized LoRA Optimizer10h◆
News/Explaining Reinforcement Learning Agents via Inductive Logic Programming
arxiv
PublishedJuly 16, 2026 at 4:00 AM
—neutral

Explaining Reinforcement Learning Agents via Inductive Logic Programming

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2607.13655v1 Announce Type: new Abstract: Explainable Reinforcement Learning (XRL) seeks to make Reinforcement Learning (RL) policies more transparent and interpretable, a key requirement in safety-critical and human-centric scenarios. However, it is mostly based on user studies, thus targetin

Stay posted· Newsletter

A 5-min weekly brief — top movers, price watch, story of the week.

// no spam · unsubscribe one-click · free forever

Discussion
Source
↗
arxiv
Read original ↗All from arxiv →

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →

Related coverage

More from ARXIV
arxivCapacity and Redundancy Trade-offs in Multi-Task Learning10harxivPredictive Training with Latent Imagination for Visual Quadruped Navigation10harxivWhere Not to Learn: Prior-Aligned Training with Subset-based Attribution Constraints for Reliable Decision-Making10harxivDid We Actually Fix It? An Independent Adversarial Stress-Test of Post-Point-Adjustment Evaluation Metrics for Time-Series Anomaly Detection10h
The Bubble Brief
WEEKLY

Read AI insights every Tuesday — top movers, new releases, story of the week.

// no spam · unsubscribe one-click · free forever

Originally published on arxiv ↗
HomeModelsNews