Unveiling Complex Collective Behaviors from Simple Rewards
Multi-agent Reinforcement Learning (MARL) holds great potential for robot swarms, but the black-box nature of neural policies complicates strategic analysis, limiting multi-robot applications. Furthermore, complex swarm behaviors can surprisingly emerge from simple rewards without explicit aggregation incentives. Unveiling the mechanisms behind this emergence is critical, but the disconnection between simple rewards and collective behaviors exacerbates interpretability challenges. This paper aim
Record details
Published: 14 July 2026
Source: arXiv cs.AI
Category: Research
Topics: Safety & alignment · Agents & autonomy
Retrieved: 15 July 2026
Related evidence
These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.
Generalizable VLA Finetuning via Representation Anchoring and Language-Action Alignment
HuggingFace Daily Papers · 14 July 2026
Isolation as a First-Class Principle for LLM-Agent System Safety: Concepts, Taxonomy, Challenges and Future Directions
arXiv · 14 July 2026
Operationalising Multi-Dimensional Evaluation for Conversational Agents: A Scalable, Governed Pipeline with Selective Re-evaluation and Model Benchmarking
arXiv · 13 July 2026
Designing Agent-Ready Websites for AI Web Agents: A Framework for Machine Readability, Actionability, and Decision Reliability
arXiv cs.HC · 13 July 2026
Forgetting Our Way to Shared Meaning: Effects of Forgetting on Conceptual Alignment in a Non-Partnership Coordination Game
arXiv cs.HC · 13 July 2026
Agent Hacks Agent: Autoresearch for Production-Agent Red-Teaming
arXiv cs.AI · 13 July 2026
How to cite this record
ethics.ai (14 July 2026), “Unveiling Complex Collective Behaviors from Simple Rewards,” evidence record 10455, https://ethics.ai/record/10455 (originally published by arXiv cs.AI).
Use and limitations
This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.